However, leveraging an AI agent for more complex tasks than research and summarization—and implementing them in industrial environments like manufacturing—presents certain challenges.

For example, as new technology is introduced into already established companies, the visibility of brownfield deployments increases. This installation and configuration of new hardware or software must coexist with legacy IT systems. And, while it is easy to run an AI agent in a sandboxed environment, it is harder to integrate agents with machines and Operational Technology (OT) systems speaking industrial protocols like Modbus, PROFINET, and BACnet due to existing legacy infrastructure and an accumulation of tech debt.

To ensure governance and security in industrial environments, data security policies, regulatory compliance, and governance models are essential. Agent profiles with defined goals, rules, responsibilities, and constraints must be established before agents are deployed. Additionally, addressing real-world constraints—like LLM latency—and strategically selecting use cases and database providers can enhance Al agent effectiveness and optimize response times.

What’s more, the successful implementation of AI agents in industrial environments requires a number of foundational elements, including:

• Flexible data storage and scalability: An agent requires different types of data to function, such as agent profile, short-term memory, and long-term memory. Industrial AI agents require even more types of data, such as time series data from sensors and PLCs. They require efficient and scalable data storage that adapts to the dynamic needs of the environment.

• Continuous monitoring and analysis: An agent deployed in a manufacturing environment requires real-time observability of ever-changing data generated by the factory. It also needs to keep humans in the loop for any critical decisions that might affect production.

• High availability: Industrial environments demand near-zero downtime, making system resilience and failover capabilities essential.

XMPro joins forces with MongoDB

To address these challenges, we are pleased to announce XMPro’s partnership with MongoDB. XMPro offers APEX AI, a low-code control room for creating and managing advanced AI agents for industrial applications.

To ensure seamless control over these autonomous agents, XMPro APEX serves as the command center for configuring, monitoring, and orchestrating agent activities, empowering operators to remain in control.

APEX AI, combined with MongoDB Atlas and MongoDB Atlas Vector Search, addresses a variety of challenges faced by developers when building AI agents for industrial environments.

XMPro complements this by seamlessly integrating with industrial equipment such as SCADA systems, PLCs, IoT sensors, and ERPs, enabling continuous monitoring of operations. This integration ensures real-time data acquisition, contextualization, and advanced analytics, transforming raw data into actionable insights. XMPro’s capabilities include condition monitoring, predictive maintenance, anomaly detection, and process optimization, which help reduce downtime and improve operational efficiency while maintaining compliance and safety standards.

XMPro’s industrial AI agents rely on memory persistence for contextual decision-making. MongoDB Atlas acts as the database for storing and retrieving agent memories. Using a flexible document database for storing agentic memories enables agents to store different types of data, such as conversational logs, state transitions, and telemetry data, without requiring schema re-design.

The capabilities of MongoDB Atlas Vector Search empower APEX AI agents with a retrieval-augmented generation (RAG) tool, which helps to reduce LLM hallucinations. This integration allows agents to access and retrieve verified data, grounding their responses. Having database and vector search tools together in MongoDB Atlas also helps reduce agent latency and speeds up development.

APEX AI-enabled multi-agent systems working together in an industrial setting. These context-aware agents can work in tandem, retrieving relevant knowledge stored in MongoDB Atlas to enable meaningful collaboration and better decision-making.

XMPro APEX AI also leverages MongoDB Atlas’s robust security and high availability to ensure that agents can securely access and leverage data in real time; features such as role-based access controls, network isolation, encryption in transit and at-rest are key to why this agent-based AI solution is ideal for securing industrial production environments. MongoDB’s highly available and horizontal scalability ensures seamless data access at scale as organizations scale up their APEX AI deployments.

Unlocking the future of AI in industrial automation

XMPro APEX AI and MongoDB Atlas are a winning combination that paves the way for a new era of industrial automation. By tackling the core challenges of AI agents' deployment in industrial environments, we’re enabling organizations to deploy robust, intelligent, and autonomous industrial AI agents at scale.

MongoDB has been at the forefront of this revolution. Specifically, MongoDB helps financial institutions worldwide take advantage of OpenAPI. This open-source technology enables an organization’s applications, software, and digital platforms to connect and exchange data with third-party services efficiently and securely.

An example is VPBank. One of Vietnam’s largest private banks, it serves over 30 million customers. In 2020, VPBank was the first Vietnamese bank to adopt MongoDB Atlas for OpenAPI.

Working with MongoDB, VPBank moved to a microservices architecture, which supported the creation of its own OpenAPI platform and set a new standard for digital banking in Vietnam.

Speaking at MongoDB Day in Vietnam in November 2024, Anh K. Pham, Head of Database Services and Operations for VPBank, shared how MongoDB set up the bank for success with open banking.

Migrating from the relational model to the document model

Before working with MongoDB, VPBank operated in SQL.

The COVID pandemic and the rise of models such as open banking in the early 2020s mandated rapid digitization of banking operations and services. VPBank realized it needed to build the next generation of intelligent banking services to remain competitive. This was not feasible with traditional relational database management systems and the SQL model.

VPBank’s primary goal was to harness the power of data and to more efficiently manage unstructured data. This meant switching to an agile architecture based on microservices.

“When I was introduced to NoSQL, it made sense,” said Pham. “Data is not always structured. There’s a bunch of different data points here and there, and you can’t make anything of it. But it has to be stored somewhere, it has to be read, and it has to be fed into your applications.”

MongoDB Atlas was hosted on Amazon Web Services (AWS) as part of VPBank’s cloud transformation journey. The bank chose MongoDB Atlas for its ability to handle multiple workload types, which had been inadequately supported by its relational databases. These workloads include time series data, event data, real-time analytics, notifications, and big data (like transaction histories, catalog data, and JSON data).

Powering 220 microservices with flexibility, scalability, and performance

VPBank’s OpenAPI platform consists of over 220 microservices, and it processes more than 100 million transactions per month. By supporting these transactions, MongoDB is ultimately helping VPBank enhance customer experiences and streamline operations.

By using MongoDB Atlas, VPBank can better unlock the power of its data to quickly build data-driven applications and services on its microservices architecture. It experienced three substantial benefits by using MongoDB:

• Flexibility: MongoDB Atlas empowers VPBank to handle complex data, conduct rapid development and iterations, and facilitate efficient API development with BSON.

• Scalability: MongoDB enables dynamic scaling to handle increasing workloads. Additionally, horizontal scaling distributes data across multiple servers to handle high volumes, spikes in transactions, and API requests.

• Performance: MongoDB Atlas’s performance capabilities enable VPBank to manage large volumes of data in real time, regardless of acute throughput and latency demands.

Using OpenShift Container Platform (OCP), VPBank deployed a microservices architecture to run its Open Banking services.

“Choosing MongoDB as the modern database was the best choice since it can handle multiple types of data workloads with the performance we needed,” said Pham.

Looking to the future

VPBank plans to continue its cloud transformation journey. “We’re continuing to migrate our applications from on-premises into the cloud, and we’re continuing to modernize our applications as well,” said Pham. “That means that maybe those other databases that we used to have might be turning into MongoDB databases.”

VPBank is also looking at MongoDB to support its AI-driven future: “We really want to focus on AI and data analytics, pulling information from all our customers’ transactions,” explained Pham. “We want to ensure that what we build caters to our 30-plus million customers.”

April is Autism Awareness Month, and across industries, organizations are putting an increased focus on neurodiversity in the workplace. Luce and Ronan—members of our Config employee resource group—open up about their experiences as part of the neurodivergent community and how it shapes their work lives.

Read on to learn more about the importance of neurodiversity awareness, building an inclusive culture, and ways we can all be better colleagues and advocates.

Luce: Advocating for developers and neurodiversity

Luce, a Senior Developer Advocate on MongoDB’s Developer Relations team, and someone with ADHD and high-functioning autism, says she’s always had a strong connection to technology. With a deep knowledge of Microsoft and C#, Luce brings both technical expertise and a wealth of personal experience to her role. But her journey to becoming an advocate for neurodiversity was shaped by challenges she faced early in her career.

“I started out as a software developer, and in some ways, the logic of coding suited my neurodiversity,” said Luce. However, she struggled with breaking tasks into manageable steps and understanding the broader impact of a single change. This led to feelings of impostor syndrome, where she often saw herself as a “beginner” despite her technical skills.

What kept Luce moving forward, though, was her passion for learning and her desire to support others. She began sharing her knowledge through blog posts and tutorials, which eventually led to her becoming a Microsoft MVP—a recognition she’s received seven times—and finding a career in developer advocacy.

Luce’s advice to her colleagues? “Find out how your colleagues work best and leverage that. Corporate life can sometimes be too structured, and people should be allowed to work in ways that make them feel comfortable.”

For someone with ADHD like Luce, this often looks like periods of hyperfocus and intense productivity, followed by periods of distraction that appear to be an “inconsistent” working cadence. Part of MongoDB’s hybrid working approach means that employees are owners of their success and trusted to work in a way that’s best for them—here you have the flexibility to decide how you get your work done in a setting that’s most productive for you.

Ronan: A problem-solver at heart

Ronan, a Senior Escalation Engineer on MongoDB’s Technical Services team, has spent his career untangling complex technical issues under pressure. As part of the escalation team, his role involves solving high-stakes, challenging problems that require deep focus and problem-solving skills. But as Ronan reflects, his journey to understanding his neurodivergence wasn’t straightforward.

“For a long time, I didn’t realize what made me different,” said Ronan. “I loved working on complex technical issues, but at the same time, simple tasks would leave me stressed and anxious.” It wasn’t until his niece was diagnosed with autism that Ronan started to recognize similarities between her experience and his own. After pursuing a diagnosis in 2024, he finally understood what had been holding him back for so long.

Ronan has learned that his own autism means he’s a highly detailed thinker who thrives when faced with a problem to solve. However, hyperfocus—while a strength—can be overwhelming, especially when switching between tasks. Ronan admits that large Zoom calls or constant Slack pings can overstimulate him, so he prefers written communication where he can process information at his own pace.

One of Ronan’s biggest lessons learned is that neurodivergence is unique to each individual. “There’s no one-size-fits-all,” he said. “Some people may have sensory needs, communication preferences, or support needs that are very different from mine. It’s important to learn what neurodivergence means for the person in front of you.”

The power of Config: A place for connection

Both Luce and Ronan have found a supportive community in Config, MongoDB’s Employee Resource Group (ERG) for neurodiverse employees and allies. For Luce, being one of the leads of Config is incredibly fulfilling. She’s able to interact with members, understand how neurodiversity impacts them, and offer support in tangible ways. “I set up an always-open Zoom call where members can drop by for ‘body doubling,’ a technique that can help boost productivity,” Luce noted. “It’s so rewarding to see how simple actions can make a big difference for someone.”

Ronan also values the sense of connection that Config has given him. “It’s helped me find a place where I can share my experiences and learn from others who understand,” he says. “The leadership team is fantastic at organizing events, and I feel like I can finally talk about the challenges that I face as a neurodivergent employee without fear of being judged.”

Embracing neurodivergence: What we can all learn

Luce and Ronan both emphasize the importance of understanding that neurodivergence isn’t the same for everyone. As Ronan puts it, “If you’ve met one neurodivergent person, you’ve met one neurodivergent person.” Whether it’s about communication styles, sensory needs, or work preferences, the key takeaway is this: when we listen, adapt, and support one another, we create an environment where everyone can thrive.

Ronan’s advice to other neurodivergent employees? “There’s nothing wrong with you. You are not broken.” For Luce, the message is clear: “If you’re struggling, you don’t have to do it alone.” Both of them remind us that support is out there, whether it’s from friends, colleagues, employee resource groups, or the neurodiverse community at large.

At MongoDB, we’re committed to celebrating neurodiversity and creating a workplace where everyone’s unique strengths are recognized. By continuing to build a culture of empathy, understanding, and collaboration, we’re ensuring that everyone—neurodivergent or not—has the opportunity to shine.

The role of the modern database is evolving. AI-powered applications require more than just fast, scalable, and durable data management: they need highly accurate data retrieval and intelligent ranking, which are enabled by the ability to extract meaning from large volumes of unstructured inputs like text, images, and video. Retrieval-augmented generation (RAG) is now the default for LLM-powered applications, making accuracy in AI-driven search and retrieval a critical priority for developers. Meanwhile, customers in industries like healthcare, legal, and finance need highly reliable answers to power the applications their users rely on.

MongoDB Atlas Search already combines keyword and vector search through its hybrid capabilities. However, to truly meet developers’ needs and expectations, we are expanding our focus to integrating best-in-class embedding and reranking models into Atlas to ensure optimal performance and superior outcomes. These models enable search systems to understand meaning beyond exact words in text, and to recognize semantic similarities across images, video, and audio. Embedding models and rerankers empower customer support teams to quickly match queries with pertinent documents, assist legal professionals in surfacing key clauses within long contracts, and optimize RAG pipelines by retrieving contextually significant information that addresses users’ queries.

MongoDB is actively building this future. In February, we announced the acquisition of Voyage AI, a pioneer in state-of-the-art embedding and reranking models. With Voyage’s leading models and Atlas Search, developers will get a unified, production-ready stack for semantic retrieval.

Why embedding and reranking matter

Embedding and reranking models are core components of modern information retrieval, providing the link between natural language and accurate results:

• Embedding models transform data into vector representations that capture meaning and context, enabling searches based on semantic similarity rather than just keyword matches.

• Reranking models improve search accuracy by scoring and ranking a smaller set (e.g., 1000) of documents based on their relevance to a query, ensuring the most meaningful results appear first.

A typical system uses an embedding model to project documents into a vector space that encodes semantics. A nearest neighbor search provides a list of documents close to a given query. These results are processed with a reranking model that enables deeper, clause-by-clause comparison between the queries and the nearest neighbors.

This combination can greatly improve retrieval accuracy. For example, the system processing a user query for “holiday cookie recipes without tree nuts” may first retrieve a set of holiday recipes with the nearest neighbor search. In reranking, the query would be fully compared to each retrieved document to ensure each recipe does not contain any nuts.

Voyage AI’s embedding and reranking models

Voyage offers a suite of embedding models that support both general-purpose use cases and domain-specific needs. General models like voyage-3, voyage-3-large, and voyage-3-lite handle diverse text inputs. For specialized applications, Voyage provides models tailored to domains like code (voyage-code-3), legal (voyage-law-2), and finance (voyage-finance-2), offering higher accuracy by capturing the context and semantics unique to each field. They also offer a multimodal model (voyage-multimodal-3) capable of processing interleaved text and images. In addition, Voyage provides reranking models in standard and lite versions, each focused on optimizing relevance while keeping latency and computational load under control.

Voyage’s embedding models are designed to optimize the two distinct workloads required for each application, and our inference platform is purpose-built to support both scenarios efficiently:

• Document embeddings are created for all documents in a database whenever they are added or updated, capturing the semantic meaning of the documents an application has access to. Typically generated in batch, they are optimized for scale and throughput.

• Query embeddings enable the system to effectively interpret the user's intent for relevant results. Produced for a user's search query at the moment it's made, they are optimized for low latency and high precision.

Voyage AI’s embedding and reranking models consistently outperform leading production-grade models across industry benchmarks. For example, the general-purpose voyage-3-large model shows up to 20% improved retrieval accuracy over widely adopted production models across 100 datasets spanning domains like law, finance, and code. Despite its performance, it requires 200x less storage when using binary quantized embeddings. Domain-specific models like voyage-code-2 also outperform general-purpose models by up to 15% on code tasks

On the reranking side, rerank-lite-1 and rerank-1 deliver gains of up to 14% in precision and recall across over 80 multilingual and vertical-specific datasets. These improvements translate directly into better relevance, faster inference, and more efficient RAG pipelines at scale.

MongoDB Atlas Search + Voyage AI models today

MongoDB Atlas Vector Search enables powerful semantic retrieval with a wide range of embedding and reranking models. Developers can benefit from using Voyage models with Atlas Vector Search today, even before the deeper integration arrives.

“AI-powered search”, not “AI Search”

Not all AI search experiences are created equal. As we begin integrating Voyage AI models directly into MongoDB Atlas, it’s worth sharing how we’re approaching this work.

The best solutions today blend traditional information retrieval with modern AI techniques, improving relevance while keeping systems explainable and tunable.

AI-powered search in MongoDB Atlas enhances traditional search techniques with modern AI models. Embeddings improve semantic understanding, and reranking models refine relevance. But unlike opaque AI stacks, this approach remains transparent, customizable, and efficient:

• More control: Developers can tune search logic and ranking strategies based on their domain.

• More flexibility: Models can be updated or swapped to improve on an industry-specific corpus of data.

• More efficiency: MongoDB handles both storage and retrieval, optimizing cost and performance at scale.

With Voyage’s models integrated directly into Atlas workflows, developers gain powerful semantic capabilities without sacrificing clarity or maintainability.

Building the MongoDB + Voyage AI “better together” story

While MongoDB’s flexible query language unlocks powerful capabilities, Atlas Vector Search can require thoughtful setup, especially for advanced use cases. Users must select and fine-tune embedding models to fit specific use cases. Additionally, they must either rely on serverless model APIs or build and maintain infrastructure to host models themselves. Each insert of new data and search query requires independent API calls, adding operational overhead. As applications scale or when models need updating, managing these new data types in clusters introduces additional friction. Finally, integrating rerankers further complicates the workflow by requiring separate API calls and custom handling for reordering results.

By natively bringing Voyage AI's industry-leading models to MongoDB Atlas, we will eliminate these burdens and introduce new capabilities that empower customers to deliver highly relevant query results with simplicity.

MongoDB is actively integrating Voyage's embedding and reranking models into Atlas to deliver a truly native experience. These deep integrations will not only simplify the developer workflow but will also enhance accuracy, performance, and cost efficiency - all without the usual complexity of tuning disparate systems. And our ongoing commitment to partnering with innovative companies across AI and tech ensures that models from various providers remain supported within a collaborative ecosystem. However, adopting the native Voyage models allows developers to focus on building their applications while achieving the highest quality of information retrieval.

As we work on these native integrations, we're actively exploring advanced capabilities to further enhance the Atlas platform. Our investigations focus on:

• Defining the optimal approach to multi-modal information retrieval, integrating diverse inputs like text and images for richer results.

• Developing instruction-tuned retrieval, which allows concise prompts to precisely guide model interpretations, ensuring searches align closely with user intent. For example, enabling a search for “shoes” to prioritize sneakers or dress shoes, depending on user behavior and preferences.

• Determining the best ways to integrate domain-specific models tailored to the unique needs and use cases of industries such as legal, finance, and healthcare to achieve superior retrieval accuracy.

• Making it easy to update and change models without impacting availability.

• Bringing additional AI capabilities into our expressive aggregation pipeline language

• Improving the ability to automatically assess model performance, with the potential to offer this capability to customers.

Building the future of AI-powered search

From RAG pipelines to AI-powered customer experiences, information retrieval is the backbone of real-world AI applications. Voyage’s models strengthen this foundation by surfacing better documents and improving final LLM outputs.

We are building this future around four core principles, with accuracy at the forefront:

• Accurate: ensuring the precision of information retrieval is always our top priority, empowering applications to achieve production-grade quality and mass adoption.

• Seamless: built into existing developer workflows.

• Scalable: optimized for performance and cost.

• Composable: open, flexible, and deeply integrated.

By embedding Voyage into Atlas, MongoDB offers the best of both worlds: industry-leading retrieval models inside a fully managed, developer-friendly platform. This unified platform allows models and data to work together seamlessly, empowering developers to build scalable, high-performance AI applications with precision at their core.

At MongoDB, we talk a lot about what it means to be at an inflection point—a moment where you can either maintain the status quo or redefine what's possible. For my team and for software engineers around the world, that inflection point is here. Now.

Large language models (LLMs), agents, and now model context protocol (MCP) are fundamentally changing not just how we work, but what is possible with technology. So we must adapt; we have to build differently, and be faster. At MongoDB, we're creating something unique. Something smarter. We’re embracing and exploring everything that AI can do for developers.

And we’re inviting the next generation of engineers to join us in shaping it.

The hidden cost of legacy systems

If you've spent any time in enterprise engineering, you know the challenge many organizations face: decades-old systems that are critical to a company’s operations but are held together with duct tape and wishful thinking. The developers who built them have long since moved on. The documentation is missing, outdated, or was never written. The last update was six years ago; some of the dependencies are abandoned. Whether you call it “tech debt” or “care and feeding” or “maintenance”, it consumes a huge fraction of our time and engineering budget—without a clear path forward.

This is the reality many companies face—whether they’re in finance, healthcare, or the public sector—they have no choice but to pour millions of dollars into just keeping these systems afloat.

Or do they?

At MongoDB, we're building a new kind of engineering capability—one that combines the latest advancements in generative AI with the principles of forward-deployed engineering to help modernize legacy systems at a speed and scale that feels impossible.

What is forward-deployed AI engineering?

Because it’s new, you may not have heard of this role before. It's a bridge between engineering, consulting, and product development. As an AI Forward Deployed Engineer, you won’t just sit behind the scenes writing code. You'll be embedded with our customers, working side by side with their teams to solve real-world modernization challenges.

No theory, just hands-on engineering at the sharpest edge of AI innovation.

Your goal? Write software with AI, write software for AI, at speeds you’ve never experienced. In some tasks, we have benchmarked AI as being more than eight hundred times faster than a human being… working in that environment is, I assure you, radically different. You’ll deliver immediate, meaningful impact—whether that’s untangling a million-line code base, modernizing outdated Java frameworks, or helping teams migrate from niche, unsupported languages to modern tech stacks.

MongoDB + AI: Changing the game

One of the most exciting parts of our work is how AI is fundamentally changing what's possible. In the past, a modernization project like this might take five years and dozens of engineers. Today, with the power of MongoDB and AI, that same work is done in a fraction of the time—sometimes in months, sometimes in weeks—with a much smaller, highly focused team.

We use LLMs and other AI tools to do things like:

• Add missing documentation to legacy code

• Write unit tests where none existed

• Remove outdated frameworks, replacing them with others

• Analyze and map massive, messy code bases

• Move between programming languages, frameworks, platforms, ORMs, databases, and front-end technologies.

Let me be clear—none of this is easy, or on autopilot. We augment great engineers with powerful tools so they can focus on the work that matters most, so they can think big, and go far. Combining human expertise with AI capability leads to outcomes no one thought possible.

Why it matters

Modernizing these systems isn’t just about efficiency or cost savings—it’s mission-critical. The legacy platforms power trading systems, healthcare infrastructure, and governmental services that support real people every day. In multiple countries around the world.

Being able to show them a fully functional prototype in weeks, instead of years, is game-changing. It proves what's possible. It builds momentum. It allows them to rethink how they, too, can structure teams and processes in a world where technology moves faster than ever.

Why join MongoDB?

If you're an engineer who thrives on autonomy, problem-solving, and building real solutions with immediate impact—and you want to work with AI every day in novel and complex ways— this is your chance. We’re looking for folks from a variety of backgrounds—software development, consulting, product engineering, or technical architecture—who are passionate about learning and applying new technologies, skilled communicators who are excited to partner with customers, and who are comfortable operating in ambiguity.

As part of our Application Modernization function, you will:

• Work directly with customers and stakeholders

• Rapidly build solutions that solve meaningful business challenges

• Operate at the intersection of engineering, product, and consulting

• Learn and apply cutting-edge AI, inventing both new methodologies and technologies

• Be part of a small, fast-moving, high-impact team

You might even write a white paper or two.

You’ll also be part of a culture that values leadership at every level. At MongoDB, we believe in making it matter—and this role is designed to make change, not just for our customers, but for the software industry as a whole.

If you’ve ever wanted to be part of a startup within an enterprise, this is your opportunity. We're building a new way of working — one where experimentation, agility, and ownership are at the core.

Help us build what’s next

At MongoDB, we’re not just modernizing applications—we’re modernizing how software is made.

If that excites you, if you want to shape the future of software development alongside a team of builders, thinkers, and innovators—we’d love to hear from you.

In this guest post, David explores integrating MongoDB with Drupal to enhance its performance and scalability, helping Drupal remain competitive in the digital landscape.

-Rishabh Bisht, Product Manager, Developer Experience

Who am I?

Hello! My name is David Bekker (a.k.a. daffie), and I’m a seasoned Drupal core contributor with over 600 commit credits. I maintain Drupal’s Database API and database driver modules. My passion lies in open source development, driven by a desire to create maximum business value.

When I was looking for a new high-impact project to work on, I chose to develop a MongoDB driver for Drupal—one that stores entity instances as JSON objects. This project addresses Drupal’s evolving needs in a meaningful way.

User-centric innovation: Drupal’s next evolution

Drupal is rapidly evolving, making it particularly suitable for community and client portal solutions. This progression introduces new technical requirements, especially for authenticated, session-based scenarios like intranets and dashboards, which benefit from more adaptable storage solutions. While Drupal's abstract database layer remains tied to the relational models, embracing NoSQL databases would better support its evolving needs for modern applications.

To understand why this shift is crucial, let's compare this transition to a challenge Drupal faced years ago: optimizing sites for mobile devices. Back then, significant changes were needed to enhance mobile usability. Now, we face a similar paradigm shift as the market evolves from sites for anonymous users to those centered on authenticated users. Drupal must adapt, and Drupal on MongoDB is the key to this transformation. MongoDB, with its flexible, JSON-based structure, complements Drupal's architecture well. A robust integration with MongoDB would enhance capabilities and better equip Drupal to meet the expanding demands of enterprises.

Beyond traditional use cases, Drupal on MongoDB is also ideal as a backend for iOS, Android, and JavaScript applications, providing personalized and scalable solutions.

Redefining data storage and retrieval

Drupal on MongoDB is more than just a new database option. It enhances Drupal’s ability to compete in a changing digital landscape.

Drupal’s robust entity system provides a solid foundation where everything is structured as an entity. Traditionally, Drupal leverages relational databases like MySQL or MariaDB, efficiently managing data across multiple tables. This approach performs well for sites with a large number of anonymous users. However, for sites with many authenticated users, the complexity of retrieving entity data from multiple tables can introduce performance challenges. Optimizing data retrieval can significantly enhance the user experience, making Drupal even more powerful for dynamic, user-centric applications.

With MongoDB, every Drupal entity instance is stored as a single JSON object, including all revisions, translations, and field data. This streamlined data structure allows for significantly faster retrieval, making Drupal a stronger solution for personalized, user-focused experiences.

As the market shifts toward authentication-driven sites, supporting MongoDB ensures that Drupal remains a competitive and scalable option. Rather than replacing Drupal’s strengths, this integration enhances them, allowing Drupal to meet modern performance demands while maintaining its flexibility and power.

Scalability: Why MongoDB makes sense for large Drupal projects

The scalability of NoSQL databases like MongoDB sets them apart from traditional relational databases such as MySQL or MariaDB. While relational databases typically rely on a single-server model, MongoDB supports horizontal scaling, enabling distributed setups with thousands of servers acting as a unified database. This architecture provides the performance needed for large-scale projects with millions of authenticated users.

As community-driven software, Drupal is built to support interactive, user-focused experiences, including forums, profiles, and content management. Traditionally, its relational model organizes data across multiple tables—similar to storing the chapters of a book separately in a library. This approach ensures data consistency and flexibility, making it highly effective for managing structured content.

However, as the demand for authentication-heavy sites grows, the way data is stored becomes a crucial factor in performance. MongoDB offers a more efficient alternative by storing entire entities as JSON objects—just like keeping an entire book intact rather than splitting it into separate chapters across different locations. This eliminates the need for complex table joins, significantly accelerating data retrieval and making MongoDB well suited for personalized dashboards and dynamic content feeds.

For small-scale sites, both relational and NoSQL approaches work. But when scalability, speed, and efficiency become priorities—particularly for sites with millions of authenticated users—MongoDB provides a natural and powerful solution for taking Drupal to the next level.

Example of a user entity stored in MongoDB

The sample document below is an example of how a user entity could look like in MongoDB, containing fields like _id, uid, uuid, and langcode. It includes an embedded user_translations array that holds user details such as name, email, timezone, status, and timestamps for various activities.

{
  _id: ObjectId('664afdd4a3a001e71e0b49c7'), 
  uid: 1,
  uuid: '841149cd-fe56-47c4-a112-6d23f561332f', 
  langcode: 'en', 
  user_translations: [ { 
      uid: 1, 
      uuid: '841149cd-fe56-47c4-a112-6d23f561332f', 
      langcode: 'en', 
      preferred_langcode: 'en', 
      name: 'root', 
      pass: '$2y$10$kjGuIsPOTDa2TseuWMFGS.veLzH/khl0SfsuZNAeRPRtABgfq5GSC', 
      mail: 'admin@example.com', 
      timezone: 'Europe/Amsterdam', 
      status: true, 
      created: ISODate('2024-05-20T07:37:54.000Z'), 
      changed: ISODate('2024-05-20T07:42:08.000Z'), 
      access: ISODate('2024-05-20T08:46:47.000Z'), 
      login: ISODate('2024-05-20T07:44:16.000Z'), 
      init: 'admin@example.com', 
      default_langcode: true, 
      user_translations__roles: [ { 
          bundle: 'user', 
          deleted: false, 
          langcode: 'en', 
          entity_id: 1, 
          revision_id: 1, 
          delta: 0, 
          roles_target_id: 'administrator'
      } ]
  } ], 
  login: ISODate('2024-05-20T07:44:16.000Z'), 
  access: ISODate('2024-05-20T08:46:47.000Z') 
}

Optimizing data storage for performance

Switching to MongoDB alone isn’t enough to make Drupal a top-tier solution for sites with a high number of authenticated users. Indeed, developers must rethink how data is stored.

In traditional Drupal setups optimized for anonymous users, caching mechanisms like Redis compensate for slow database queries. However, for authenticated users, where content is dynamic and personalized, this approach falls short. Drupal itself needs to be fast, not just its caching layer.

MongoDB enables developers to store data in the way the application uses it, reducing the need for complex queries that slow down performance. Instead of relying on costly operations like joins and subqueries, simple and efficient queries should be the norm. Tools like materialized views—precomputed query results stored as database tables—help achieve this, ensuring faster data retrieval while keeping the database structured for high performance.

Why MongoDB for Drupal?

While many databases support JSON storage, MongoDB is the only one that fully meets Drupal’s needs. Its capabilities extend beyond basic JSON support, making it the optimal choice for storing entity instances efficiently.

Additionally, MongoDB offers several key advantages that align with Drupal’s evolving requirements:

• Horizontal scaling: Easily distribute database load across multiple servers, making it scalable for large user bases.

• Integrated file storage: Store user-uploaded files directly in the database instead of on the web server, simplifying hosting.

• Built-in full-text search: Eliminates the need for separate search solutions like SOLR, reducing infrastructure complexity.

• AI capabilities: Supports AI vectors, allowing for features like advanced search and personalization tailored to a site’s content.

Current status

Drupal’s journey to embracing more flexible data storage solutions is advancing with promising developments:

• The MongoDB driver for Drupal is available as a contrib module for Drupal 11, with over 99% of core tests passing.

• Discussions are ongoing to merge MongoDB support into Drupal core, pending community contributions.

• Finalist / Tech Blog is already running entirely on MongoDB.

These steps mark a significant transition for Drupal, showcasing its evolution towards accommodating non-relational databases like MongoDB. It paves the way for broader applications and more robust infrastructure by leveraging MongoDB’s strengths in flexibility and scalability.

Conclusion

As the web moves toward more personalized, user-centric experiences, Drupal must evolve to remain competitive. MongoDB is a key enabler of this evolution, providing faster, more scalable solutions for authenticated user-heavy sites. By embracing MongoDB, Drupal developers can unlock new performance possibilities, simplify infrastructure, and build future-ready web applications.

Check out the tutorial on how to run Drupal on MongoDB Atlas and start experiencing the benefits of this powerful integration today!

Want to get involved? Join the conversation in the Drupal community via Slack in the #mongodb and #contribute channels. Let’s shape the future of Drupal together!

In 2025, the demand for audio content—particularly podcasts—surged, with 9 million new active listeners in the United States alone, prompting news organizations to seek efficient ways to deliver daily summaries to their audiences. However, automating news delivery has proven to be a challenging task, as media outlets must manage dynamic article data and convert this information into high-quality audio formats at scale.

To overcome these hurdles, media organizations can use MongoDB for data storage alongside generative AI for podcast creation, developing a scalable solution for automated news broadcasting. This approach unlocks new AI-driven business opportunities and can attract new customers while strengthening the loyalty of existing ones, contributing to increased revenue streams for media outlets.

The secret sauce: MongoDB + AI

In a news automation solution, MongoDB acts as the system’s backbone, storing news article information as flexible documents with fields like title, content, and publication date—all within a single collection. Alongside this, dynamic elements (such as the number of qualified reads) can be seamlessly integrated into the same document to track content popularity.

Moreover, derived insights—e.g., sentiment analysis and key entities—can be generated and enriched through a gen AI pipeline directly within the existing collection.

This adaptable data structure ensures that the system remains both efficient and scalable, regardless of content diversity or evolving features. As a result, media outlets have created a robust framework to query and extract the latest news and metadata from MongoDB. They can now integrate AI with advanced language models to transform this information into an audio podcast. With this foundation in place, let's examine why MongoDB is well-suited for implementing AI-driven applications.

Why MongoDB is the perfect fit

News data is inherently diverse, with each article containing a unique mix of attributes, including main content fields (e.g. id, title, body, date, imageURL), calculated meta data (e.g. read count), generated fields with the help of GenAI (e.g. keywords, sentiment) and embeddings for semantic/vector search. Some of these elements originate from publishers, while others emerge from user interactions or AI-driven analysis. MongoDB’s flexible document model accommodates all these attributes—whether predefined or dynamically generated, within a single, adaptable structure. This eliminates the rigidity of traditional databases and ensures that the system evolves seamlessly alongside the data it manages.

What’s more, speed is critical in news automation. By storing complete, self-contained documents, MongoDB enables rapid retrieval and processing without the need for complex joins. This efficiency allows articles to be enriched, analyzed, and transformed into audio content in near real-time.

And scalability is built in. Whether handling a small stream of updates or processing vast amounts of constantly changing data, MongoDB’s distributed architecture ensures high availability and seamless growth, making it ideal for large-scale media applications.

Last but hardly least, developers benefit from MongoDB’s agility. Without the constraints of fixed schemas, new data points—whether from evolving AI models, audience engagement metrics, or editorial enhancements—can be integrated effortlessly. This flexibility allows teams to experiment, iterate, and scale without friction, ensuring that the system remains future-proof as news consumption evolves.

Bringing news to life with generative AI

Selecting MongoDB for database storage is just the beginning; the real magic unfolds when text meets AI-powered speech synthesis. In our labs, we have experimented with Google’s NotebookLM model to refine news text, ensuring smooth narration with accurate intonation and pacing.

Putting all these pieces together, the diagram below illustrates the workflow for automating AI-based news summaries into audio conversions.

The process begins with a script that retrieves relevant news articles from MongoDB, using the Aggregation Framework and Vector Search to ensure semantic relevance. These selected articles are then passed through an AI-powered pipeline, where they are condensed into a structured podcast script featuring multiple voices. Once the script is refined, advanced text-to-speech models transform it into high-quality audio, which is stored as a .wav file. To optimize delivery, the generated podcast is cached, ensuring seamless playback for users on demand. The result? A polished, human-like narration, ready for listeners in MP3 format.

Thanks to this implementation, media outlets can finally let go of the robotic voices of past automations. Instead, they can now deliver a listening experience to their customers that's human, engaging, and professional.

The future of AI-powered news consumption

This system isn’t just a technological innovation; it’s a revolution in how we consume news. By combining MongoDB’s efficiency with AI’s creative capabilities, media organizations can deliver personalized, real-time news summaries without human intervention. It’s faster, smarter, and scalable—ushering in a new era of automated audio content.

Want to build the next-gen AI-powered media platform? Start with MongoDB and let your content speak for itself!

In this article, Kyle Lai describes his role as a Software Engineer 2 at MongoDB; why he’d rather not be like the characters on the TV show, Severance; and how his commute helps set boundaries between his professional and personal lives.

Q: What do you do at MongoDB?

Kyle: Hi! I’m an engineer on Atlas Growth 1, where we run experiments on Atlas and coordinate closely with marketing, product, design, and analytics to improve the user experience. Atlas Growth 1 is part of the broader Atlas Growth engineering teams, where we own the experimentation platform and experiment software development kit, allowing other teams to run experiments as well!

The engineers on Atlas Growth are very involved with the product side of our experiments. We help the analytics team collect metrics and decide if a given experiment was a win. Sometimes, seemingly obvious positive improvements can turn out to be detrimental to the user flow, so our experimentation process allows us to learn greatly about our users, whether the experiment wins or not.

Q: What does work-life balance look like for you?

Kyle: Work-life balance for me means that I won’t be worrying about responding to messages or needing to open my laptop after work hours. It also means that my teammates equally respect my work-life balance and do not expect me to work during non-work hours.

Q: How do you ensure you set boundaries between work and personal life?

Kyle: Generally, for me, it’s more difficult to set boundaries between work and personal life when I’m working from home, so I try to come into the office most days. My commute also provides me with time to wind down and signal that work is over for the day. In a way, the drive to and from the train station allows me to transition to getting into the mindset for work or to decompress at the end of the day.

Q: Has work-life balance always been a priority for you, or did you develop it later in your career?

Kyle: As someone who is early in my career, work-life balance is something that I’ve grown to appreciate and see as a priority in my life. It allows me to enjoy my personal life, and definitely contributes to a healthier me and a healthier team.

Q: What benefits has this balance given you in your career?

Kyle: Our team has a weekly Friday hangout meeting, where we have a different question posed to us each week. One of the questions was based on the TV show, Severance. Would we choose to be “severed” like the characters in the show? They undergo a procedure that separates their work and personal brains—their work brains have no awareness of their personal lives, and vice versa.

As someone who hasn’t seen the show, but has heard about it from the rest of my team, I wouldn’t do it. Balancing my work and personal lives allows me to enjoy each side more. I’m motivated for the end of the week so I can enjoy the weekend, and I’m also excited to come to work with a fresh headspace on Mondays, since I am not overworking during non-work hours.

Q: What advice would you give to someone seeking to find a better balance?

Kyle: I’ll sometimes have the urge to continue working past work hours, as I’ll feel like I’m about to finish whatever task I’m working on very soon or think I can get even more done if I don’t stop working. That backfires pretty quickly. You have to realize you can be easily fatigued and are not able to give your best work if you constantly keep working. A proper work-life balance will allow you to have a fresh start and a clear mind each day.

As for how to better separate work and personal life, I’d suggest changing notification settings on your phone for Slack, so that non-urgent work messages won’t tempt you to open your laptop. Another strategy would be to associate some event with a cutoff for checking work things, such as not reading messages once you’ve left the office or boarded the train. I’ve had teammates tell me they delete Slack from their phones when they’re on vacation, which is a good idea!

Thank you to Kyle Lai for sharing these insights! And thanks to all of you for reading.

For past articles in this series, check out our interviews with:

• Staff Engineer, Ariel Hou

• Senior AI Developer Advocate, Apoorva Joshi

• Developer Advocate, Anaiya Raisinghani

• Senior Partner Marketing Manager, Rafa Liou

• Staff Software Engineer, Everton Agner

Interested in learning more about or connecting more with MongoDB? Join our MongoDB Community to meet other community members, hear about inspiring topics, and receive the latest MongoDB news and events.

And let us know if you have any questions for our future guests when it comes to building a better work-life balance as developers. Tag us on social media: @/mongodb #LoveYourDevelopers #AwayFromTheKeyboard

Enter DataGenie, powered by MongoDB—BI reimagined for the modern enterprise. DataGenie autonomously tracks millions of metrics across the entire business datascape. It learns complex trends like seasonality, discovers correlations & causations, detects issues & opportunities, connects the dots across related items, and delivers 5 to 10 prioritized actionable insights as stories in natural language to non-data-savvy business users. This enables business leaders to make bold, data-backed decisions without the need for manual data analysis. With advanced natural language capabilities through Talk to Data, users can query their data conversationally, making analytics truly accessible.

The challenges: Why DataGenie needed a change

DataGenie processes large volumes of enterprise data on a daily basis for customers, tracking billions of time series metrics and performing anomaly detection autonomously to generate deep, connected insights for business users. The below diagram represents the functional layers of DataGenie.

Central to the capability of DataGenie is the metrics store, which stores, rolls up, and serves billions of metrics. At DataGenie, we were using an RDBMS (PostgreSQL) as the metrics store.

As we scaled to larger enterprise customers, DataGenie processed significantly higher volumes of data. The complex feature sets we were building also required enormous flexibility and low latency in how we store & retrieve our metrics.

DataGenie had multiple components that served different purposes, and all of these had to be scaled independently to meet our sub-second latency requirements.

• With PostgreSQL as the metrics store for quite some time and tried to squeeze it to the maximum extent possible at the cost of flexibility. Since we over-optimized the structure for performance, we lost the flexibility we required to build our next-gen features, which were extremely demanding

• We defaulted to PostgreSQL for storing the insights (i.e. stories), again optimized for storage and speed, hurting us on the flexibility part

• For the vector store, we had been using ChromaDB for storing all our vector embeddings. As the data volumes grew, the most challenging part was maintaining the data sync

• We had to use a different data store for knowledge store and yet another technology for caching

The major problems we had were as follows:

• Rigid schema that hindered flexibility for evolving data needs.

• High latency & processing cost due to extensive preprocessing to achieve the desired structure

• Slow development cycles that hampered rapid innovation

How MongoDB gave DataGenie a superpower

After extensive experimentation with time-series databases, document databases, and vector stores, we realized that MongoDB would be the perfect fit for us since it exactly solved all our requirements with a single database.

Metrics store

When we migrated to MongoDB, we achieved a remarkable reduction in query latency. Previously, complex queries on 120 million documents took around 3 seconds to execute. With MongoDB's efficient architecture, we brought this down to an impressive 350-500 milliseconds for 500M+ docs, representing an 85-90% improvement in query speed for a much larger scale.

Additionally, for storing metrics, we transitioned to a key-value pair schema in MongoDB. This change allowed us to reduce our data volume significantly—from 300 million documents to just 10 million documents—thanks to MongoDB's flexible schema and optimized storage. This optimization not only reduced our storage footprint for metrics but also enhanced query efficiency.

Insights store

By leveraging MongoDB for the insight service, we eliminated the need for extensive post-processing, which previously consumed substantial computational resources. This resulted in a significant cost advantage, reducing our Spark processing costs by 90% or more (from $80 to $8 per job).

Querying 10,000+ insights took a minute before. With MongoDB, the same task is now completed in under 6 seconds—a 10x improvement in performance. MongoDB’s flexible aggregation pipeline was instrumental in achieving these results. For example, we extensively use dynamic filter presets to control which insights are shown to which users, based on their role & authority. The MongoDB aggregation pipeline dynamically adapts to user configurations, retrieving only the data that’s relevant.

LLM service & vector store

The Genie+ feature in DataGenie is our LLM-powered application that unifies all DataGenie features through a conversational interface. We leverage MongoDB as a vector database to store KPI details, dimensions, and dimension values. Each vector document embeds essential metadata, facilitating fast and accurate retrieval for LLM-based queries. By serving as the vector store for DataGenie, MongoDB enables efficient semantic search, allowing the LLM to retrieve contextual, relevant KPIs, dimensions, and values with minimal latency, enhancing the accuracy and responsiveness of Genie+ interactions.

Additionally, integrating MongoDB Atlas Search for semantic search significantly improved performance. It provided faster, more relevant results while minimizing integration challenges.MongoDB’s schema-less design and scalable architecture also streamlined data management.

Knowledge store & cache

MongoDB’s schema-less design enables us to store complex, dynamic relationships and scale them with ease. We also shifted to using MongoDB as our caching layer.

Previously, having separate data stores made syncing and maintenance cumbersome. Centralizing this information in MongoDB simplified operations, enabled automatic syncing, and ensured consistent data availability across all features.

With MongoDB, DataGenie is reducing time-to-market for feature releases

Although we started the MongoDB migration to solve only our existing scalability and latency issues, we soon realized that just by migrating to MongoDB, we could imagine even bigger and more demanding features without engineering limitations.

DataGenie engineering team refers v2 magic moment since migrating to MongoDB makes it a lot easier & flexible to roll out the following new features:

• DataGenie Nirvana: A delay in the supply chain for a raw material can cascade into a revenue impact. Conventional analytics relies on complex ETL pipelines and data marts to unify disparate data and deliver connected dashboard metrics. DataGenie Nirvana eliminates the need for a centralized data lake by independently generating aggregate metrics from each source and applying advanced correlation and causation algorithms on aggregated data to detect hidden connections.

• DataGenie Wisdom: Wisdom leverages an agentic framework & knowledge stores, to achieve two outcomes:

  • Guided onboarding: Onboarding a new use case in DataGenie is as simple as explaining the business problem, success criteria, and sharing sample data - DataGenie autonomously configures itself for relevant metrics tracking to deliver the desired outcome.

  • Next best action: DataGenie autonomously surfaces insights - like a 10% brand adoption spike in a specific market and customer demographics. By leveraging enterprise knowledge bases and domain-specific learning, DataGenie would propose targeted marketing campaigns as the Next Best Action for this insight.

• Powered by Genie: DataGenie offers powerful augmented analytics that can be quickly configured for any use case and integrated through secure, high-performance APIs. This powers data products in multiple verticals, including Healthcare & FinOps, to deliver compelling augmented analytics as a premium add-on, drastically reducing their engineering burden and GTM risk.

All of these advanced features require enormous schema flexibility, low latency aggregation, and a vector database that’s always in sync with the metrics & insights. That’s exactly what we get with MongoDB!

Powered by MongoDB Atlas, DataGenie delivers actionable insights to enterprises, helping them unlock new revenue potential and reduce costs. The following are some of the DataGenie use cases in Retail:

• Demand shifts & forecasting: Proactively adjust inventory or revise marketing strategies based on product demand changes.

• Promotional effectiveness: Optimize marketing spend by understanding which promotions resonate with which customer segments.

• Customer segmentation & personalization: Personalize offers based on customer behavior and demographics.

• Supply chain & logistics: Minimize disruptions by identifying potential bottlenecks and proposing alternative solutions.

• Inventory optimization: Streamline inventory management by flagging potential stockouts or overstock.

• Fraud & loss prevention: Detect anomalies in transaction data that may signal fraud or errors.

• Customer retention & loyalty: Propose retention strategies to address customer churn.

• Staffing optimization: Optimize customer support staffing.

Final thoughts

Migrating to MongoDB did more than just solve DataGenie’s scalability and latency challenges - it unlocked new possibilities. The flexibility of MongoDB allowed DataGenie to innovate faster and conceptualize new features such as Nirvana, Wisdom, and ultra-efficient microservices.

This transformation stands as a proof of concept for future product companies considering partnering with MongoDB. The partnership between DataGenie and MongoDB is a testament to how the right technology choices can drive massive business value, improving performance, scalability, and cost-efficiency.

The dawn of data ubiquity

Currently, we stand in what McKinsey calls the "data ubiquity era"— with data flowing through organizations as essentially as electricity powers the modern world. The transformation to this era has brought both unprecedented opportunity and formidable challenges. Organizations must simultaneously manage huge volumes of data while delivering the real-time, personalized experiences that define competitive advantage today.

Successfully doing so doesn’t mean merely adopting new technologies. Instead, it requires fundamentally rethinking how data is stored, processed, and leveraged to drive business value. Traditional relational database systems simply cannot meet these demands. The future belongs to organizations with data architectures designed for the agility, scalability, and versatility needed to handle diverse data types while seamlessly integrating with emerging technologies like AI.

The cornerstone of AI success

The rise of AI, and the speed at which the market has been changing have fundamentally shifted the importance of adaptability. However, software can only adapt as fast as its foundation allows.

At MongoDB, we believe modern databases are the cornerstone of the age of AI, providing the essential capabilities needed for success in this new era. To do so, they must be able to:

• Handle all forms of data and provide intelligent search: Modern databases consolidate structured and unstructured data into a single system, eliminating silos that restrict AI innovation. They ground AI output in accurate, contextual data that drives better outcomes.

• Scale without constraints and react instantly: Databases should be able to adapt to unpredictable workloads and massive data volumes without performance degradation. They should enable real-time decisions and actions when opportunities or threats emerge.

• Embed domain-specific AI and secure data throughout: Modern databases enhance accuracy with specialized models that reduce hallucinations, and they protect information at every stage without sacrificing speed or functionality.

The impact of a modern database on AI innovation isn’t theoretical—we're seeing organizations like Swisscom leverage this approach to apply generative AI to their extensive expert content library, transforming how they serve the banking industry by delivering bespoke, contextual information within seconds.

The AI revolution

Perhaps nowhere is the impact of a modern data foundation more profound than in the rapid evolution of AI applications. In just a short time, we've progressed from simple LLM-powered chatbots to more advanced agentic systems capable of understanding complex goals, breaking them into manageable steps, and executing them autonomously—all while maintaining context and learning from interactions.

This represents more than incremental progress—it's a fundamental shift in how AI serves as a strategic partner in solving business challenges. MongoDB sits at the heart of this transformation, providing the critical bridge between AI models and data while enabling vector storage, real-time processing, and seamless integration with LLM orchestrators.

Companies like Questflow demonstrate the power of this approach, revolutionizing the future of work through a decentralized, autonomous AI agent network that orchestrates multiple AI agents collaborating with humans. By leveraging MongoDB's flexible document model and vector search capabilities, they're enabling startups to create dynamic AI solutions that handle everything from data analysis to content creation, while maintaining context and learning from interactions.

Modernizing legacy systems: the strategic imperative

For established enterprises, the journey to a modern data foundation often begins with addressing the legacy systems that consume up to 80% of IT budgets yet constrain innovation. Modernization isn't just a technical upgrade—it's a strategic move toward growth, efficiency, and competitive advantage.

The evidence is compelling: Bendigo and Adelaide Bank achieved a staggering 90% reduction in both time and cost modernizing core banking applications using MongoDB's repeatable modernization framework and AI-powered migration tools. This transformation isn't just about cost savings—it's about creating the foundation for entirely new capabilities that drive business value.

Modern data architecture must embody flexibility across multiple dimensions—from supporting diverse data models to providing deployment options spanning cloud-native, cloud-agnostic, and on-premise environments. This approach enables organizations to break free from silos, integrate AI capabilities, and create a unified data foundation supporting both current operations and future innovations.

What’s next for data

The organizations featured throughout Database Digest share a common vision: they recognize that tomorrow's success depends on today's data foundation. The convergence of flexible document models, advanced AI integration, and cloud-native capabilities isn't just enabling incremental improvements—it's powering applications and experiences that were never before possible.

So I invite you to explore the full, inaugural issue of Database Digest to discover how MongoDB is helping organizations across industries build the foundation for tomorrow's success. This isn't just about technology—it's about creating the foundation for transformation that delivers real business value in our increasingly data-driven world.

Today, MongoDB is announcing the general availability of resource policies in MongoDB Atlas. This release introduces seven additional policies and a new graphical user interface (GUI) for creating and managing policies. These enhancements give organizations greater control over MongoDB Atlas configurations, simplifying security and compliance automation.

How resource policies enable secure innovation

Innovation is essential for organizations to maintain competitiveness in a rapidly evolving global landscape. Companies with higher levels of innovation outperformed their peers financially, according to a Cornell University study analyzing S&P 500 companies between 1998 and 2023¹. One of the most effective ways to drive innovation is by equipping developers with the right tools and giving them the autonomy to put them into action².

However, without standardized controls governing those tools, developers can inadvertently configure Atlas clusters to deviate from corporate or regulatory best practices. Manual approval processes for every new project create delays. Concurrently, platform teams struggle to enforce consistent security policies across the organization, leading to increased complexity and costs.

As cybersecurity threats evolve daily and regulations tighten, granting developers autonomy and quickly provisioning access to essential tools can introduce risks. Organizations must implement strong security measures to maintain compliance and enable secure innovation.

Resource policies empower organizations to enforce security and compliance standards across their entire Atlas environment. Instead of targeting specific user groups, these policies establish organization-wide guardrails to govern how Atlas can be configured. This reduces the risk of misconfigurations and security gaps.

With resource policies, security and compliance standards are applied automatically across all Atlas projects and clusters. This eliminates the need for manual approvals. Developers gain self-service access to the resources they need while remaining within approved organizational boundaries. Simultaneously, platform teams can centrally manage resource policies to ensure consistency and free up time for strategic initiatives.

Resource policies strengthen security, streamline operations, and help accelerate innovation by automating guardrails and simplifying governance. Organizations can scale securely while empowering developers to move faster without compromising compliance.

What resource policies are available?

When resource policies first launched in public preview, organizations could use them to enforce consistency in deployment configurations and strengthen access controls. These foundational policies helped teams ensure security and compliance requirements were being followed automatically:

• Restrict cloud provider

• Restrict cloud region

• Block wildcard IP

The three inaugural policies available in public preview established a strong foundation for securing MongoDB Atlas environments while enabling self-service access for developers. With the general availability of resource policies, MongoDB is expanding its capabilities with seven additional policies designed to strengthen security, streamline compliance, and improve operational governance. These additions give organizations greater control over cluster configurations, network access, encryption standards, and maintenance schedules.

Without predefined guardrails, Atlas clusters can grow beyond manageable limits. Organizations are therefore prone to unforeseen infrastructure expenses and increased resource management complexity. To anticipate this risk, MongoDB has introduced the 'Enforce Atlas cluster size limits' resource policy to define and police relevant parameters.

Uncontrolled network access can expose sensitive data and increase security risks. Without guardrails, organizations may inadvertently allow overly permissive connections. This weakens security policy enforcement and puts compliance at risk. To mitigate these risks, the following resource policies have been introduced to ensure strict control over network configurations:

• Restrict IP access list modifications

• Restrict VPC peering modifications

• Restrict private endpoint modifications

Weak or outdated encryption can leave sensitive data vulnerable to interception and compromise. Without strict enforcement of encryption standards, organizations risk non-compliance and exposure to evolving cybersecurity threats. To maintain robust data protection, MongoDB is introducing the following two resource policies to strengthen encryption controls:

• Enforce minimum TLS version

• Restrict TLS cipher suites

Unplanned maintenance can lead to downtime, operational disruptions, and inconsistent system updates. Without a structured approach, organizations will struggle to coordinate upgrades and minimize the impact on critical workloads. To enhance operation predictability, MongoDB has introduced the 'Require maintenance windows' resource policy to standardize maintenance scheduling.

Creating and managing resource policies

With the general availability of resource policies, organizations can now create and manage policies through the Atlas user interface (UI) and programmatically. This flexibility allows teams to enforce security, compliance, and operational standards in a way that aligns with their existing workflows.

For infrastructure automation, resource policies can be defined using infrastructure-as-code (IaC) tools like Terraform and CloudFormation or integrated directly via the Atlas Admin API. This enables organizations to apply policies consistently across their environments while maintaining agility.

Resource policies in Atlas are written using Cedar, an open-source policy language designed for both expressiveness and ease of use. Cedar’s concise syntax simplifies policy creation and management. This makes it easier for teams to enforce guardrails without unnecessary complexity.

To see the process of creating a resource policy through the Atlas UI in action, consider this example: A global financial services company uses MongoDB Atlas to support business-critical applications. To control costs and ensure performance consistency, the company wants to implement a resource policy restricting cluster provisioning to between M30 and M60.

To create the policy through the Atlas UI, a platform engineer would start by navigating to Organization Settings. They would then navigate to the Resource Policies page and select Create Policy to initiate the setup process.

Next, the platform engineer would name the policy and provide a description. In this example, the policy is named “Restrict Cluster Tier,” with the description: “Restrict Atlas from provisioning or scaling clusters to less than M30 or greater than M60.”

After naming the policy and providing a description, the next step is to define the policy using Cedar. In this case, the policy includes a when clause that restricts Atlas from provisioning or scaling clusters to less than M30 or greater than M60.

Finally, the policy is created and activated by clicking Create Policy. Once completed, the platform engineer can view an overview of the newly created policy.

This resource policy now applies to all newly provisioned Atlas projects and clusters. For existing clusters that exceed or fall below the policy’s limits, Atlas only allows modifications that adjust them to comply with the policy. By implementing this policy, the financial services company prevents overspending. Concurrently, they ensure that production workloads run on appropriately sized clusters. Additionally, non-critical environments are prevented from consuming excessive resources.

Getting started with resource policies

Resource policies are now generally available in MongoDB Atlas. Get started creating and managing resource policies through the Atlas user interface or programmatically using infrastructure-as-code tools like Terraform or CloudFormation. Alternatively, integrate directly with the Atlas Admin API. Use the resources below to learn more about resource policies.

• Feature documentation

• Postman Collection

• Terraform Provider documentation

• AWS Cloud Formation documentation

Now generally available, MongoDB Atlas’ new LangChain integration for GraphRAG—a variation of RAG architecture that integrates a knowledge graph with LLMs—can help address these limitations.

GraphRAG: Connecting the dots

First, a short explanation of knowledge graphs: a knowledge graph is a structured representation of information in which entities (such as people, organizations, or concepts) are connected by relationships. Knowledge graphs work like maps, and show how different pieces of information relate to each other. This structure helps computers understand connections between facts, answer complex questions, and find relevant information more easily.

Traditional RAG applications split knowledge data into chunks, vectorize them into embeddings, and then retrieve chunks of data through semantic similarity search; GraphRAG builds on this approach. But instead of treating each document or chunk as an isolated piece of information, GraphRAG considers how different pieces of knowledge are connected and relate to each other through a knowledge graph.

GraphRAG improves RAG architectures in three ways:

First, GraphRAG can improve response accuracy. Integrating knowledge graphs into the retrieval component of RAG has shown significant improvements in multiple publications. For example, benchmarks in the AWS investigation, “Improving Retrieval Augmented Generation Accuracy with GraphRAG” demonstrated nearly double the correct answers compared to traditional embedding-based RAG.

Also, embedding-based methods rely on numerical vectors and can make it difficult to interpret why certain chunks are related. Conversely, a graph-based approach provides a visual and auditable representation of document relationships.

Consequently, GraphRAG offers more explainability and transparency into retrieved information for improved insight into why certain data is being retrieved. These insights can help optimize data retrieval patterns to improve accuracy.

Finally, GraphRAG can help answer questions that RAG is not well-suited for—particularly when understanding a knowledge base's structure, hierarchy, and links is essential. Vector-based RAG struggles in these cases because breaking documents into chunks loses the big picture.

For example, prompts like “What are the themes covered in the 2025 strategic plan?” are not well handled. This is because the semantic similarity between the prompt, with keywords like “themes,” and the actual themes in the document may be weak, especially if they are scattered across different sections. Another example prompt like, “What is John Doe’s role in ACME’s renewable energy projects?” presents challenges because if the relationships between the person, the company, and the related projects are mentioned in different places, it becomes difficult to provide accurate responses with vector-based RAG.

Traditional vector-based RAG can struggle in cases like these because it relies solely on semantic similarity search. The logical connections between different entities—such as contract clauses, legal precedents, financial indicators, and market conditions—are often complex and lack semantic keyword overlap. Making logical connections across entities is often referred to as multi-hop retrieval or reasoning in GraphRAG.

However, GraphRAG has its own limitations, and is use-case dependent to achieve better accuracy than vector-based RAG:

• It introduces an extra step: creating the knowledge graph using LLMs to extract entities and relationships. Maintaining and updating the graph as new data arrives becomes an ongoing operational burden. Unlike vector-based RAG, which requires embedding and indexing—a relatively lightweight and fast process—GraphRAG depends on a large LLM to accurately understand, map complex relationships, and integrate them into the existing graph.

• The added complexity of graph traversal can lead to response latency and scalability challenges as the knowledge base grows. Latency is closely tied to the depth of traversal and the chosen retrieval strategy, both of which must align with the specific requirements of the application.

• GraphRAG introduces additional retrieval options. While this allows developers more flexibility in the implementation, it also adds complexity. The additional retrieval options include keyword and entity-based retrieval, semantic similarity on the first node, and more.

MongoDB Atlas: A unified database for operational data, vectors, and graphs

MongoDB Atlas is perfectly suited as a unified database for documents, vectors, and graphs. As a unified platform, it’s ideal for powering LLM-based applications with vector-based or graph-based RAG. Indeed, adopting MongoDB Atlas eliminates the need for point or bolt-on solutions for vector or graph functionality, which often introduce unnecessary complexity, such as data synchronization challenges that can lead to increased latency and potential errors.

The unified approach offered by MongoDB Atlas simplifies the architecture and reduces operational overhead, but most importantly, it greatly simplifies the development experience. In practice, this means you can leverage MongoDB Atlas' document model to store rich application data, use vector indexes for similarity search, and model relationships using document references for graph-like structures.

Implementing GraphRAG with MongoDB Atlas and LangChain

Starting from version 0.5.0, the langchain-mongodb package introduces a new class to simplify the implementation of a GraphRAG architecture.

First, it enables the automatic creation of a knowledge graph. Under the hood, it uses a specific prompt sent to an LLM of your choice to extract entities and relationships, structuring the data to be stored as a graph in MongoDB Atlas.

Then, it sends a query to the LLM to extract entities and then searches within the graph to find connected entities, their relationships, and associated data. This information, along with the original query, then goes back to the LLM to generate an accurate final response.

MongoDB Atlas’ integration in LangChain for GraphRAG follows an entity-based graph approach. However, you can also develop and implement your own GraphRAG with a hybrid approach using MongoDB drivers and MongoDB Atlas’ rich search and aggregation capabilities.

Enhancing knowledge retrieval with GraphRAG

GraphRAG complements traditional RAG methods by enabling deeper understanding of complex, hierarchical relationships, supporting effective information aggregation and multi-hop reasoning. Hybrid approaches that combine GraphRAG with embedding-based vector search further enhance knowledge retrieval, making them especially effective for advanced RAG and agentic systems.

MongoDB Atlas’ unified database simplifies RAG implementation and its variants, including GraphRAG and other hybrid approaches, by supporting documents, vectors, and graph representations in a unified data model that can seamlessly scale from prototype to production. With robust retrieval capabilities ranging from full-text and semantic search to graph search, MongoDB Atlas provides a comprehensive solution for building AI applications. And its integration with proven developer frameworks like LangChain accelerates the development experience—enabling AI developers to build more advanced and efficient retrieval-augmented generation systems that underpin AI applications.

In this race to stay ahead of their competition, retailers today are struggling to quickly bring to market these agents and don’t always have the AI skills in-house. Many are looking to the broad ecosystem of off-the-shelf solutions to leverage the best of what’s already out there—reducing time to market for their AI agents and leaving the AI models and integrations to the experts in the field.

Some of the most successful retail conversational AI agents we’ve seen are built on Cognigy, a global leader in customer service solutions. With Cognigy, retailers are quickly spinning up conversational AI agents on top of their MongoDB data to create personalized conversational experiences that not only meet but anticipate customer expectations. Increasingly, whether or not retailers offer customers immediate, seamless interactions are key to retaining their loyalty.

Why next-gen conversational AI matters in retail

Customer loyalty has been declining yearly, and customers are moving to retailers who can provide an elevated experience at every interaction. According to HubSpot’s 2024 annual customer service survey, 90% of customers expect an immediate response to their inquiries, highlighting how speed has become a critical factor in customer satisfaction. Additionally, 45.9% of business leaders prioritize improving customer experience over product and pricing, demonstrating that in retail, speed and personalization are no longer optional as they define whether a customer stays or moves on.

The chatbots of the past that relied on simple rules-based engines and static data don’t meet these customers' new expectations as they lack real-time business context, and can generate misleading answers as they’re not training on the retailer's in-house data sets.

This is where Cognigy’s AI agents can create a more compelling experience: These intelligent systems integrate real-time business data with the capabilities of LLMs, enabling AI-driven experiences that are not only personalized but also precise and controlled. Instead of leaving responses open to interpretation, retailers can customize interactions, guide users through processes, and ensure AI-driven recommendations align with actual inventory, customer history, and business rules. This level of contextual understanding and action creates trust-driven experiences that foster loyalty.

Having quality data and the ability to harness it effectively is the only way to meet the strategic imperatives that customers demand today. This requires key factors such as being fast, flexible, and high-performing at the scale of your business operations, as winning companies must store and manage their information efficiently. This is where MongoDB, a general-purpose database, truly shines. It is designed to manage your constantly evolving business data, such as inventory, orders, transaction history, and user preferences. MongoDB’s document model stands out in the retail industry, offering the flexibility and scalability businesses need to thrive in today’s fast-paced environment.

Cognigy can use this real-time operational data from MongoDB as a direct input to build, run, and deploy conversational AI agents at scale. With just a few clicks, businesses can create AI-driven chatbots and voice agents powered by large language models (LLMs), following their business workflows in a smooth and easy-to-implement way. These agents can seamlessly engage with customers across various phone lines as a major driver for customer interactions, including website chat, Facebook Messenger, and WhatsApp, offering personalized interactions. On the back end, Cognigy is built on MongoDB as its operational data store, taking full advantage of MongoDB’s scalability and high performance to ensure that its conversational AI systems can efficiently process and store large volumes of real-time data while maintaining high availability and reliability.

The power of combining AI agents with real-time business data transforms personalization from a static concept into a dynamic ever-evolving experience that makes customers feel truly recognized and understood at every touchpoint. By harnessing these intelligent systems, retailers can go beyond generic interactions to deliver seamless, relevant, and engaging experiences that naturally strengthen customer relationships. Ultimately, true personalization isn’t just about efficiency; it’s about creating meaningful connections that drive lasting customer engagement and loyalty.

Let’s look at how this looks in the Cognigy interface when you’re creating a flow for your chatbot:

What’s happening behind the scenes?

Figure 1 below shows an example customer journey, and demonstrates how Cognigy and MongoDB work together to use real-time data to give reliable and conversational responses to customer questions:

This user’s journey starts when they make a purchase on a retailer’s ecommerce application. The platform securely stores the order details, including product information, customer data, and order status, in MongoDB.

To coordinate the delivery, the user reaches out via a chatbot or phone conversation orchestrated by Cognigy AI agents, using advanced Large Language Models (LLMs) to understand the user’s inquiries and respond in a natural, conversational tone.

The AI agent retrieves the necessary user information and order details from MongoDB, configured as the data source, taking real-time data that is always up to date. By understanding the user’s query, the agent retrieves the appropriate database information and is also able to update the database with any relevant information generated during the conversation, such as modifying a delivery appointment.

As the user schedules their delivery, Cognigy updates the information directly in MongoDB, leveraging features like triggers and change streams to seamlessly synchronize real-time data with other key systems in the customer journey, such as inventory management and delivery providers. This ensures personalized user experiences at every interaction.

Shaping the future of customer service with MongoDB and Cognigy

Delivering responsive, personalized customer service is more essential than ever. By combining MongoDB’s flexible, versatile, and performant data management with Cognigy’s powerful conversational AI, businesses can create seamless, real-time interactions that keep customers engaged. The future of customer service is fast, dynamic, and seamlessly integrated into business operations.

With MongoDB and Cognigy, organizations can harness the power of AI to automate and personalize customer interactions in real time, without the need for extensive development efforts. The MongoDB-Cognigy integration enables businesses to scale context-driven interactions, strengthen customer relationships, and exceed expectations while building lasting customer loyalty.

The event comes as MongoDB Atlas adds availability for Google Cloud regions in Mexico and South Africa, further expanding joint customers’ ability to deploy, scale, and manage their applications closer to their users while meeting local compliance and performance requirements.

MongoDB is also honored to have achieved the 2025 Google Cloud Partner of the Year for Data & Analytics - Marketplace. This award is a testament to the enterprise-scale success stories driven by our combined data and analytics solutions. It’s also MongoDB’s sixth consecutive year as a Google Cloud Partner of the Year, reflecting the relentless innovation and customer-first mindset that define MongoDB’s partnership with Google Cloud.

This is in addition to achieving the Google Cloud Ready – Regulated and Sovereignty Solutions Badge. The designation is a major milestone for MongoDB, and demonstrates our ability to deliver compliant and secure solutions that meet the highest standards for data sovereignty.

More broadly, we’ve been focused on expanding our collaboration in order to unlock new opportunities for customers to enhance developer productivity, launch AI-powered applications, and do more with their data in 2025. Read on to learn more about what we’ve been working on.

Enhancing developer productivity with gen AI

For over a decade, MongoDB and Google Cloud have established a rich track record of making it easier, faster, and more secure to build enterprise-grade applications. Our latest gen AI collaborations further this mission, simultaneously enhancing innovation and efficiency.

MongoDB is proud to be a launch partner for Google Cloud’s Gemini Code Assist. Announced in December and launching for MongoDB users this week at Google Cloud Next, our integration with Gemini Code Assist enables developers to seamlessly access the latest MongoDB documentation and code snippets within their IDEs. This innovative integration enhances developer productivity by providing immediate access to MongoDB resources, making development workflows more efficient by keeping developers 'in the flow' rather than having to hop in and out of the IDEs to find the information and code examples they need.

MongoDB is also expanding our presence in Project IDX, an AI-assisted development workspace for full-stack, multiplatform applications. With MongoDB templates now available in IDX, developers can quickly set up MongoDB environments without leaving their IDE, accelerating the development of generative AI applications and other cloud-based solutions. Learn more by reading this blog post from Google.

Developers building applications in Firebase can now integrate MongoDB Atlas with a few clicks. The new Firebase extension for MongoDB Atlas eliminates the need for complex query pipelines or manual data transfers, making it easier than ever to deploy and scale apps leveraging MongoDB Atlas as a vector database in Firebase. Additionally, the new MongoDB extension enables real-time synchronization between Firebase and MongoDB data, ensuring data consistency across both platforms. By combining the power of Firebase Extensions, MongoDB Atlas, and a direct MongoDB connector, developers can create innovative and data-driven applications with greater efficiency and ease.

Streamlining cloud migrations

Developers are taking advantage of the latest models and tooling to build the next wave of gen AI applications. As they do so, they’re wrangling unprecedented volumes of structured and unstructured data, and, in doing so, are facing growing requirements for application scalability and performance. As such, businesses have uncovered a newfound imperative to modernize and take advantage of cloud-native solutions that offer the highest levels of scalability and performance, as well as interoperability with their favorite tools.

MongoDB and Google Cloud have made it even easier to make the move to the cloud with Google Migration Center, a unified platform that streamlines the transition from on-premises servers to the Google Cloud environment, offering tools for discovery, assessment, and planning. Within Google Cloud Migration Center, users can now generate cost assessments and migration plans for the on-premises MongoDB instances directly in Google Cloud Migration Console, simplifying the transition to MongoDB Atlas on Google Cloud.

Specifically, users can now use integrated MongoDB cluster assessment to gain in-depth visibility into MongoDB deployments, both Community and Enterprise editions, running on your existing infrastructure. Learn more on our blog.

With the aim of making it easier and quicker to deploy to the cloud, MongoDB Atlas is now available as part of Cloud Foundation Fabric. Specifically, Atlas is available within Fabric FAST, which streamlines Google Cloud organization setup using a pre-defined enterprise-grade design and a Terraform reference implementation.

By integrating MongoDB Atlas, enterprises can enhance production readiness for applications that require persistent data, offloading database management overhead while ensuring high availability, scalability, and security. This integration complements FAST’s infrastructure automation, enabling organizations to quickly deploy robust, data-driven applications within an accelerated Google Cloud environment, reducing the time needed to establish a fully functional, enterprise-level platform. Check out the Github repository.

Optimizing analytics and archiving

Despite many organizations’ focus on modernization and generative AI, analytics and data warehousing remain essential pillars of the enterprise workflow.

Building on our existing integration with BigQuery, Google Cloud’s fully managed data warehouse, MongoDB Atlas now offers native JSON support for BigQuery, eliminating the need for complex data transformations. This enhancement significantly reduces operational costs, improves query performance, and enables businesses to analyze structured and unstructured data with greater flexibility and efficiency. The Dataflow template is now 'Generally Available' for MongoDB and Google Cloud customers.

A key advantage of this pipeline lies in its ability to directly leverage BigQuery's powerful JSON functions on the MongoDB data loaded into BigQuery. This eliminates the need for a complex and time-consuming data transformation process as the JSON data within BigQuery can be queried and analyzed using standard BQML queries. Learn more about the new launch from our blog.

In a big step forward for MongoDB, Atlas now supports Data Federation and Online Archive directly on Google Cloud. With these new additions, users can effortlessly manage and archive cold data and perform federated queries across Google Cloud storage, all from within their MongoDB Atlas console. This integration provides businesses with cost-effective data management and analysis capabilities.

Upskilling for the AI era

Earlier this year, MongoDB introduced a new Skill Badges program, offering focused credentials to help learners quickly master and validate their skills with MongoDB. These badges are an excellent way for developers, database administrators, and architects to demonstrate their dedication to skill development and continuous learning. In just 60-90 minutes, participants can learn new skills, finish a short assessment, and earn a shareable digital badge through Credly. They can then display this badge on platforms like LinkedIn to highlight their accomplishments and career development.

At Google Next, attendees will have the opportunity to earn the RAG with MongoDB Skill Badge by using our self-paced Google Lab or by interacting directly with our experts in the Makerspace. This badge focuses on building Retrieval-Augmented Generation (RAG) applications, teaching participants how to integrate vector search and improve retrieval workflows to enhance apps powered by LLMs. Whether you prefer the guided support in the Makerspace or the flexibility of the self-paced lab, this hands-on experience will provide you with advanced skills that you can apply to your projects.

The bottom line is that we’ve been busy! MongoDB’s deepening collaboration with Google Cloud continues to unlock new innovations across AI, application development, and cloud infrastructure.

The Atlas platform has expanded exponentially since the last navigation redesign in 2020, with MongoDB introducing a plethora of new features and functionality, including Atlas Search and Vector Search, Atlas Charts, and Atlas Stream Processing. The latest navigation redesign has been architected from the outset to encompass these capabilities, addressing users' diverse needs—from monitoring deployments and managing billing to enhancing data visualization and enabling advanced search functionality—while delivering a streamlined, workflow-driven platform for users.

Starting two and a half years ago, MongoDB’s Design Strategy team began the redesign process by collecting customer feedback and engaging in dialogue. The team’s overall goal with the Atlas and Cloud Manager redesign was to create a holistic, seamlessly integrated platform that streamlined the developer experience.

The redesigned navigation improves developers’ experience in the following ways:

• Workflow-focused architecture: The new architecture is clean and intuitive. It preserves developers’ “flow state” by guiding them through drill-down workflows. The new navigation prioritizes platform services, highlighting them based on the user’s workflow. This makes it easier for developers to focus on the most relevant tools for their current tasks, enabling them to work more efficiently and innovate faster.

• Consistent, familiar experience: The new navigation design provides a consistent experience across Atlas and Cloud Manager platforms. This makes it easier for developers to switch between the two interfaces. This consistent, intuitive interface enhances wayfinding and boosts overall productivity.

What’s changing in MongoDB Atlas?

The redesigned Atlas navigation introduces the following key updates:

1. Clearer resource context

The updated top navigation bar, the resource navigator, ensures developers always know which resource (e.g., project, organization, cluster) they are working on. Switching between resources is now simpler, with improved context clarity as users navigate deeper into Atlas. For example, imagine switching between search indexes across different collections. Now, it can be done in a single click. The new workflow negates any need to backtrack to a project’s overview.

2. Centralized utilities hub

Essential utilities like Alerts, Billing, Help, and Identity Management (IAM) are consolidated in one location at the top-right corner. This ensures rapid access and saves time. Users can also access the product menu to find MongoDB University, Documentation, Community Forums, and Support.

3. Simplified left navigation

The side navigation is now organized into four categories: Database, Data, Services, and Security. These categories act as distinct containers, grouping Atlas’s capabilities to reflect the tasks a developer needs to perform within Atlas.

This new structure makes navigating Atlas easier, helping developers find the right tools faster. Below is a breakdown of where features will be housed to make access to your essential tools even more straightforward:

• Database: Contains all core database capabilities. Includes cluster management and monitoring tools for browsing and querying, backups, and Online Archive.

• Data: Contains tools for working with data. Includes tools like visualization (Atlas Charts) to create and embed data visualizations, Atlas Search and Vector Search for powerful search capabilities and Data Federation for cross-source queries.

• Services: Contains features for event-driven data processing and automation. Includes capabilities such as Stream Processing for real-time data analysis, Triggers for automating database actions, and Migration for migrating existing deployments to Atlas.

• Security: Contains controls for data access and protection. Includes capabilities like project settings, Identity & Access Management (IAM), auditing, and advanced security.

On the organizational level, the new architecture for the side navigation will be organized into two categories: identity and access, and billing.

What’s changing in Cloud Manager?

Although Cloud Manager will function similarly to Atlas, we did make several changes to refine the Cloud Manager experience:

1. Left navigation

On the organizational level, the new architecture for the side navigation will be organized into three categories:

• Identity and access: Add, delete, and manage users, teams, and API Keys within a specific project.

• Billing: View, track, and manage your charges while using Cloud Manager.

• Management: Set up Kubernetes, and manage additional administrative functions.

On a project level, the side navigation will be organized into two categories:

• Database: Manage Processes, Servers, Agents, Security, and Continuous Backup for your deployments.

• Admin: Monitor Pings, MongoDB Process Arguments, Deleted Hosts, Profiler Request History, and Raw Automation Config.

2. Resource navigator

Cloud Manager will have the same resource navigator tool in the top navigation bar as Atlas. This provides clear visibility of the resource users are working on in Cloud Manager, whether it’s a project or an organization.

3. Centralized utilities hub

Cloud Manager will also feature the updated utility hub mentioned in the changes coming to Atlas. This hub allows users to access the same essential utilities and product menu to discover other MongoDB offerings in one place.

Rollout timeline

To ensure a smooth transition, MongoDB will be rolling out the new navigation experience in phases. The Atlas update is currently going live, and the Cloud Manager update will begin the week of May 12, 2025.

Note that the Atlas experience will take 6 to 8 weeks to be available to all Atlas organizations. All organizations will experience the new navigation by June 2025.

Atlas and Cloud Manager users can submit feedback to share their thoughts on the new navigation experience. Explore MongoDB’s updated documentation for more details on the latest changes to the navigation.

MongoDB is excited to announce a significant enhancement to Google Cloud Migration Center: integrated MongoDB cluster assessment in the Migration Center Use Case Navigator. Google Cloud and MongoDB have collaborated to help you gain in-depth visibility into your MongoDB deployments, both MongoDB Community Edition and MongoDB Enterprise Edition, and simplify your move to the cloud.

To understand the benefits of using Migration Center, let’s compare it with the process of migrating without it.

Migrating without Migration Center

• Manual discovery: Without automation, asset inventories were laborious, leading to frequent errors and omissions.

• Complex planning: Planning involved cumbersome spreadsheets and manual dependency mapping, making accurate cost estimation and risk assessment difficult.

• Increased risk: Lack of automated assessment resulted in higher migration failure rates and potential data loss, due to undiscovered compatibility issues.

• Fragmented tooling: Disparate tools for each migration phase created inefficiencies and complexity, hindering a unified migration strategy.

• Higher costs and timelines: Manual processes and increased risks significantly lengthened project timelines and inflated migration costs.

• Specialized skill requirement: Migrating required teams to have deep specialized knowledge of all parts of the infrastructure being moved.

Migrating with Migration Center

When you move to the cloud, you want to make your systems better, reduce costs, and improve performance. A well-planned migration helps you do that. With Migration Center’s new MongoDB assessment, you can:

• Discover and inventory your MongoDB clusters: Easily identify all your MongoDB Community Server and MongoDB Enterprise Server clusters running in your on-premises environment.

• Gain deep insights: Understand the configuration, performance, and resource utilization of your MongoDB clusters. This data is essential for planning a successful and cost-effective migration.

• Simplify your migration journey: By providing a clear understanding of your current environment, Migration Center helps you make informed decisions and streamline the migration process, minimizing risk and maximizing efficiency.

• Use a unified platform: Migration Center is designed to be a one-stop shop for your cloud migration needs. It integrates asset discovery, cloud spend estimation, and various migration tools, simplifying your end-to-end journey.

• Accelerate using MongoDB Atlas: Migrate your MongoDB workloads to MongoDB Atlas running on Google Cloud with confidence. Migration Center provides the data you need to ensure a smooth transition, enabling you to fully use the scalability and flexibility of MongoDB Atlas.

By providing MongoDB workload identification and guidance, the Migration Center Use Case Navigator enables you to gain valuable insights into the potential transformation journeys for your MongoDB workloads. With the ability to generate comprehensive reports on your MongoDB workload footprint, you can better understand your MongoDB databases. This ultimately enables you to update your systems and gain the performance enhancement of using MongoDB Atlas on Google Cloud, all while saving money.

We’re pleased to announce the release of the Firebase extension MongoDB Atlas, a direct MongoDB connector for Firebase that further streamlines the development process by enabling seamless integration between the two platforms. This extension enables developers to directly interact with MongoDB collections and documents from within their Firebase projects, simplifying data operations and reducing development time.

A direct MongoDB connector, built as a Firebase extension, facilitates real-time data synchronization between Firebase and MongoDB Atlas. This enables data consistency across both platforms, empowering developers to build efficient, data-driven applications using the strengths of Firebase and MongoDB.

MongoDB as a backend database for Firebase applications

Firebase offers a streamlined backend for rapid application development, providing offerings like authentication, hosting, and real-time databases. However, applications requiring complex data modeling, high data volumes, or sophisticated querying often work well with MongoDB’s document store. Integrating MongoDB as the primary data store alongside Firebase addresses these challenges. MongoDB provides a robust document database with a rich query language (MongoDB Query Language), powerful indexing (including compound, geospatial, and text indexes), and horizontal scalability for handling massive datasets. This architecture enables developers to use Firebase’s convenient backend services while benefiting from MongoDB’s powerful data management capabilities. Developers commonly use Firebase Authentication for user management, then store core application data, including complex relationships and large volumes of information, in MongoDB. This hybrid approach combines Firebase’s ease of use with MongoDB’s data-handling prowess.

Furthermore, the integration of MongoDB Atlas Vector Search significantly expands the capabilities of this hybrid architecture. Modern applications increasingly rely on semantic search and AI-driven features, which require efficient handling of vector embeddings. MongoDB Atlas Vector Search enables developers to perform similarity searches on vector data, unlocking powerful use cases

Quick-start guide for Firebase’s MongoDB Atlas extension

With the initial release of the MongoDB Atlas extension in Firebase, we are targeting the extension to perform operations such as findOne, insertOne, and vectorSearch on MongoDB. This blog will not cover how to create a Firebase application but will walk you through creating a MongoDB backend for connecting to MongoDB using our Firebase extension. To learn more about how to integrate the deployed backend into a Firebase application, see the official Firebase documentation.

1. Install the MongoDB Atlas extension in Firebase.

• Open the Firebase Extensions Hub.

• Find and select the MongoDB Atlas extension.

2. Or use the search bar to find “MongoDB Atlas.”

• Click on the extension card.

• Click the “Install” button. You will be redirected to the Firebase console.

3. On the Firebase console, choose the Firebase project where you want to install the extension.

4. On the installation page:

• Review “Billing and Usage.”

• Review “API Endpoints.”

• Review the permissions granted to the function that will be created.

• Configure the extension: Provide the following configuration details:

  • MongoDB URI: The connection string for your MongoDB Atlas cluster

    • Database Name: The name of the database you want to use

    • Collection Name: The name of the collection you want to use

    • Vertex AI Embedding to use: The type of embedding model from Vertex AI

    • Vertex AI LLM model name: The name of the large language model (LLM) model from Vertex AI

    • MongoDB Index Name: The name of the index in MongoDB

    • MongoDB Index Field: The field that the index is created upon

    • MongoDB Embedding Field: The field that contains the embedding vectors

    • LLM Prompt: The prompt that will be sent to the LLM

5. Click on “Install Extension.”

Once the extension is created, you can interact with it through the associated Cloud Function.

In conclusion, the synergy between Firebase extensions and MongoDB Atlas opens up exciting possibilities for developers seeking to build efficient, scalable, AI-powered applications. By using Firebase’s streamlined backend services alongside MongoDB’s robust data management and vector search capabilities, developers can create applications that handle complex data and sophisticated AI functionalities with ease.

The newly introduced Firebase extension for MongoDB Atlas, specifically targeting operations like findOne, insertOne, and vectorSearch, marks a significant step toward simplifying this integration. While this initial release provides a solid foundation, the potential for further enhancements, such as direct connectors and real-time synchronization, promises to further empower developers. As demonstrated through the quick-start guide, setting up this powerful combination is straightforward, enabling developers to quickly harness the combined strength of these platforms.

Ultimately, this integration fosters a more flexible and powerful development environment, enabling the creation of innovative, data-driven applications that meet the demands of modern users.

Vehicles today are sophisticated machines, computers on wheels, that generate massive amounts of data, driving demand for connected and electric vehicles. Automotive players are embracing artificial intelligence (AI), battery electrical vehicles (BEVs), and software-defined vehicles (SDVs) to maintain their competitive advantage.

However, managing fleets of connected vehicles can be a challenge. As cars get more sophisticated and are increasingly integrated with internal and external systems, the volume of data they produce and receive greatly increases. This data needs to be stored, transferred, and consumed by various downstream applications to unlock new business opportunities. This will only grow: the global fleet management market is projected to reach $65.7 billion by 2030, growing at a rate of almost 10.8% annually.

A 2024 study conducted by Webfleet showed that 32% of fleet managers believe AI and machine learning will significantly impact fleet operations in the coming years; optimizing route planning and improving driver safety are the two most commonly cited use cases. As fleet management software providers continue to invest in AI, the integration of agentic AI can significantly help with things like route optimization and driver safety enhancement. For example, AI agents can process real-time traffic updates and weather conditions to dynamically adjust routes, ensuring timely deliveries while advising drivers on their car condition. This proactive approach contrasts with traditional reactive methods, improving vehicle utilization and reducing operational and maintenance costs.

But what are agents? In short, they are operational applications that attempt to achieve goals by observing the world and acting upon it using the data and tools the application has at its disposal. The term "agentic" denotes having agency, as AI agents can proactively take steps to achieve objectives without constant human oversight. For example, rather than just reporting an anomaly based on telemetry data analysis, an agent for a connected fleet could autonomously cross-check that anomaly against known issues, decide whether it's critical or not, and schedule a maintenance appointment all on its own.

Why MongoDB for agentic AI

Agentic AI applications are dynamic by nature as they require the ability to create a chain of thought, use external tools, and maintain context across their entire workflow. These applications generate and consume diverse data types, including structured and unstructured data. MongoDB’s flexible document model is uniquely suited to handle both structured and unstructured data as vectors. It allows all of an agent’s context, chain-of-thought, tools metadata, and short-term and long-term memory to be stored in a single database. This means that developers can spend more time on innovation and rapidly iterate on agent designs without being constrained by rigid schemas of a legacy relational database.

Figure 1 shows the major components of an AI agent. The agent will first receive a task from a human or via an automated trigger, and will then use a large language model (LLM) to generate a chain of thought or follow a predetermined workflow. The agent will use various tools and models during its run and store/retrieve data from a memory provider like MongoDB Atlas.

• Tools: The agent utilizes tools to interact with the environment. This can contain API methods, database queries, vector search, RAG application, anything to support the model

• Models: can be a large language model (LLM), vision language model (VLM), or a simple supervised machine learning model. Models can be general purpose or specialized, and agents may use more than one.

• Data: An agent requires different types of data to function. MongoDB’s document model allows you to easily model all of this data in one single database. An agentic AI spans a wide range of functional tools and context. The underlying data structures evolve throughout the agentic workflow and as an agent uses different tools to complete a task. It also builds up memory over time. Let us list down the typical data types you will find in an agentic AI application.

Data types:

• Agent profile: This contains the identity of the agent. It includes instructions, goals and constraints.

• Short-term memory: This holds temporary, contextual information—recent data inputs or ongoing interactions—that the agent uses in real-time. For example, short-term memory could store sensor data from the last few hours of vehicle activity. In certain agentic AI frameworks like Langgraph, short term memory is implemented through a checkpointer. The checkpointer stores intermediate states of the agent’s actions and/or reasoning. This memory allows the agent to seamlessly pause and resume operations.

• Long-term memory: This is where the agent stores accumulated knowledge over time. This may include patterns, trends, logs and historical recommendations and decisions.

By storing each of these data types into rich, nested documents in MongoDB, AI developers can create a single-view representation of an agent’s state and behavior. This enables fast retrieval and simplifies development. In addition to the document model advantage, building agentic AI solutions for mobility requires a robust data infrastructure. MongoDB Atlas offers several key advantages that make it an ideal foundation for these AI-driven architectures. These include:

Scalability and flexibility: Connected Car platforms like fleet management systems need to handle extreme data volumes and variety. MongoDB Atlas is proven to scale horizontally across cloud clusters, letting you ingest millions of telemetry events per minute and store terabytes of telemetry data with ease. For example, the German company ZF uses MongoDB to process 90,000 vehicle messages per minute (over 50 GB of data per day) from hundreds of thousands of connected cars​. The flexibility of the document model accelerates development and ensures your data model stays aligned with the real-world entities it represents.

Built-in vector search: AI agents require a robust set of tools to work with. One of the most widely used tools is vector search, which allows agents to perform semantic searches on unstructured data like driver logs, error codes descriptions, and repair manuals. MongoDB Atlas Vector Search allows you to store and index high-dimensional vectors alongside your documents and to perform semantic search over unstructured data. In practice, this means your AI embeddings live right next to the relevant vehicle telemetry and operational data in the database, simplifying architectures for use cases like the connected car incident advisor, in which a new issue can be matched against past issues before passing contextual information to the LLM. For more, check out this example of how an automotive OEM leverages vector search for audio based diagnostics with MongoDB Atlas Vector Search.

Time series collections and real-time data processing: MongoDB Atlas is designed for real-time applications. It provides time series collections for connected car telemetry data storage, change streams, and triggers that can react to new data instantly. This is crucial for agentic AI feedback loops, where ongoing data ingestion and learning are happening continuously.

Best-in-class embedding models with Voyage AI: In early 2025, MongoDB acquired Voyage AI, a leader in embedding and reranking models. Voyage AI embedding models are currently being integrated into MongoDB Atlas, which means developers will no longer need to manage external embedding APIs, standalone vector stores, or complex search pipelines. AI retrieval will be built into the database itself, making semantic search, vector retrieval, and ranking as seamless as traditional queries. This will reduce the time required for developing agentic AI applications.

Agentic AI in action: Connected fleet incident advisor

Figure 2 shows a list of use cases in the Mobility sector, sorted by various capabilities that an agent might demonstrate. AI agents excel at managing multi-step tasks via context management across tasks, they automate repetitive tasks better than Robotic process automation (RPA), and they demonstrate human-like reasoning by revisiting and revising past decisions. These capabilities enable a wide range of applications both during the manufacturing of a vehicle and while it's on the road, connected and sending telemetry. We will review a use case in detail below, and will see how it can be implemented using MongoDB Atlas, LangGraph, Open AI, and Voyage AI.

First, the AI agent connects to traditional fleet management software and supports the fleet manager in diagnosing and advising the drivers. This is an example of a multi-step diagnostic workflow that gets triggered when a driver submits a complaint about the vehicle's performance (for example, increased fuel consumption). Figure 3 shows the sequence diagram of the agent. Upon receiving the driver complaint, it creates a chain of thought that follows a multi-step diagnostic workflow where the system ingests vehicle data such as engine codes and sensor readings, generates embeddings using the Voyage AI voyage-3-large embedding model, and performs a vector search using MongoDB Atlas to find similar past incidents.

Once relevant cases are identified, those–along with selected telemetry data–are passed to OpenAI gpt-4o LLM to generate a final recommendation for the driver (for example, to pull off immediately or to keep driving and schedule regular maintenance). All data, including telemetry, past issues, session logs, agent profiles, and recommendations are stored in MongoDB Atlas, ensuring traceability and the ability to refine diagnostics over time. Additionally, MongoDB Atlas is used as a checkpointer by LangGraph, which defines the agent's workflow.

Figure 4 shows the agent in action, from receiving an issue to generating a recommendation. So by leveraging MongoDB’s flexible data model and powerful Vector Search capabilities, we can agentic AI can transform fleet management through predictive maintenance and proactive decision-making.

But achieving these gains isn’t always straightforward. Many manufacturers still face the challenge of siloed data and legacy systems, making it difficult to get a real-time, holistic view of operations. Shop floor data, enterprise resource planning (ERP) systems, manufacturing execution system (MES) platforms, and other sources often operate in isolation, limiting the potential for optimization.

The concept of a Unified Namespace model, which provides a single source of truth for all operational data, is a game-changing approach that helps unify these siloed systems into a cohesive ecosystem. MongoDB, with its powerful document-based model, is perfectly suited to be the backbone of this Unified Namespace model, acting as a flexible, scalable, highly available, and real-time repository that can seamlessly integrate and manage complex manufacturing data.

In this blog post, we’ll explore how MongoDB’s architecture and capabilities align perfectly with the needs of a UNS, and how our "Leafy Factory" demo serves as a strong proof point of this alignment.

Understanding the Unified Namespace and its importance in manufacturing

A Unified Namespace (UNS) is an architecture in which production data across an organization is consolidated into one central data repository. In a manufacturing setup, a UNS enables the integration of diverse sources like ERP for business operations, MES for production monitoring, and real-time shop floor data. This centralized model provides a single, consistent view of data, allowing teams across the organization to access reliable information for decision-making.

By unifying data from various systems, a UNS makes it significantly easier to connect disparate systems and ensures that data can be shared seamlessly across platforms, reducing complexity and integration overhead. Unlike the traditional automation pyramid, in which information flows hierarchically from sensors up through control systems, MES, and finally to ERP, the UNS breaks down these layers. It creates a flat, real-time data model that allows every system to access and contribute to a shared source of truth—eliminating delays, redundancies, and disconnects between layers.

One of the most impactful advantages of a UNS is real-time data visibility. By centralizing live data streams from the production floor, it provides stakeholders—from operators to executives—with up-to-the-second insights. This immediacy empowers teams to make informed decisions quickly, respond to issues as they arise, and continuously optimize operations.

And because the UNS consolidates all data into one namespace, it also unlocks cross-functional insights. Teams can correlate metrics across departmental boundaries—for instance, comparing machine uptime with production targets and financial performance. This integrated perspective enables more strategic planning, better alignment across departments, and continuous improvement initiatives grounded in data.

The importance of flexible data to UNS success

A key prerequisite for a successful UNS implementation is high adaptability. The model must be capable of easily incorporating new data sources, machines, or production lines without requiring a complete overhaul of the data architecture. This flexibility ensures that as operations evolve and scale, the UNS can grow with them—maintaining a unified and responsive data environment.

While the UNS itself does not perform functions like predictive maintenance or cost optimization, it serves as the foundational data layer that enables such advanced applications. By centralizing and contextualizing historical and real-time data on machinery, materials, and production workflows, a UNS provides the essential infrastructure for building IoT-based solutions. With this data in place, manufacturers can develop predictive maintenance strategies, detect anomalies, and optimize costs—leading to reduced downtime, better resource utilization, and smarter decision-making.

Together, these capabilities make the Unified Namespace a foundational element for smart manufacturing—bridging systems, enhancing visibility, and enabling data-driven transformation at scale.

MongoDB as the ideal central repository for a UNS model

The requirements of a UNS model map directly to MongoDB's strengths, making it an ideal choice for manufacturing environments seeking to unify their data.

Manufacturing environments always deal with highly variable and constantly evolving data structures, ranging from raw machine sensor data to structured ERP records. This diversity presents a challenge for traditional relational databases, which rely on rigid schemas that are difficult to adapt. MongoDB, with its document-oriented design, offers a more flexible solution. By storing data in JSON-like structures, it allows manufacturers to easily accommodate changes—such as adding new sensors or modifying machine attributes—without the need to redesign a fixed schema.

Another key requirement in smart manufacturing is the ability to process data in real time. With streaming data from multiple sources flowing into a UNS, manufacturers can maintain up-to-date information that supports timely interventions and data-driven decision-making. MongoDB supports real-time data ingestion through technologies like Kafka, change streams, and MQTT. This makes it simple to capture live data directly from shop floor machines into a time series collection and synchronize it with information from ERP and MES.

Live shopfloor data, ERP, and MES information in one database—combined with MongoDB’s powerful querying, aggregating and analytics capabilities—allows teams to analyze and correlate diverse data streams in one platform. For instance, production teams can cross-reference MES quality metrics with sensor data to uncover patterns that lead to improved quality control. Finance teams can blend ERP cost data with MES output to gain a more comprehensive view of operational efficiency and cost drivers.

What’s more, MongoDB’s distributed architecture supports horizontal scaling, which is crucial for large manufacturing operations where data volumes grow quickly. As more machines and production lines are brought online, MongoDB clusters can be expanded seamlessly, ensuring the UNS remains performant and responsive under increasing load.

And by serving as a central repository for historical machine sensor data, a UNS allows manufacturers to analyze long-term patterns, detect anomalies, and anticipate maintenance needs. This approach helps reduce unplanned downtime, optimize maintenance schedules, and ultimately lower operational costs. However, with a UNS acting as a centralized data hub, high availability becomes critical—since any failure could disrupt the entire data ecosystem. MongoDB addresses this with replica sets, which provide ultra-high availability and allow updates without any downtime, eliminating the risk of a single point of failure.

Proof point: Building a UNS on MongoDB in the "leafy factory"

As shown below, MongoDB’s "Leafy Factory" demo offers a hands-on example of how MongoDB serves as an ideal central repository within a UNS for manufacturing. The demo simulates a realistic industrial environment, combining data from SQL-based ERP and MES systems with real-time MQTT streams from shop floor machines. This setup showcases MongoDB’s ability to consolidate and manage diverse data types into a single, accessible, and continuously updated source of truth.

In the demo, SQL data from a simulated MES is ingested into MongoDB. This includes key production planning, monitoring, and quality metrics—all seamlessly captured using MongoDB’s flexible, document-based JSON format. This structure allows the MES data to remain both organized and accessible for real-time analysis and reporting. Similarly, SQL-based ERP data (like work orders, material tracking, and cost breakdowns) is integrated using a combination of Kafka change streams and the MongoDB Sink connector. The SQL data is captured into Kafka topics using the Debezium connector, with SQL acting as a Kafka producer. The data is then consumed, transformed, and inserted into MongoDB via the MongoDB Sink connector, creating a seamless connection between SQL, Kafka, and MongoDB. This approach keeps ERP data continuously synchronized in MongoDB, demonstrating its reliability as a live source of business-critical information.

At the same time, simulated MQTT data streams feed real-time shop floor data into the database, including machine status, quality outputs, and sensor readings like temperature and vibration. MongoDB’s support for real-time ingestion ensures that this data is immediately available, enabling up-to-date machine monitoring and faster response times.

Change streams play a central role by enabling real-time data updates across systems. For instance, when a work order is updated in the ERP system, the change is automatically reflected downstream in MES and shop floor views—illustrating MongoDB’s capability for bi-directional data flows and live synchronization within a unified data model.

Another critical capability shown in the demo is data contextualization and enrichment. As data enters the UNS, MongoDB enriches it with metadata such as machine ID, operator name, and location according to the ISA95 structure. This enriched model allows for fine-grained analysis and filtering, which is crucial for generating actionable, cross-functional insights across manufacturing, operations, and business teams.

Together, the Leafy Factory demo not only validates MongoDB’s technical strengths—like real-time processing, flexible data modeling, and scalable architecture—but also demonstrates how these capabilities come together to support a robust, dynamic, and future-ready Unified Namespace for smart manufacturing.

Conclusion

A Unified Namespace is essential for modern manufacturing, offering a single, consistent view of data that drives operational efficiency, cross-functional insights, and cost savings. MongoDB, with its flexible schema, real-time data processing, and scalability, is uniquely suited to serve as the central repository in a UNS. The Leafy Factory demo showcases MongoDB’s potential in consolidating ERP, MES, and shop floor data, illustrating how MongoDB can transform manufacturing data management, enabling real-time insights and data-driven decision-making.

In choosing MongoDB as the backbone of a UNS, manufacturers gain a powerful data infrastructure that not only meets current operational needs but also scales with future growth, creating an agile, responsive, and insight-driven manufacturing environment.

To achieve these improvements, we created an 8.0 multi-disciplinary tiger team focused on performance, and eventually expanded this team into a broad “performance army.” The work by these engineers led to new ideas on how to process simple queries and how writes are replicated. Combined with a new way of measuring performance, we also added a new way to catch the gradual performance loss over time due to many miniscule regressions.

Benchmarking at MongoDB

The MongoDB Engineering team runs a set of benchmarks internally to measure MongoDB’s performance. Industry standard benchmarks like YCSB, Linkbench, TPCC, and TPCH are run periodically on a variety of configurations and architectures, and these benchmarks are augmented by custom benchmarks based on customer workloads. By running these benchmarks in our continuous integration system, we ensure that developers do not make commits that are detrimental to performance. For instance, if any commit would regress a benchmark by more than 5% for our most important workloads, we would revert the commit. However, this threshold does not detect regressions of 0.1%, and there are thousands of commits per release (e.g., more than 9000 in MongoDB 8.0).

During the release of MongoDB 7.0, we started to take this gradual accumulation of performance loss by tiny regressions of release over release regressions seriously, so we changed the rules of the game. We decided we could not ship MongoDB 7.0 unless it at least matched MongoDB 6.0’s performance on the most important benchmarks.

We began investigating regressions and made changes to get performance back. Typically, we use tools like Intel VTune and Linux perf to find regressions across releases. With the release of MongoDB 7.0 approaching, engineers limited the scope of these fixes to reduce their risk to the release. Some proposed fixes were considered too risky. Other fixes didn’t deliver statistically significant performance improvements (Z-score > 1). Unfortunately, MongoDB lost performance with many tiny cuts at a time, and our team realized that it would take many tiny steps to improve it. We got performance back to MongoDB 6.0’s levels, but we weren't quite satisfied. We knew that what we started with MongoDB 7.0 would need to continue into MongoDB 8.0 as a first-tier concern from the start.

The MongoDB 8.0 performance push

For the release of MongoDB 8.0, we increased the priority of performance over other work and set the goal of matching MongoDB 4.4’s performance at the start. This release was chosen because it switched the default to Write Concern Majority for replica sets. This change in write concern improved MongoDB’s default durability guarantees but came with a loss in performance since the primary needs to wait for a second write on a second machine to be performed.

Before the release of MongoDB 5.0, the default write concern was w:1; when a client inserted a document, the response was returned to the client as soon as the write was journaled to the local disk. With write concern majority, the MongoDB server waits for a majority of the nodes to write the document to disk before returning a response. On the primary, MongoDB server inserts the document in the collection, journals this change to disk, sends the change to the secondary where it also journals the document to disk and then inserts the document into its collection. Applying the change immediately to collection on the secondary minimizes the latency for the secondary reads.

To start our journey to improving the performance of MongoDB 8.0, we created a multi-disciplinary tiger team of 10 people in August 2023, with myself as the leader. The team comprised two performance engineers, three staff engineers, two senior staff engineers, a senior lead, and one technical program manager. Our team of ten worked together to generate ideas, proofs of concept, and experiments.

The team’s process was different from our normal process, as we focused on idea experimentation, versus making ideas production-ready. I gave the team free reign to make any changes they thought could help, and I encouraged experimentation—the MongoDB 8.0 performance tiger team was a safe space. This spirit of experimentation was both important and successful, as it led to new ideas that delivered several big improvements (which are highlighted below). We were able to try quick hacks and measure their performance without having to worry about making our work production quality.

The big improvements

Two of the big improvements we made to MongoDB 8.0 came out of this team: simple finds and replication latency.

MongoDB supports a rich query language, but a lot of queries are simple ones to look up a document by a single _id field; the _id field always has a unique index. MongoDB optimized this with a special query plan stage called IDHACK—a query stage optimized to retrieve a single document with a minimal code path. When the tiger team looked at this code, we realized that it was spending a lot of time going through the general purpose query planning code paths before choosing the IDHACK plan.

So, a tiger team member did an experiment to bypass the entire query planner and hard code reading from the storage engine. When this delivered significant improvements to the YCSB 100% read, we knew we had a winner. While we knew it could not be committed as-is, it did serve as motivation to improve the IDHACK code path in the server in a new code path called ExpressPlan. The query team took this idea and ran with it by expanding it further for updates, deletes, and other unique index lookups.

Here are traces for MongoDB from LLVM XRay and Perfetto. The highlighted red areas show the difference between 7.0 and 8.0 for query planning for a db.coll.find({_id:1}).

The second big change was how we viewed replicating changes in a cloud database. As explained above, on secondaries, MongoDB journals the writes and then applies it to the collection before acknowledging it back to the primary.

During a team brainstorming session, a tiger team member asked, “what if we acknowledge the write as soon as it is journaled, but before we applied it to the collection in-memory?” This reduces the latency of the primary and speeds up writes in a replica set while still maintaining our durability guarantees. A second engineer ran with this idea, prototyped it quickly, and proved that it provided a significant performance boost in a week.

Now that the idea was proven to be beneficial, we handed it to the replication team to ship this work. Shipping this change took three months because we had to prove it was correct in the TLA+ models for our replication system and all corner cases before we could ship it.

Catching very small regressions

To detect small regressions, it is important to have benchmarks with no or low noise. But if the threshold is too small, this creates needless noise and creates a very noisy or flakey test that developers will learn to ignore.

Given the noisiness of various metrics such as latency and throughput, a tiger team member came up with the idea of simply counting instructions via Linux perf_event_open syscall. In this test, the code exercises the request processing code to do a simple MongoDB ping command. We run the ping command in a loop on a CI machine a few times and report the average instruction count. This test has a 0.2% tolerance and uses a hard code number. Developers can adjust the threshold up or down as needed, but this test has been a huge success as it allows us to detect regressions without spurious noise. Check out the benchmark on GitHub.

From tiger team to (tiger) army

A small tiger team can only do so much, and we didn’t want to create a situation in which one team ships features only for another team to clean up their work later. For example, the MongoDB 8.0 performance tiger team focused on a subset of benchmarks, but MongoDB’s performance is measured with dozens of benchmarks. From November 2023 to January 2024, we started implementing all the performance ideas that the tiger team implemented, but more work remained to improve performance.

This was when we built a performance “army”—we enlisted 75 people from across the 11 MongoDB server teams to work on performance. In this phase of the project, engineers were charged with idea generation, and fixing performance issues allowed us to accomplish even more than the tiger team; the larger team finished eight performance projects and 140 additional tickets as part of this work.

By bringing in additional team members, we were able to draw on ideas from a larger pool of database experts. This led to improvements in a wide variety of areas—like parsing of large $in queries, improvements to query yielding, making config.transactions a clustered collection, reworking locking in count less places, micro optimizations in authorization checks, and a change to a new TCMalloc memory allocator with lower fragmentation. Engineers also looked at improving common code such as namespace string handling, our custom code generation (we found tries helped speed up generated parsers), reducing memory usage, and choosing better data structures in some cases.

To give people the time and space they needed to succeed, we gave them dedicated weeks of time to focus on this work in lieu of adding new features. We encouraged both experimentation and for people to go with their gut feelings for small improvements that didn’t appear to move the needle on performance. Because not every experiment succeeded, it was important to encourage each other to keep experimenting and trying in the face of failure.

For example, in one failed experiment two engineers tried to use restartable sequences on Linux, but the change failed to deliver the improvements we wanted given their cost and complexity. On the other hand, custom containers and reader writer mutexes did deliver. For my part, the most impactful thing I did during this phase was to be a cheerleader and to support the team’s efforts in our performance push. Being positive and optimistic helped people push forward in their performance work even when ideas didn’t work out.

Performance improvements take a village

Overall, MongoDB 8.0 was our most successful release ever in terms of performance. Concerted, innovative work by a passionate team—and later an army—of engineers led to new ideas for performance and new ways of thinking. Performance work is neither easy nor straightforward. But by building a sense of community around our performance push, we supported each other and encouraged each other to deliver great performance improvements for MongoDB 8.0.

To read more about how MongoDB raised the bar with the release of MongoDB 8.0, check out our Chief Technology Officer Jim Scharf’s blog post. And please visit the MongoDB 8.0 page to learn more about all of its features and upgrades.

To help developer teams meet these requirements, we’re excited to announce that Service Accounts via OAuth 2.0 for MongoDB Atlas is now generally available! MongoDB Atlas Service Accounts offer a more streamlined way of authenticating API requests for applications, enabling your developers to use their preferred authentication workflow.

Addressing the challenges of using programmatic access keys

At some point in your MongoDB Atlas journey, you have likely created PAKs. These API keys enable MongoDB Atlas project owners to authenticate access for their users. API keys include a public key and a private key. These two parts serve the same function as a username and a password when you make API requests to MongoDB Atlas. Each API key belongs to only one organization, but you can grant API keys access to any number of projects in that organization.

PAKs use a method of authentication known as HTTP Digest, which is a challenge-response authentication mechanism that uses a hash function to securely transmit credentials without sending plaintext passwords over the network. MongoDB Atlas hashes the public key and the private key using a unique value called a nonce. The HTTP Digest authentication specifies that the nonce is only valid for a short amount of time. This is to prevent replay attacks so that you can’t cache a nonce and use it forever. It’s also why your API keys are a mix of random symbols, letters, and numbers and why you can only view a private key once. As a result, many teams must manage and rotate PAKs to maintain application access security. However, doing this across multiple applications can be cumbersome, especially for teams operating in complex environments.

As a result, we’ve introduced support for an alternate authentication method through Service Accounts via OAuth 2.0, which enables users to take advantage of a more automated authentication method for application development.

Using Service Accounts with an OAuth 2.0 client credentials flow

OAuth 2.0 is a standard for interapplication authentication that relies on in-flight TLS encryption to secure its communication channels. This prevents unauthorized parties from intercepting or tampering with the data. The MongoDB Atlas Administration API supports in-flight TLS encryption and uses it to enable Service Accounts as an alternative method for authenticating users.

MongoDB Atlas Service Accounts provide a form of OAuth 2.0 authentication that enables machine-to-machine communication. This enables applications, rather than users, to authenticate and access MongoDB Atlas resources. Authentication through Service Accounts follows the same access control model as PAKs, with full authentication lifecycle management. Service Accounts use the OAuth 2.0 client credentials flow, with MongoDB Atlas acting as both the identity provider and the authorization server. Like PAKs, Service Accounts are not tied to individual MongoDB Atlas users but are still ingrained with MongoDB Atlas.

Experiencing benefits through Service Accounts

Using Service Accounts to manage programmatic access offers a number of advantages:

Automation

Service Accounts offer an automated way to manage access. Users don’t need to manually manage authentication mechanisms, like recreating a Service Account to rotate the “client secrets.” Instead, they only need to regenerate the client secrets while keeping the other configuration of the existing Service Account intact. Furthermore, Service Accounts are broadly supported across many platforms, enabling easier integration between different services and tools and facilitating easier connections across applications and infrastructure components, regardless of the underlying technology.

Seamless integration with MongoDB Atlas

Service Accounts enable developers to manage authentication in the workflow of their choice. Users can manage the Service Account lifecycle at the organization and project levels via the MongoDB Atlas Administration API, the provided client library (currently, the Atlas Go SDK) , and the Atlas UI. They integrate with MongoDB Atlas via the OAuth 2.0 client credential flow, enabling seamless authentication using cloud-native identity systems.

Granular access control and role management

Service Accounts also have robust security features, providing a standardized and consistent way to manage access. Each organization or project can have its own Service Account, simplifying credential management and access control. Additionally, you can define granular roles for a Service Account to limit its access to only the necessary resources. This reduces the risk of over-permissioning and unauthorized access.

Ready to uplevel your user authentication?

Learn how to create your first Service Account by visiting our documentation. Not a MongoDB Atlas user yet? Sign up for free today.

Open finance is reshaping the financial services industry, pushing traditional institutions to modernize with a data-driven approach. Consumers increasingly expect personalized experiences, making innovation key to customer retention and satisfaction. According to a number of studies¹, there is an exponential increase of dynamic transformations in financial services, driven primarily by the impact of Banking-as-a-Service (BaaS), embedded banking services, and AI. All of these initiatives are mainly powered by API services intended for data sharing, and have become must-have technical capabilities for financial institutions.

Open finance can also unlock massive opportunities for continuous innovation. As a result, financial institutions must provision themselves with the right tools and expertise to be fully aware of the potential risks and challenges of embarking on such a “data-driven” journey.

Now, let’s dive deeper into an application of open finance with MongoDB.

MongoDB as the open finance data store

Integrating diverse financial data while ensuring its security, compliance, and scalability represents a series of considerable challenges for financial institutions. Bringing together data from a variety of backend systems entails a set of complex hurdles for financial ecosystem participants—banks, fintechs, and third-party providers (TPP).

First, they need to be able to handle structured, semi-structured, and increasingly unstructured data types. Then, cybersecurity and regulatory compliance concerns must be addressed. What’s more, an increase in data-sharing scenarios can open up potential vulnerabilities, which lead to the risk of breach exposure and cyber-attacks (and, therefore, possible legal penalties and/or eventual reputational damage).

To implement open finance strategies, organizations must first determine the role they will play: whether they act as data holders, are in charge of sharing the data with TPP, or whether they will be data users, the ones able to provide enhanced financial capabilities to end-users. Then, they must choose the most suitable technology for the data management strategy—and this is where MongoDB comes in, functioning as the operational data store.

Let’s explore how MongoDB can play a crucial role for both actors—data holders and data users—through an open finance functional prototype.

Open finance in action: Aggregated financial view for banking users

Figure 2 below shows a digital application from a fictional bank—Leafy Bank—that allows customers to aggregate all their bank accounts into a single platform.

Four actors are involved in this scenario:

a. Customer - User

b. Data Users - Leafy Bank

c. Data Holders - External Institution

d. Open Finance Data Store - MongoDB Atlas

Now let’s go through the steps from the customer experience.

Step 1. Log in to the banking application

Once logged in, the Leafy Bank digital banking application allows users to aggregate their external bank accounts. It is done behind the scenes, through a RESTFul API request that will usually interchange data in JSON format.

For the Leafy Bank prototype, we are using MongoDB and FastAPI together, exposing and consuming RESTful APIs and therefore taking advantage of MongoDB Atlas’s high performance, scalability, and flexibility.

Step 2. User authentication and authorization

A crucial step to ensure security and compliance is user consent. End-users are responsible for granting access to their financial information (authorization). In our case, Leafy Bank emulates the OAuth 2.0 authentication. It generates the corresponding tokens for securing the service communication between participants.

To achieve efficient interoperability without security issues, data holders must enable a secured technological “fence” for sharing data while preventing the operational risk of exposing core systems.

Step 3. Data exposure

After the authorization has been granted, Leafy Bank will fetch the corresponding account data from the data custodian—external banks (in our fictional scenario, Green Bank or MongoDB Bank)—via APIs. Usually, participants expose customers’ financial data (accounts, transactions, and balances) through their exposed services in JSON format to ensure compatibility and seamless data exchange.

Because MongoDB stores data in BSON, a superset of JSON, it provides a significant advantage by allowing seamless storage and retrieval of JSON-like data—making it an ideal backend for open finance.

Step 4. Data fetching

The retrieved financial data is then pushed into the open finance data store—in our case, in MongoDB Atlas—where it is centrally stored. Unlike rigid relational databases, MongoDB uses a flexible schema model, making it easy for financial institutions to aggregate diverse data structures from different sources, making it ideal for dynamic ecosystems and easy to adapt without costly migrations or downtime.

Step 5. Data retrieval

Now that the data has been aggregated in the operational data store (powered by MongoDB Atlas), Leafy Bank can leverage MongoDB Aggregation Pipelines for real-time data analysis and enrichment. To become “open finance” compliant, our Leafy Bank provides a holistic financial view and a global position accessible in a single application, thus improving individuals' experience with their finances.

Furthermore, this set of features also benefits financial institutions. They can unveil useful insights for building unique services meant to enhance customers' financial well-being.

Step 6. Bank connected!

In the end, customers can view all their finances in one place, while enabling banks to offer competitive, data-driven, tailored services.

Demo in action

Now, let’s combine these steps into a real-world demo application:

Advantages of MongoDB for open finance

Open finance presents opportunities for all the ecosystem participants. On the one hand, bank customers can benefit from tailored experiences. For personal financial management, it can provide end-users central visibility of their bank accounts. And open finance can enable extended payment initiation services, financial product comparison, enhanced insurance premium assessments, more accurate loan and credit scoring, and more.

From a technical standpoint, MongoDB can empower data holders, data users, and TPP to achieve open finance solutions. By offering a flexible schema, banks can adapt to open finance’s evolving requirements and regulatory changes while avoiding the complexity of rigid schemas, yet allowing a secure and manageable schema validation if required.

Furthermore, a scalable (vertical and horizontal) and cloud-native (multi-cloud) platform like MongoDB can simplify data sharing in JSON format, as it has been widely adopted as the data interchange “defacto” format, making it ideal for open finance applications. Internally, MongoDB uses BSON, the binary representation of JSON, for efficient storage and data traversal.

MongoDB’s rich extensions and connectors support a variety of frameworks to create RESTful API development. Besides FastAPI, there are libraries for Express.js (Node.js), Django (Python), Spring Boot (Java), and Flask (Python). The goal is to empower developers with an intuitive and easy-to-use data platform that boosts productivity and performance.

Additionally, MongoDB offers key features like its aggregation pipeline, which is designed to process data more efficiently by simplifying complex transformations, real-time analytics, and detailed queries. Sophisticated aggregation capabilities from MongoDB allow financial institutions to improve their agility while maintaining their competitive edge, all by having data as their strategic advantage.

Lastly, MongoDB provides financial institutions with critical built-in security controls, including encryption, role-based access controls (RBAC), and auditing. It seamlessly integrates with existing security protocols and compliance standards while enforcing privileged access controls and continuous monitoring to safeguard sensitive data, as detailed in the MongoDB Trust Center.

M-DAQ also offers AI-powered services like Know Your Business (KYB), onboarding, and advanced risk management tools. Amidst ever-evolving requirements, these enable business transactions across borders with ease, while staying compliant.

One of M-DAQ's most innovative solutions, CheckGPT, is an AI-powered platform designed to streamline Anti-Money Laundering (AML) compliance. It was built on MongoDB Atlas, providing a strong foundation for designing multitenant data storage. This approach ensures that each client has a dedicated database, effectively preventing any data co-mingling.

Traditional AML processes often involve tedious, time-consuming tasks, from document review, to background checks, to customer onboarding. By building CheckGPT, M-DAQ’s aim was to change this paradigm, and to leverage AI to automate (and speed) these manual processes. Today, CheckGPT allows businesses to process onboarding 30 times faster than traditional human processing.

The platform also leverages MongoDB Atlas’s native Vector Search capabilities to power intelligent semantic searches across unstructured data.

The challenge: Managing unstructured, sensitive data, and performing complex searches

One of CheckGPT’s priorities was to improve processes around collecting, summarizing, and analyzing data, while flagging potential risks to customers quickly and accurately.

Considering the vast number and complexity of data sets its AI platform had to handle, and the strict regulatory landscape the company operates in, it was crucial that M-DAQ chose a robust database.

CheckGPT needed a database that could efficiently and accurately handle unstructured data, and adapt rapidly as the data evolved. The database also had to be highly secure; to function, the AI tool would have to handle highly sensitive data, and would need to be used by companies operating in highly regulated industries.

Finally, CheckGPT was looking for the ability to perform complex, high-dimensional searches to power a wide range of complex searches and real-time information analysis.

MongoDB Atlas: A complete platform with unique features

According to M-DAQ, there are many benefits of using MongoDB Atlas’ document model:

• Flexibility: MongoDB Atlas’s document model accommodates the evolving nature of compliance data, providing the flexibility needed to manage CheckGPT's dynamic data structures, such as onboarding documents and compliance workflows.

• Security and performance: The MongoDB Atlas platform also ensures that data remains secure throughout its lifecycle. M-DAQ was able to implement a multi-tenancy architecture that securely isolates data across its diverse client base. This ensures that the platform can handle varying compliance demands while maintaining exceptional performance, giving M-DAQ’s customers the confidence that the AML processes handled by CheckGPT are compliant with stringent regulatory standards.

• Vector search capabilities: MongoDB Atlas provides a unified development experience. Particularly, MongoDB Atlas Vector Search enables real-time searches across a vast amount of high-dimensional datasets. This makes it easier to verify documents, conduct background checks, and continuously monitor customer activity, ensuring fast and accurate results during AML processes.

“AI, together with the flexibility of MongoDB, has greatly impacted CheckGPT, enabling us to scale operations and automate complex AML compliance processes,” said Andrew Marchen, General Manager, Payments and Co-founder, Wallex at M-DAQ Global. “This integration significantly reduces onboarding time, which typically took between 4-8 hours to three days depending on the document’s complexity, to less than 10 minutes. With MongoDB, M-DAQ is able to deliver faster and more accurate results while meeting customer needs in a secure and adaptable environment."

The future of CheckGPT, powered by MongoDB

M-DAQ believes that AI and data-driven technologies and tools will continue to play a central role in automating complex processes. By employing AI, M-DAQ aims to improve operational efficiency, enhance customer experiences, and scale rapidly—while maintaining high service standards.

MongoDB’s flexibility and multi-cloud support will be key as M-DAQ plans to use single/multi-cluster and multi-region capabilities in the future.

M-DAQ aims to explore additional features that could enhance CheckGPT's scalability and performance. The company, for example, plans to expand its use of MongoDB for future projects involving automating complex processes like compliance, onboarding, and risk management in 2025.

Learn more about CheckGPT on their site.

With robust support for retrieval-augmented generation (RAG) and AI agents, MongoDB enables efficient knowledge retrieval, contextual understanding, and real-time AI-driven workflows. Read on to learn more about this integration and the advantages of using MongoDB as a vector database for AI/ML applications in Go.

LangChainGo: Bringing LangChain to the Go ecosystem

LangChain is an open-source framework that simplifies building LLM-powered applications. It offers tools and abstractions to integrate LLMs with diverse data sources, APIs, and workflows, supporting use cases like chatbots, document processing, and autonomous agents. While LangChain currently supports only Python and JavaScript, the need for a similar solution in the Go ecosystem led to the development of LangChainGo.

LangChainGo is a community-driven, third-party port of the LangChain framework for the Go programming language. It allows Go developers to directly integrate LLMs into their Go applications, bringing the capabilities of the original LangChain framework into the Go ecosystem. LangChainGo enables users to embed data using various services, including OpenAI, Ollama, Mistral, and others. It also supports integration with a variety of vector stores, such as MongoDB.

MongoDB’s role as an operational and vector database

MongoDB excels as a unified data layer for AI applications with native vector search capabilities due to its simplicity, scalability, security, and rich set of features. With Atlas Vector Search built into the core database, there's no need to sync operational and vector data separately—everything stays in one place, saving time and reducing complexity when you develop AI-powered applications. You can easily combine semantic searches with metadata filters, graph lookups, aggregation pipelines, and even geo-spatial or lexical search, enabling powerful hybrid queries all within a single platform.

MongoDB’s distributed architecture allows the usage of vector search to scale independently from the core database, ensuring optimized vector query performance and workload isolation for superior scalability. Plus, with enterprise-grade security and high availability, MongoDB provides the reliability and peace of mind you need to power your AI-driven applications at scale.

MongoDB, Go, and AI/ML

As the Go AI/ML landscape grows, MongoDB continues to drive innovation with its powerful vector search capabilities and LangChainGo integration, empowering developers to build RAG implementations and AI agents. This integration is powered by the MongoDB Go Driver, which supports vector search and allows developers to interact with MongoDB directly from their Go applications, streamlining development and reducing friction.

While Python and JavaScript dominate the AI/ML ecosystem, Go’s AI/ML ecosystem is still emerging—yet its potential is undeniable. Go’s simplicity, scalability, runtime safety, concurrency, and single-binary deployment make it an ideal production-ready language for AI. With MongoDB’s powerful database and helpful learning resources, developers can seamlessly build next-generation AI solutions in Go. Ready to dive in? Explore the tutorials below to get started!

Getting Started with MongoDB and LangChainGo

MongoDB was added as a vector store in LangChainGo’s v0.1.13 release. It is packaged as mongovector, a component that enables developers to use MongoDB as a powerful vector store in LangChainGo. Usage guidance is provided through the mongovector-vectorstore-example, along with the in-depth tutorials linked below. Dive into this integration to unlock the full potential of Go AI applications with MongoDB.

We’re excited for you to work with LangChainGo. Here are some tutorials to help you get started:

• Get Started with the LangChainGo Integration

• Retrieval-Augmented Generation (RAG) with Atlas Vector Search

• Build a Local RAG Implementation with Atlas Vector Search

• Get started with Atlas Vector Search (select Go from the dropdown menu)

]]> Mon, 31 Mar 2025 14:00:00 GMT https://www.mongodb.com/blog/post/langchaingo-mongodb-powering-rag-applications-in-go https://www.mongodb.com/blog/post/langchaingo-mongodb-powering-rag-applications-in-go At MongoDB, we’re committed to fostering collaboration between academia and industry to support emerging research leaders. Now in its second year, the aim of the MongoDB PhD Fellowship Program is to advance cutting-edge research in computer science. Fellows receive financial support, mentorship, and opportunities to engage with MongoDB’s researchers and engineers throughout the year-long fellowship. They are also invited to present their research at MongoDB events.

It’s hardly groundbreaking—but nonetheless true—to say that the world runs on software. As a result, investing in the future of software development is of paramount importance. So MongoDB is excited and honored to help these students push the frontiers of knowledge in their fields, and to contribute to innovations that will redefine the future of technology.

Celebrating the 2025 MongoDB PhD Fellows

This year, the selection process was extremely competitive, and the quality of the applications was excellent. The review panel of MongoDB researchers and engineers was impressed with the applicants' accomplishments to date, as well as with their ambitious goals for future research.

Without further ado, I’m delighted to announce the recipients of the 2025 MongoDB PhD Fellowship. Congratulations to Xingjian Bai, William Zhang, and Renfei Zhou! These three exceptional scholars stood out for their innovative research and potential to drive significant advancements in their field.

Xingjian Bai, PhD candidate at MIT

Xingjian Bai is a first-year PhD student in Electrical Engineering and Computer Science at MIT, supervised by Associate Professor Kaiming He. He obtained his master's and bachelor's degrees in Mathematics and Computer Science from the University of Oxford. His research lies at the intersection of classic algorithms and deep learning, with a focus on physics-inspired generative models and learning-augmented algorithms. More broadly, he is driven by research directions that are scientifically impactful or intellectually stimulating. In his spare time, he enjoys playing tennis and jogging.

“I sincerely appreciate MongoDB’s support for Xingjian and contributions to fundamental research on artificial intelligence, deep learning, and machine learning.”
- Kaiming He, Associate Professor of the Department of Electrical Engineering and Computer Science (EECS) at MIT

William Zhang, PhD candidate at Carnegie Mellon University

William Zhang is a third-year PhD student in the Computer Science Department, School of Computer Science, at Carnegie Mellon University. His research interest focuses on "self-driving" database management systems (DBMSs), specifically focusing on machine-learning-based techniques for optimizing their performance. He is advised by Associate Professor Andy Pavlo and is a member of the Database Group (CMU-DB) and Parallel Data Lab.

"Will Zhang's PhD research at Carnegie Mellon University seeks to solve the problem all developers have struggled with since the 1970s: how to automate tuning and optimizing a database. Will is using an AI-based approach to develop database optimization algorithms that automatically learn how to exploit similarities between tuning options to reduce the complexity of database optimization. If successful his research will make it easier for anyone to deploy a database and maintain it as it grows over its lifetime. Removing the human burden of maintaining a database is especially important in the modern era of data-intensive AI applications.

The Carnegie Mellon Database Group is grateful for MongoDB's support for Will's research through their PhD Fellowship program. Working with his mentor at MongoDB as part of the program provides Will with invaluable guidance and insight into the challenges developers face with databases, especially in a cloud setting like MongoDB Atlas."
- Andy Pavlo, Associate Professor of Computer Science at CMU

Renfei Zhou, PhD candidate at Carnegie Mellon University

Renfei Zhou is a first-year PhD student studying theoretical computer science at CMU, co-advised by Assistant Professor William Kuszmaul and U.A. and Helen Whitaker Professor Guy Blelloch. He completed his bachelor’s degree in the Yao Class at Tsinghua University. He mainly works on classical data structures, especially hash tables and succinct data structures. He is also known for his work on fast matrix multiplication.

"Renfei's research focuses on answering basic questions about how space- and time-efficient data structures can be. This is a research area that has a lot of potential for impact—both on how we, as theoreticians, think about data structures, but also on how data structures are implemented in the real world. Renfei isn't just a great researcher, he's also a great collaborator, and his research will almost certainly benefit from the mentorship that he will receive from researchers and engineers at MongoDB."
- William Kuszmaul, Assistant Professor of Computer Science at CMU

Seny Kamara, Head of Research at MongoDB, shared his thoughts on the program’s second year: “The applications we received for the fellowship were outstanding, but Renfei's, Will's and Xingjian’s research stood out for their depth and ambition. Their work tackles important problems in computer science and has the potential to impact both the wider industry as well as MongoDB’s efforts. We are very excited to collaborate with these exceptional students and to support their research.”

We proudly congratulate this year’s winners and thank everyone who took the time to apply!

The nomination window for the 2026 MongoDB PhD Fellowship Program will open on September 2, and we invite all PhD students with innovative ideas to apply. For more information about the MongoDB PhD Fellowship Program, the application process, and deadlines for next year's fellowships, please visit our PhD Fellowship Program page.

Join a global community of educators and students, and access a wealth of resources, including free curriculum, specialized training, and certification pathways designed to enhance your teaching and student outcomes.

]]> Thu, 27 Mar 2025 15:00:00 GMT https://www.mongodb.com/blog/post/announcing-2025-mongodb-phd-fellowship-recipients https://www.mongodb.com/blog/post/announcing-2025-mongodb-phd-fellowship-recipients MongoDB is committed to simplifying the development of robust, data-driven applications—regardless of where the data resides. Today, we’re announcing two major updates that enhance the security, scalability, and flexibility of MongoDB Atlas across cloud providers.

Private, secure connectivity with Azure Private Link for MongoDB Atlas Data Federation, Atlas Online Archive, and Atlas SQL

Developers building on Microsoft Azure can now establish private, secure connections to MongoDB Atlas Data Federation, MongoDB Atlas Online Archive, and MongoDB Atlas SQL using Azure Private Link, enabling:

• End-to-end security: Reduce exposure to security risks by keeping sensitive data off the public internet.

• Low-latency performance: Ensure faster and more reliable access through direct, private connectivity.

• Scalability: Build applications that scale while maintaining secure, seamless data access.

Imagine a financial services company that needs to run complex risk analysis across multiple data sources, including live transactional databases and archived records. With MongoDB Atlas Data Federation and Azure Private Link, the company can securely query and aggregate this data without exposing it to the public internet, helping it achieve compliance with strict regulatory standards.

Similarly, an e-commerce company managing high volumes of customer orders and inventory updates can use MongoDB Atlas Online Archive to seamlessly move older transaction records to cost-effective storage—all while ensuring real-time analytics dashboards still have instant access to historical trends.

With Azure Private Link, these applications benefit from secure, low-latency connections, enabling developers to focus on innovation instead of on managing complex networking and security policies.

General availability of MongoDB Atlas Data Federation and Atlas Online Archive on Google Cloud

Developers working with Google Cloud can now use MongoDB Atlas Data Federation and Atlas Online Archive, which are now generally available in GA. This empowers developers to:

• Query data across sources: Run a single query across live databases, cloud storage, and data lakes without complex extract, transform, and load (ETL) pipelines.

• Optimize storage costs: Automatically move infrequently accessed data to lower-cost storage while keeping it queryable with MongoDB Atlas Online Archive.

• Achieve multi-cloud flexibility: Run applications across Amazon Web Services (AWS), Azure, and Google Cloud without being locked in.

For example, a media streaming service might store frequently accessed content metadata in a high-performance database while archiving older user activity logs in Google Cloud Storage. With MongoDB Atlas Data Federation, the streaming service can analyze both live and archived data in a single query, making it easier to surface personalized recommendations without complex ETL processes.

For a healthcare analytics platform, keeping years’ worth of patient records in a primary database can be expensive. By using MongoDB Atlas Online Archive, the platform can automatically move older records to lower-cost storage—while still enabling fast access to historical patient data for research and reporting.

These updates give developers more control over building and scaling in the cloud. Whether they need secure access on Azure or seamless querying and archiving on Google Cloud, MongoDB Atlas simplifies security, performance, and cost efficiency.

These updates are now live! Log in to your MongoDB Atlas account to start exploring the possibilities today.

]]> Thu, 27 Mar 2025 13:00:00 GMT https://www.mongodb.com/blog/post/secure-scale-your-data-with-mongodb-atlas-on-azure-and-gcp https://www.mongodb.com/blog/post/secure-scale-your-data-with-mongodb-atlas-on-azure-and-gcp In a world where information is abundant but often buried, finding precise answers can be tedious and time-consuming. People spend hours a week simply searching for the information they need. Cognistx, an applied AI startup and a member of the MongoDB for Startups program, is on a mission to eliminate this inefficiency. Through its flagship product, SQUARY AI, the company is building tools to make information retrieval faster, more reliable, and radically simpler.

As Cognistx seeks to unlock the future of intuitive search with speed, accuracy, and innovation, MongoDB Atlas serves as a reliable backbone for the company’s data operations.

A company journey: From bespoke AI projects to a market-ready solution

Cognistx started its journey with a focus on developing custom AI solutions for clients. Over time, the company identified a common pain point across industries: the need for efficient, high-quality tools to extract actionable insights from large volumes of data. This realization led it to pivot toward a product-based approach, culminating in the development of SQUARY AI—a next-generation intelligent search platform.

SQUARY AI’s first iteration was born out of a bespoke project. The goal was to build a smart search engine capable of extracting answers to open-ended questions across multiple predefined categories. Early on, the team incorporated features like source tracking to improve trustworthiness and support human-assisted reviews, ensuring that the AI’s answers could be verified and trusted. Seeing the broader potential of its technology, Cognistx began using advancements in natural language processing and machine learning, transforming its early work into a stand-alone product designed for diverse industries.

The evolution of SQUARY AI: Using state-of-the-art large language models

Cognistx initially deployed traditional machine learning approaches to power SQUARY AI’s search capabilities, such as conversation contextualization and multihop reasoning (the ability to combine information from multiple sources to form a more complete answer). Before the rise of large language models (LLMs), this was no small feat.

Today, SQUARY AI incorporates state-of-the-art LLMs to elevate both speed and precision. The platform uses a combination of retrieval-augmented generation (RAG), custom text-cleaning methods, and advanced vector search techniques. MongoDB Atlas integrates seamlessly into this ecosystem. MongoDB Atlas Vector Search powers SQUARY AI’s advanced search capabilities and lays the groundwork for even faster and more accurate information retrieval. With MongoDB Atlas, the company can store vectorized data alongside the rest of its operational data. There’s no need to add a separate, stand-alone database to handle vector search. MongoDB Atlas serves as both the operational data store and vector data store.

Cognistx offers multiple branches of SQUARY AI, including:

1. SQUARY Chat: Designed for public-facing or intranet deployment, these website chatbots provide instant, 24/7 access to website content, eliminating the need for human agents. It also empowers website owners with searchable, preprocessed AI insights from user queries. These analytics enable organizations to directly address customer needs, refine marketing strategies, and ensure that their sites contain the most relevant and valuable information for their audiences.

2. SQUARY Enterprise: Built with businesses in mind, this enterprise platform helps companies retrieve precise answers from vast and unorganized knowledge bases. Whether it’s assisting employees or streamlining review processes, this tool helps organizations save time, improve team efficiency, and deliver actionable insights.

One of the standout features of SQUARY AI is it's AI-driven metrics that assess system performance and provide insights into user interests and requirements. This is particularly valuable for public-facing website chatbots.

A powerful database: How MongoDB powers SQUARY AI

Cognistx attributes much of its technical success to MongoDB. The company’s history with MongoDB spans years, and its trust in MongoDB’s performance and reliability made the database the obvious choice for powering SQUARY AI.

“MongoDB has been pivotal in our journey,” said Cognistx Data Scientist Ihor Markevych. “The scalable, easy-to-use database has allowed us to focus on innovating and refining SQUARY AI without worrying about infrastructure constraints. With MongoDB’s support, we’ve been able to confidently scale as our product grows, ensuring both performance and reliability.”

The team’s focus when selecting a database was on cost, convenience, and development effort. MongoDB checked all those boxes, said Markevych. The company’s expertise with MongoDB, coupled with years of consistent satisfaction with its performance, made it the obvious choice. With no additional ramp-up effort necessary, the team was able to deploy very quickly.

In addition to MongoDB Atlas Vector Search, the other critical feature of MongoDB is its scalability, which Markevych described as seamless. “Its intuitive structure enables us to monitor usage patterns closely and scale up or down as needed. This flexibility ensures we’re always operating efficiently without overcommitting resources,” Markevych said.

The MongoDB for Startups program has also been instrumental in the company’s success. The program provides early-stage startups with free MongoDB Atlas credits, technical guidance, co-marketing opportunities, and access to a network of partners. With help from MongoDB technical advisors, the Cognistx team is now confidently migrating data from OpenSearch to MongoDB Atlas to achieve better performance at a reduced cost. The free MongoDB Atlas credits enabled the team to experiment with various configurations to optimize the product further. It also gained access to a large network of like-minded innovators. “The MongoDB for Startups community has provided invaluable networking opportunities, enhancing our visibility and connections within the industry,” Markevych said.

The future: Scaling for more projects

Looking ahead, Cognistx is focusing on making SQUARY AI even more accessible and customizable. Key projects include automating the onboarding process, which will enable users to define and fine-tune system behavior from the start. The company also aims to expand SQUARY AI’s availability across various marketplaces. With a successful launch on AWS Marketplace, the company next hopes to offer its product on WordPress, making it simple for businesses to integrate SQUARY Chat into their websites.

Cognistx is continuing to refine SQUARY AI’s balance between speed, accuracy, and usability. By blending cutting-edge technologies with a user-centric approach, the company is shaping the future of how people access and interact with information.

See it in action

Cognistx isn’t just building a tool; it’s building a movement toward intuitive, efficient, and conversational search. Experience the possibilities for yourself—schedule a demo of SQUARY AI today.

To get started with vector search in MongoDB, visit our MongoDB Atlas Vector Search Quick Start guide.

]]> Wed, 26 Mar 2025 15:00:00 GMT https://www.mongodb.com/blog/post/how-cognistxs-squary-ai-is-redefining-information-access https://www.mongodb.com/blog/post/how-cognistxs-squary-ai-is-redefining-information-access The term "open finance" is increasingly a topic of discussion among banks, fintechs, and other financial services providers—and for good reason. Open finance, as the next stage of open banking, expands the scope of data sharing beyond traditional banking to include investments, insurance, pension funds, and more. To deliver these enhanced capabilities, financial service providers need a versatile and flexible data store that can seamlessly manage a wide array of financial data.

MongoDB serves as an ideal solution, providing a unified data platform that empowers financial services providers to integrate various data sources, enabling real-time analytics, efficient data retrieval, and scalability. These capabilities are pivotal in enhancing customer experiences, providing users with a comprehensive view of their finances, and empowering them with greater visibility and control over their own data. By adopting MongoDB, financial services can seamlessly adapt to the growing demands of open finance and deliver innovative, data-driven solutions.

Open finance's past and future

As highlighted in a study conducted by the Cambridge Centre for Alternative Finance¹, the terms 'open banking' and 'open finance' vary globally. Acknowledging these differences, we'll focus on the model displayed in Figure 1 due to its widespread adoption and relevance in our study.

Figure 1. The three waves of innovation in financial services.

The development of open finance started with open banking, which intended for banks to promote innovation by allowing customers to share their financial data with third-party service providers (TPP) and allow those TPP—fintech and techfin companies—to initiate transactions on their behalf solely in the context of payments. This proved to be an effective way to promote innovation and thus led to a broader spectrum of financial products adding loans, mortgages, savings, pensions, insurance, investments, and more. Leading to this new directive, commonly referred to as: open finance.

If we take a step further—regardless of its final implementation—a third development called open data suggests sharing data beyond the traditional boundaries of the financial services industry (FSI), exponentially increasing the potential for financial services by moving into cross-sector offerings, positioning FSI as a horizontal industry rather than an independent vertical as it was previously known.

Who and what plays a role in open finance?

Among the different actors across open finance, the most important are:

• Consumers: End-users empowered to grant or revoke consent to share their data primarily through digital channels.

• Data holders: These are mainly financial services companies, and thereby consumer data custodians. They are responsible for controlling the data flow across the different third-party providers (TPPs).

• Data users: Data users are common third-party providers offering their services based on consumers’ data (upon request/consent).

• Connectivity providers: Trusted intermediaries that facilitate data flow, also known as TSPs in the EU and UK, and Account Aggregators in India.

• Regulatory authorities: Set standards, oversee processes, and may intervene in open finance implementation. They may vary according to the governance type.

The interactions between all these different parties define the pillars for open finance functioning:

• Technology: Ensures secure data storage and the exposure-consumption of services.

• Standards: Establishes frameworks for data interchange schemas.

• Regulations and enforceability: Encompasses security policies and data access controls.

• Participation and trust: Enables traceability and reliability within a regulated ecosystem.

Figure 2. High-level explanation of data sharing in open finance.

Drivers behind open finance: Adoption, impact, and compliance

Open finance seeks to stimulate innovation by promoting competition, safeguarding consumer privacy, and ensuring market stability—ultimately leading to economic growth. Additionally, it has the potential to provide financial institutions with greater access to data and better insights into consumers' preferences, allowing them to tailor their offerings and to enhance user experiences.

This data sharing between the ecosystem’s participants requires a regulated set of rules to ensure data protection, security, and compliance according to each jurisdiction. As seen in Figure 3 below, there are two broad drivers of open finance adoption: regulation-led and market-driven adoption. Whether organizations adopt open finance depends on factors like market dynamics, digital readiness, and regulatory environment.

Figure 3. An illustrative example of open finance ecosystem maturity.

Even though there is not one single, official legal framework specifying how to comply with open finance, countries around the world have crafted their own specific set of norms as guiding principles. Recent market research reports reveal how several countries are already implementing open finance solutions, each coming from different starting points, with their own economic goals and policy objectives.

• In Europe, the Revised Payment Services Directive (PSD2) combined with the General Data Protection Regulation (GDPR) form the cornerstone of the regulatory framework. The European Commission published a proposal in June 2023 for a regulation on a framework for Financial Data Access² (FiDA) set to go live in 2027.³

• In the UK, open finance emerged from the need to address the market power held by a few dominant banks.

• In India, open finance emerged as a solution to promote financial inclusion by enabling identity verification for accounts opening through the national ID system.

The aim is to create a single European data space – a genuine single market for data, open to data from across the world – where personal as well as non-personal data, including sensitive business data, are secure and businesses also have easy access to an almost infinite amount of high-quality industrial data, boosting growth and creating value, while minimising the human carbon and environmental footprint.⁴

Build vs. buy: Choosing the right open finance strategy

One of the biggest strategic decisions financial institutions face is whether to build their own open finance solutions in-house or buy from third-party open finance service providers. Both approaches come with trade-offs:

• Building in-house provides full ownership, flexibility, and control over security and compliance. While it requires significant investment in infrastructure, talent, and ongoing maintenance, it ensures lower total cost of ownership (TCO) in the long run, avoids vendor lock-in, and offers complete traceability—reducing reliance on external providers and eliminating “black box” risks. Institutions that build their own solutions also benefit from customization to fit specific business needs and evolving regulations.

• Buying from a provider accelerates time to market and reduces development costs while ensuring compliance with industry standards. However, it introduces potential challenges such as vendor lock-in, limited customization, and integration complexities with existing systems.

For financial institutions that prioritize long-term cost efficiency, compliance control, and adaptability, the building approach offers a strategic advantage—though it comes with its own set of challenges.

What are the challenges and why do they matter?

As open finance continues to evolve, it brings significant opportunities for innovation—but also introduces key challenges that financial institutions and fintech companies must navigate. These challenges impact efficiency, security, and compliance, ultimately influencing how quickly new financial products and services can reach the market.

1. Integration of data from various sources

Open finance relies on aggregating data from multiple institutions, each with different systems, APIs, and data formats. This complexity leads to operational inefficiencies, increased latency, and higher costs associated with data processing and infrastructure maintenance. Without seamless integration, financial services struggle to provide real-time insights and a frictionless user experience.

2. Diverse data types

Financial data comes in various formats—structured, semi-structured, and unstructured—which creates integration challenges. Many legacy systems operate with rigid schemas that don’t adapt well to evolving data needs, making it difficult to manage new financial products, regulations, and customer demands. Without flexible data structures, innovation is slowed, and interoperability between systems becomes a persistent issue.

3. Data security

With open finance, vast amounts of sensitive customer data are shared across multiple platforms, increasing the risk of breaches and cyberattacks. A single vulnerability in the ecosystem can lead to data leaks, fraud, and identity theft, eroding customer trust. Security vulnerabilities have financial consequences and can result in legal examination and long-term reputational damage.

4. Regulatory compliance

Navigating a complex and evolving regulatory landscape is a major challenge for open finance players. Compliance with data protection laws, financial regulations, and industry standards—such as GDPR or PSD2—requires constant updates to systems and processes. Failure to comply can lead to legal penalties, substantial fines, and loss of credibility—making it difficult for institutions to operate confidently in a global financial ecosystem.

These challenges directly impact the ability of financial institutions to innovate and launch new products quickly. Integration issues, security concerns, and regulatory complexities contribute to longer development cycles, operational inefficiencies, and increased costs—ultimately slowing the time to market for new financial services. In a highly competitive industry where speed and adaptability are critical, overcoming these challenges is essential for success in open finance.

MongoDB as the open finance data store

To overcome open finance’s challenges, a flexible, scalable, secure, and high-performing data store is required. MongoDB is an ideal solution, as it offers a modern, developer-friendly data platform that accelerates innovation while meeting the critical demands of financial applications.

Seamless integration with RESTful JSON APIs

According to OpenID’s 2022 research, most open finance ecosystems adopt RESTful JSON APIs as the standard for data exchange, ensuring interoperability across financial institutions, third-party providers, and regulatory bodies. MongoDB’s document-based model natively supports JSON, making it the perfect backend for open banking APIs. This enables financial institutions to ingest, store, and process API data efficiently while ensuring compatibility with existing and emerging industry standards.

Flexible data model for seamless integration

Open finance relies on diverse data types from multiple sources, each with different schemas. Traditional relational databases require rigid schema migrations, often causing downtime and disrupting high-availability services.

MongoDB's document-based model—with its flexible schema—offers an easy, intuitive, and developer-friendly solution that eliminates bottlenecks, allowing financial institutions to adapt data structures dynamically, all without costly migrations or downtime. This ensures seamless integration of structured, semi-structured, and unstructured data, increasing productivity and performance while being cost-effective, enables faster iteration, reduced complexity, and continuous scalability.

Enterprise-grade security and compliance

Security and compliance are non-negotiable requirements in open finance, where financial data must be protected against breaches and unauthorized access. MongoDB provides built-in security controls, including encryption, role-based access controls, and auditing.

It seamlessly integrates with existing security protocols and compliance standards, ensuring adherence to regulations such as GDPR and PSD2. MongoDB also enforces privileged access controls and continuous monitoring to safeguard sensitive data, as outlined in the MongoDB Trust Center.

Reliability and transactional consistency

Financial applications demand zero downtime and high availability, especially when processing transactions and real-time financial data. MongoDB’s replica sets ensure continuous availability, while its support for ACID transactions guarantees data integrity and consistency—critical for handling sensitive financial operations such as payments, lending, and regulatory reporting.

The future of open finance

The evolution of open finance is reshaping the financial industry, enabling seamless data-sharing while introducing new challenges in security, compliance, and interoperability. As financial institutions, fintechs, and regulators navigate this shift, the focus remains on balancing innovation with risk management to build a more inclusive and efficient financial ecosystem.

For organizations looking to stay ahead in this landscape, choosing the right technology stack is crucial. MongoDB provides the flexibility, scalability, and security needed to power the next generation of open finance applications—helping financial institutions accelerate innovation while ensuring compliance and data integrity.

In Part 2 of our look at open finance, we’ll explore a demo from the Industry Solutions team that leverages MongoDB to implement an open finance strategy that enhances customer experience, streamlines operations, and drives financial accessibility. Stay tuned!

Head over to our GitHub repo to view the demo.

Visit our solutions page to learn more about how MongoDB can support financial services.

---

¹ CCAF, The Global State of Open Banking and Open Finance (Cambridge: Cambridge Centre for Alternative Finance, Cambridge Judge Business School, University of Cambridge, 2024).

² “The Financial Data Access (FiDA) Regulation,” financial-data-access.com, 2024, https://www.financial-data-access.com/

³ Maout, Thierry, “What is Financial Data Access (FiDA), and how to get ready?”, July 16th, 2024, https://www.didomi.io/blog/financial-data-access-fida?315c2b35_page=2

⁴ European Commission (2020), COMMUNICATION FROM THE COMMISSION TO THE EUROPEAN PARLIAMENT, THE COUNCIL, THE EUROPEAN ECONOMIC AND SOCIAL COMMITTEE AND THE COMMITTEE OF THE REGIONS, EUR-Lex.

]]> Tue, 25 Mar 2025 14:00:00 GMT https://www.mongodb.com/blog/post/embracing-open-finance-innovation-with-mongodb https://www.mongodb.com/blog/post/embracing-open-finance-innovation-with-mongodb ¡MongoDB ama a Mexico!

The company’s second-largest market in Latin America, Mexico is also one of MongoDB’s top 20 markets globally. With rapid customer adoption across the country, we’re doubling down on our commitment to Mexico—investing in the resources and support our customers need to build and scale with MongoDB.

That’s why I’m so thrilled to announce that MongoDB Atlas, MongoDB’s modern, cloud-native database, is now available on Amazon Web Services (AWS), Google Cloud, and Microsoft Azure cloud infrastructure regions in Mexico.

MongoDB Atlas is the most widely available data platform in the world and the only true multi-cloud database on the market. With availability in over 125 cloud regions globally, customers can deploy applications on their choice of cloud, across cloud regions, or across multiple cloud providers, which provides them with the flexibility to move data seamlessly between cloud providers and to utilize the unique services of each cloud provider simultaneously.

Innovating faster with MongoDB Atlas

Until now, customers in Mexico have used MongoDB Enterprise Advanced for on-premise deployments or they’ve run Atlas deployments on cloud infrastructure regions outside of Mexico. But for customers in highly regulated industries—or for those with highly sensitive workloads who are required to keep their data in-country—modernizing their applications in the cloud with the three major cloud providers wasn’t an option.

MongoDB Atlas streamlines and secures enterprises’ data infrastructure by integrating a globally distributed database with built-in search, analytics, and AI-ready capabilities. By eliminating the need for single-purpose databases and complex data pipelines, Atlas will help organizations in Mexico modernize faster, simplify operations, and stay competitive in an AI-driven world. Bottom line: MongoDB was built for change, and its flexibility empowers businesses to innovate with next-generation technologies like AI at the breakneck speed of the market.

Now that MongoDB Atlas is available on all three major cloud providers’ local infrastructure regions, more of our customers across Mexico can begin modernizing enterprise-grade applications in the cloud with confidence.

Around the world, tens of thousands of customers are innovating faster in the cloud thanks to MongoDB Atlas. For instance, Bendigo and Adelaide Bank recently partnered with MongoDB to modernize its core banking technology with MongoDB Atlas as the keystone of an ambitious application modernization initiative. As part of the initiative, the bank reduced the development time required to migrate a core banking application off of a legacy relational database to MongoDB Atlas by up to 90% and at one-tenth the cost of a traditional legacy-to-cloud migration.

Investing in Mexico

In recent years, MongoDB has seen significant customer growth across Mexico. Today, more than 35,000 developers in Mexico list MongoDB as a skill on LinkedIn, and MongoDB has seen meaningful adoption across the financial services, retail, telecommunications, and software development sectors.

Our team has been dedicated to helping these customers modernize their applications and accelerate their digital transformation. A key driver of this success is MongoDB’s deep partnerships with AWS, Google Cloud, and Microsoft Azure. We continue to expand our integrations with services like Amazon Bedrock, Gemini Code Assist, and Azure AI Foundry that empower Mexican customers to build intelligent applications more quickly and with less friction, while our recognition as a Partner of the Year by each of these cloud providers is a testament to the impact of our collaboration and the success of our joint customers.

Since opening MongoDB’s Mexico City office in 2016, we’ve expanded rapidly, and have hired approximately 50 employees over the past three years to support rising demand and enhance every stage of the customer journey. As our presence has grown, Mexico has become MongoDB’s corporate headquarters for all Spanish-speaking countries in LATAM. Notably, it is now our second-largest market in the region, with a total addressable market exceeding that of our third, fourth, and fifth-largest LATAM markets combined.

To further support our expanding customer base, we are committed to continued investment in our local team through 2025. Mexico boasts an exceptional talent pool, and we are actively growing our organization across key areas, including sales, customer success, partners, solutions architecture, and professional services. With this expansion, we’re ensuring that businesses in Mexico have not only the most powerful tools to innovate but also the local expertise and partnerships to accelerate their success with confidence. If you’re looking for your next adventure, take a look at our open roles in Mexico.

Connect with us

This spring, MongoDB.local is coming to Mexico City! MongoDB.local is a global series of in-person events, bringing together developers, IT professionals, and technology enthusiasts to explore the latest in MongoDB. Through expert-led talks, hands-on workshops, and networking opportunities, you’ll gain valuable insights and practical skills—all in an engaging and collaborative environment.

Join us in Mexico City on May 15 to connect with the community and take your MongoDB expertise to the next level.

]]> Mon, 24 Mar 2025 15:00:00 GMT https://www.mongodb.com/blog/post/mongodb-atlas-expands-cloud-availability-to-mexico https://www.mongodb.com/blog/post/mongodb-atlas-expands-cloud-availability-to-mexico Hello and welcome! This is the first installment of a new bi-monthly blog series showcasing how companies around the world are using MongoDB to tackle mission-critical challenges. As the leading database for modern applications, MongoDB empowers thousands of organizations to harness the power of their data and to drive creativity and efficiency across industries. This series will shine a light on some of those amazing stories.

From nimble startups to large enterprises, our customers are transforming data management, analytics, and application development with MongoDB's flexible schema, scalability, and robust cloud services.

What do I mean? Picture retailers like Rent the Runway improving customer experiences with real-time analytics, fintech companies such as Koibanx speeding up and securing transaction processes, and healthcare companies like Novo Nordisk optimizing the path to regulatory approvals. With MongoDB, every developer and organization can fully tap into the potential of their most valuable resource: their data.

So please read on—and stay tuned for more in this blog series!—to learn about the ingenuity of the MongoDB customer community, and how they’re pushing the boundaries of what's possible.

Lombard Odier

Lombard Odier, a Swiss bank with a legacy dating back to 1796, transformed its application architecture with MongoDB to stay at the forefront of financial innovation. Confronted with the challenge of modernizing its systems amidst rapid digital and AI advancements, the bank leveraged MongoDB’s Application Modernization Factory and generative AI to streamline its application upgrades.

This initiative resulted in up to 60x faster migration of simple code and slashed regression testing from three days to just three hours. By transitioning over 250 applications to MongoDB, including its pivotal portfolio management system, Lombard Odier significantly reduced technical complexity and empowered its developers to focus on next-generation technologies.

SonyLIV

SonyLIV faced challenges with its over-the-top (OTT) video-streaming platform. Their legacy relational database had poor searchability, complex maintenance, and slow content updates. Critically, it lacked the scalability necessary to support 1.6 million simultaneous users. To power their new CMS— ‘Blitz’—SonyLIV selected MongoDB Atlas’s flexible document model to improve performance and lower search query latency by 98%.

Collaborating with MongoDB Professional Services, SonyLIV optimized API latency using MongoDB Atlas Search and Atlas Online Archive, effectively managing over 500,000 content items and real-time updates. With their new high-performing, modern solution in place, SonyLIV can now deliver flawless customer experiences to the world, faster.

Swisscom

Swisscom, Switzerland's leading telecom and IT service provider, harnessed MongoDB to enrich its banking sector insights with AI. Faced with the challenge of streamlining access to its extensive library of over 3,500 documents, Swisscom utilized MongoDB Atlas and MongoDB Atlas Vector Search capabilities to transform unstructured data into precise, relevant content summaries in seconds.

In just four months, Swisscom launched a production-ready platform with improved relevance, concrete answers, and transparency. The project sets a new standard in Swiss banking, and showcases Swisscom's commitment to driving the digital future with advanced AI solutions.

Victoria’s Secret

Victoria's Secret’s e-commerce platform processes thousands of transactions daily across over 2.5 billion documents on hundreds of on-premises databases. Experiencing high costs and operational constraints with its monolithic architecture, the retailer initially adopted CouchDB but faced challenges like data duplication and limited functionality.

In 2023, Victoria's Secret migrated to MongoDB Atlas on Azure, achieving zero downtime while optimizing performance and scalability. Over four months, they successfully migrated more than four terabytes of data across 200 databases, reducing CPU core usage by 75% and achieving a 240% improvement in API performance. The move to MongoDB also allowed the retailer to introduce additional products, like MongoDB Atlas Vector Search, resulting in significant operational efficiencies and cost savings.

Video spotlight

Before you go, be sure to watch one of our recent customer videos featuring the Danish pharmaceutical giant, Novo Nordisk.

Discover how Novo Nordisk leveraged MongoDB and GenAI to reduce the time it takes to produce a Clinical Study Report (CSR) from 12 weeks to 10 minutes..

Want to get inspired by your peers and discover all the ways we empower businesses to innovate for the future? Visit our Customer Success Stories hub to see why these customers, and so many more, build modern applications with MongoDB.

]]> Tue, 18 Mar 2025 14:00:00 GMT https://www.mongodb.com/blog/post/innovating-with-mongodb-customer-successes-march-2025 https://www.mongodb.com/blog/post/innovating-with-mongodb-customer-successes-march-2025 The telecommunications industry is currently undergoing a profound transformation, fueled by innovations in 5G networks, the growth of Internet of Things applications, and the rapid rise of AI. To capitalize on these technologies, companies must effectively handle increasing volumes of unstructured data, which now represents up to 90% of all information, while also developing modern applications that are flexible, high-performance, and scalable. However, the telecommunications industry's traditional reliance on relational databases such as PostgreSQL presents a challenge to modernization. Their rigid structures limit adaptability and can lead to decreased performance as table complexity grows.

With this in mind, this blog post explores how telecom companies can modernize their legacy applications by leveraging MongoDB’s modern database and its document model. With MongoDB, telecom companies can take advantage of the latest industry innovations while freeing their developers from the burdens of maintaining legacy systems.

Navigating legacy system challenges

Legacy modernization refers to the process of updating a company’s IT infrastructure to align it with the latest technologies and workflows, and ultimately advancing and securing strategic business goals. For telecom companies, this modernization involves overcoming the limitations of their legacy systems, which hinder adjustment to changing market conditions that demand greater system scalability and availability to run real-time operations.

The main drawbacks of legacy technologies like relational databases stem from their design, which wasn’t built to support the data processing capabilities required for modern telecom services. These limitations, as illustrated in Figure 1 below, include rigid data schemas, difficulty handling complex data formats, limited scaling ability, and higher operational costs for maintenance.

Figure 1. The limitations of legacy systems.

Expanding on these limitations, relational databases depend on a predefined schema, which becomes difficult to modify once established, as changes entail extensive restructuring efforts. In telecommunications, handling growing data volumes from connected devices and 5G networks can rapidly become burdensome and costly due to frequent CPU, storage, and RAM upgrades. Over time, technology lock-in can further escalate costs by hindering the transition to alternative solutions. Altogether, these factors hold back modernization efforts urging telecoms to transform their legacy systems to newer technologies.

To overcome these challenges, telecom companies are replacing these legacy systems with modern applications that effectively provide them with greater scalability, enhanced security, and high availability, as shown in Figure 2. However, achieving this transition can be a daunting task for some organizations due to the complexity of current systems, a lack of internal technical expertise, and the hurdles of avoiding downtime. Therefore, before transforming their outdated systems, telecom companies must carefully select the appropriate technologies and formulate a modernization strategy to facilitate this transition.

Figure 2. Characteristics of modern applications.

Getting onboard with MongoDB

Enter MongoDB. The company’s document-oriented database offers a flexible data model that processes any information format, easily adapting to specific application requirements. MongoDB Atlas—MongoDB’s unified, modern database—delivers a robust cloud environment that efficiently manages growing data volumes through its distributed architecture, ensuring seamless connectivity and enhanced performance.

Moreover, as telecom providers prioritize cybersecurity and innovation, MongoDB includes robust security measures—comprising encryption, authentication, authorization, and auditing—to effectively protect sensitive information and ensure regulatory compliance. Additionally, leveraging MongoDB’s document model with built-in Atlas services like Vector Search, Atlas Charts, and Stream Processing allows telecommunications organizations to streamline advanced industry cases, including single customer view, AI integrations, and real-time analytics.

Figure 3. Core MongoDB modernization features for Modernization.

Recognizing these benefits, leading telecom companies like Nokia, Swisscom, and Vodafone have successfully modernized their applications with MongoDB. However, selecting the right technology is only part of the modernization process. In order to ensure a successful and effective modernization project, organizations should establish a comprehensive modernization strategy. This process typically follows one of three following paths:

1. Data-driven modernization: this approach transfers all data from the legacy system to the new environment and then migrates applications.

2. Application-driven modernization (all-or-nothing): this approach executes all reads and writes for new applications in the new data environment from the start, but leaves the business to decide when to retire existing legacy applications.

3. Iterative modernization (one-step-at-a-time): this approach blends the previous paths, starting with the modernization of the least complex applications and incrementally moving forward into more complex applications.

Read this customer story to learn more about telecoms migrating to MongoDB.

With this overview complete, let's dive into the migration process by examining the iterative modernization of a telecom billing system.

Modernizing a telecom billing system

Telecom billing systems often consist of siloed application stacks segmented by product lines like mobile, cable, and streaming services. This segmentation leads to inefficiencies and overly complex architectures, highlighting the need to simplify these structures. With this in mind, imagine a telecom company that has decided to modernize its entire billing system to boost performance and reduce complexity.

In the initial stage, telecom developers can assess the scope of the modernization project, scoring individual applications based on technical sustainability and organizational priorities. Applications with high scores undergo further analysis to estimate the re-platforming effort required. Later on, a cross-functional team selects the first component to migrate to MongoDB, initiating the billing system modernization. This journey then follows the steps outlined in Figure 4:

Figure 4. The modernization process.

1. First, developers analyze legacy systems by examining the codebase and the underlying architecture of the chosen billing system.

2. Then, developers create end-to-end tests to ensure the application functions correctly when deployed.

3. Later, developers design an architecture that incorporates managerial expectations of the desired application.

4. Next, developers rewrite and recode the legacy application to align with the document model and develop APIs for MongoDB interaction.

5. Following this, developers conduct user tests to identify and resolve any existing application bugs.

6. Finally, developers migrate and deploy the modernized application in MongoDB, ensuring full functionality.

Throughout this process, developers can leverage MongoDB Relational Migrator to streamline the transition. Relational Migrator helps developers with data mapping and modeling, SQL object conversion, application code generation, and data migration—corresponding to steps three, four, and five. Additionally, telecom companies can accelerate modernization initiatives by leveraging MongoDB Professional Services for dedicated, tailored end-to-end migration support. Our experts work closely with you to provide customized assistance, from targeted technical support and development resources to strategic guidance throughout the entire project.

Building on this initial project, telecom companies can progressively address more complex applications, refining their approach to support a long-term modernization strategy.

Next steps

By revamping legacy applications with MongoDB, telecom companies can improve their operations and gain a competitive edge with advanced technology. This shift allows telcos to apply the latest innovations and free developers from the burdens of maintaining legacy systems. Start your journey to migrate core telecom applications to MongoDB Atlas, by visiting our telecommunications solutions page to learn more.

If you would like to discover how to upgrade your TELCO legacy systems with MongoDB, discover how to start with the following resources:

• Visit our professional services to learn more about MongoDB Consulting

• YouTube: Relational Migrator Explained in 3 minutes

• White paper: Unleash Telco Transformation with an Operational Data Layer

• White paper: Modernization: What’s Taking So Long?

]]> Mon, 17 Mar 2025 16:00:00 GMT https://www.mongodb.com/blog/post/modernizing-telecom-legacy-applications-mongodb https://www.mongodb.com/blog/post/modernizing-telecom-legacy-applications-mongodb February was big for MongoDB—and, more importantly, for anyone looking to build AI applications that deliver highly accurate, relevant information (in other words, for everyone building AI apps). MongoDB announced the acquisition of Voyage AI, a pioneer in state-of-the-art embedding and reranking models that power next-generation AI applications.

Because generative AI is by nature probabilistic, models can “hallucinate”, and generate false or misleading information. This can lead to serious risks, especially in cases or industries (e.g., financial services) where accurate information is paramount. To address this, organizations building AI apps need high-quality retrieval; they need to trust that the most relevant information is extracted from their data with precision.

Voyage AI’s advanced embedding and reranking models enable applications to extract meaning from highly specialized and domain-specific text and unstructured data. With roots at Stanford and MIT, Voyage AI’s world-class team is trusted by AI innovators like Anthropic, LangChain, Harvey, and Replit. Integrating Voyage AI’s technology with MongoDB will enable organizations to easily build trustworthy, AI-powered applications by offering highly accurate and relevant information retrieval deeply integrated with operational data.

For more, check out MongoDB CEO Dev Ittycheria’s blog post about Voyage AI, and what this means for developers and businesses (in short, delivering high-quality results at scale). Onward!

P.S. If you’re in Vegas for HumanX this week, stop by booth 412 to say hi to MongoDB!

Welcoming new AI and tech partners

The Voyage AI news was hardly the only exciting development last month. In February 2025, MongoDB welcomed three new AI and tech partners that offer product integrations with MongoDB. Read on to learn more about each great new partner!

CopilotKit

Seattle-based CopilotKit provides open source infrastructure for in-app AI copilots. CopilotKit helps organizations build production-ready copilots and agents effortlessly.

“We’re excited to be partnering with MongoDB to help companies build best-in-class copilots that leverage RAG & take action based on internal data,” said Uli Barkai, Co-Founder and Chief Marketing Officer at CopilotKit. “MongoDB made it dead simple to build a scalable vector database with operational data. This collaboration enables developers to easily ship production-grade RAG applications.”

Varonis

Varonis is the leader in data security, protecting data wherever it lives—across SaaS, IaaS, and hybrid cloud environments. Varonis’ cloud-native Data Security Platform continuously discovers and classifies critical data, removes exposures, and detects advanced threats with AI-powered automation.

“Varonis’s mission is to protect data wherever it lives,” said David Bass, Executive Vice President of Engineering and Chief Technology Officer at Varonis. “We are thrilled to further advance our mission by offering AI-powered data security and compliance for MongoDB, the database of choice for high-performance application and AI development. With this integration, joint customers can automatically discover and classify sensitive data, detect abnormal activities, secure AI data pipelines, and prevent data leaks.”

Xlrt

Xlrt is an automated insight-generation platform that enables financial institutions to create innovative financial credit products at scale by simplifying the financial spreading process.

“We are excited to partner with MongoDB Atlas to transform AI-driven financial workflows,” said Rupesh Chaudhuri, Chief Operating Officer and Co-Founder of Xlrt. “XLRT.ai leverages agentic AI, combining graph-based contextualization, vector search, and LLMs to redefine data-driven decision-making. With MongoDB's robust NoSQL and vector search capabilities, we’re delivering unparalleled efficiency, accuracy, and scalability in automating financial processes.”

To learn more about building AI-powered apps with MongoDB, check out our AI Learning Hub and stop by our Partner Ecosystem Catalog to read about our integrations with MongoDB’s ever-evolving AI partner ecosystem. And visit the MongoDB AI Applications Program (MAAP) page to learn how MongoDB and the MAAP ecosystem helps organizations build applications with advanced AI capabilities.

]]> Wed, 12 Mar 2025 14:00:00 GMT https://www.mongodb.com/blog/post/building-gen-ai-mongodb-ai-partners-february-2025 https://www.mongodb.com/blog/post/building-gen-ai-mongodb-ai-partners-february-2025 The document model has proven to be the optimal paradigm for modern application schemas. At MongoDB, we've long understood that semi-structured data formats like JSON offer superior expressiveness compared to traditional tabular and relational representations. Their flexible schema accommodates dynamic and nested data structures, naturally representing complex relationships between data entities.

However, the machine learning (ML) community has faced persistent challenges when working with semi-structured formats. Traditional ML algorithms, as implemented in popular libraries like scikit-learn and pandas, operate on the assumption of fixed-dimensional tabular data consisting of rows and columns. This fundamental mismatch forces data scientists to manually convert JSON documents into tabular form—a time-consuming process that requires significant domain expertise.

Recent advances in natural language processing (NLP) demonstrate the power of Transformers in learning from unstructured data but their application to semi-structured data has been under-studied. To bridge this gap, MongoDB's ML research group has developed a novel Transformer-based architecture designed for supervised learning on semi-structured data (e.g., JSON data in a document model database).

We call this new architecture ORiGAMi (Object Representation through Generative, Autoregressive Modelling), and we're excited to make it available to the community at github.com/mongodb-labs/origami. It includes components that make training a Transformer model feasible on datasets entailing as few as 200 labeled samples. By combining this data efficiency with the flexibility of Transformers, ORiGAMi enables prediction directly from semi-structured documents, without the cumbersome flattening and manual feature extraction required for tabular data representation. You can read more about our model on arXiv.

Technical innovation

The key insight behind ORiGAMi lies in its tokenization strategy: documents are transformed into sequences of key-value pairs and special structural tokens that encode nested types like arrays and subdocuments:

These token sequences serve as input to the Transformer model trained to predict the next token given a portion of the document, similar to how large language models (LLMs) are trained on text tokens.

What’s more, our modifications to the standard Transformer architecture include guardrails to ensure that the model only generates valid, well-formed documents, and a novel position encoding strategy that respects the order invariance of key/value pairs in JSON. These modifications also allow for much smaller models compared to LLMs, which can thus be trained on consumer hardware in minutes to hours depending on dataset size and complexity, versus days to weeks for LLMs.

By reformulating classification as a next-token prediction task, ORiGAMi can predict any field within a document, including complex types like arrays and nested subdocuments. This unified approach eliminates the need for separate models or preprocessing pipelines for different prediction tasks.

Example use case

Our initial focus has been supervised learning: training models from labeled data to make predictions on unseen documents. Let's explore a practical example of user segmentation.

Consider a collection where each document represents a user profile, containing both simple fields and complex nested structures:

{
  "_id": "user_7842",
  "email": "sarah.chen@example.com",
  "signup_date": "2024-01-15",
  "device_history": [
    {
      "device": "mobile_ios",
      "first_seen": "2024-01-15",
      "last_seen": "2024-02-11"
    },
    {
      "device": "desktop_chrome",
      "first_seen": "2024-01-16",
      "last_seen": "2024-02-10"
    }
  ],
  "subscription": {
    "plan": "pro",
    "billing_cycle": "annual",
    "features_used": ["analytics", "api_access", "team_sharing"],
    "usage_metrics": {
      "storage_gb": 45.2,
      "api_calls_per_day": 1250,
      "active_projects": 8
    }
  },
  "user_segment": "enterprise_power_user"     // <-- target field
}

Suppose you want to automatically classify users into segments like "enterprise_power_user", "smb_growth", or "early_stage_startup" based on their behavior and characteristics. Some documents in your collection already have correct labels, perhaps assigned through manual analysis or customer interviews.

Traditional ML approaches would require flattening this rich document structure, leading to very sparse tables and potentially losing important hierarchical relationships. With ORiGAMi, you can:

1. Train directly on the raw documents with existing labels

2. Preserve the full context of nested structures and arrays

3. Make predictions for the "user_segment" field on new users immediately after signup

4. Update predictions as user behavior evolves without rebuilding feature pipelines

Getting started with ORiGAMi

We're excited to be open-sourcing ORiGAMi (github.com/mongodb-labs/origami) and you can read more about our model on arXiv. We've also included a command-line interface that lets users make predictions without writing any code.

Training a model is as simple as pointing ORiGAMi to your MongoDB collection:

origami train <mongo-uri> -d app -c users

Once trained, you can generate predictions and seamlessly integrate them back into your MongoDB workflow. For example, to predict user segments for new signups (from the analytics.signups collection) and write the resulting predictions back to MongoDB to an analytics.predicted collection:

origami predict <mongo-uri> -d analytics -c signups --target 
user_segment --json | mongoimport -d analytics -c predicted

For those looking to dive deeper, we've also included several Jupyter notebooks in the repository that demonstrate advanced features and customization options. Model performance can be improved by adjusting the hyperparameters.

We're just scratching the surface of what's possible with document-native machine learning, and have many more use cases in mind. We invite you to explore the repository, contribute to the project, and share how you use ORiGAMi to solve real-world problems.

Head over to the ORiGAMi github repo, play around with it, and tell us about new ways of applying it and problems it’s well-suited to solving.

]]> Tue, 11 Mar 2025 14:00:00 GMT https://www.mongodb.com/blog/post/origami-machine-learning-architecture-for-document-model https://www.mongodb.com/blog/post/origami-machine-learning-architecture-for-document-model ZEE5 is a leading Indian over-the-top (OTT) video-streaming platform that delivers streamed content via Internet-connected devices. The platform offers a wide variety of content—movies, TV shows, web series, and original programming—across multiple genres and languages.

Owned by Zee Entertainment Enterprises Limited, ZEE5 produces over 260 hours of content daily, with a monthly active user base of more than 119.5 million users across 190 countries.

ZEE5’s operations and customer satisfaction are dependent on its backend infrastructure being robust and scalable to handle immense traffic and complex workflows. In order to future-proof its infrastructure and to maintain its competitive edge, the company needed to streamline operations and enhance its database management capabilities. This included the migration of its entire OTT platform, including a total of 100+ microservices and 80+ databases to Google Cloud.

Pramod Prakash, Senior Vice President of Engineering at ZEE5, was on the stage of MongoDB.local Bangalore in 2024. He shared insights into how ZEE5 managed this migration without hindering performance or disrupting its services.

“It was a massive project which required a very carefully orchestrated migration plan,” said Prakash.

Massive migration, zero downtime: Challenge accepted

ZEE5’s team embarked on an ambitious journey to migrate a total of 40+ microservices (out of its 100+ microservices) to MongoDB Atlas. These were previously running on the Community Edition of MongoDB and on other NoSQL databases.

One of the challenges of this migration was to ensure continuous data flow for the platform’s 119.5 million streaming users. To do so, Prakash and his team created multiple environments using a change data capture tool. This ensured continuous replication of data so the user experience would not be impacted.

“We had to build four environments: dev, QA [Quality Assurance], UAT [User Acceptance Testing], and production,” explained Prakash. “We needed to keep testing and verifying each environment, and then finally enter the production phase when we migrated the data and moved the traffic.”

The approach involved migrating production data twice: first for testing and then for the final cutover. This was to minimize any data loss. ZEE5 used MongoDB Atlas’ integrated tools mongosync and mongomirror. The tools helped achieve an essential goal: avoiding any downtime.

“We migrated this entire mammoth application with zero downtime!” said Prakash. “We have not stopped ZEE5’s operations at all.”

“The second important thing is the performance: you want to be 100% sure that the entire scale and peak traffic will work seamlessly within the new cloud environment,” added Prakash.

ZEE5 relied on MongoDB Professional Services (PS)’s support. The PS team helped architect and plan the entire migration strategy. They also accompanied Prakash’s team step by step to ensure there would be no unexpected disruptions.

The production environment was built and tested rigorously before the final migration to ensure seamless performance at peak traffic levels.

“We iterated until we were 100% sure that the new environment was ready to take up ZEE5’s peak traffic. Functionally, it was all perfect,” said Prakash.

The power of the Atlas platform

According to Prakash, the power of MongoDB Atlas lies in the fact that it offers a fully managed platform. “There is no maintenance overhead at all,” he said. “All upgrades happen automatically without any downtime. We are also leveraging auto-scaling capabilities and point-in-time recovery.”

All of this enables efficient handling of varying traffic loads without manual intervention. Additionally, data recovery capabilities are enhanced, and most importantly, the engineering team can prioritise application development rather than operational maintenance.

As of February 2025, MongoDB Atlas supports a total of seven key use cases at ZEE5: payments, subscriptions, plans and coupons, video engineering, Zee Music (users’ preferences and playlists), content metadata, and the platform’s communication engine (SMS and email notifications).

Looking ahead, ZEE5 is working on more use cases powered by MongoDB. For example, the company is looking to completely migrate their master data source for content metadata to MongoDB Atlas. ZEE5 is also considering relying on MongoDB Atlas to support and enhance its search and recommendations capabilities.

Interested in learning how MongoDB is powering other companies applications? Head over to our customer case studies hub to read the latest stories.

Visit our product page to learn more about MongoDB Atlas.

]]> Tue, 11 Mar 2025 01:00:00 GMT https://www.mongodb.com/blog/post/zee5-masterclass-in-migrating-microservices-atlas https://www.mongodb.com/blog/post/zee5-masterclass-in-migrating-microservices-atlas MongoDB has come a long way since its founding in 2007. Many people first encountered MongoDB during its early years. They formed opinions about the database based on impressions from 2012 to 2014. However, much has changed since then.

Over the past eleven years, MongoDB has made significant strides. Foremost being the launch of MongoDB Atlas in 2016. It has placed a substantial focus on improving the four critical areas that matter most to businesses and developers alike: security, durability, availability, and performance.

• Security: Protecting sensitive data from unauthorized access and ensuring regulatory compliance.

• Durability: Ensuring data remains intact and reliable, even during system failures or unexpected disruptions.

• Availability: Minimizing downtime and maintaining system operation, no matter what happens.

• Performance: Delivering fast, consistent application response times and scaling efficiently to meet growing demand.

These advancements have earned the trust of some of the world’s largest enterprises, including Toyota, Cisco, Wells Fargo, Bosch, and Verizon.

Yet despite this progress, outdated myths regarding MongoDB persist—particularly in these four foundational areas. In this blog, we will tackle those misconceptions head on and set the record straight about MongoDB’s security, durability, availability, and performance. Let’s dive in.

Myth 1: “MongoDB is not as secure as a relational database”

One of the most persistent myths about MongoDB is that it is not secure—certainly not as secure as traditional relational databases. This misconception likely stems from a series of ransomware attacks in the mid-2010s. Hackers exploited unsecured databases that lacked proper authentication and were left exposed on default TCP ports. While these incidents highlighted poor configuration practices, they have unfairly cast a shadow over MongoDB’s contemporary security capabilities.

MongoDB provides robust, intelligent security features designed to protect sensitive data at every stage of its lifecycle. MongoDB encrypts data both in transit and at rest, just like other leading NoSQL and relational databases. However, what sets MongoDB apart is its ability to keep data encrypted while in use. With Queryable Encryption, an industry-first innovation unique to MongoDB, sensitive data can remain encrypted even while it is queried. This eliminates the need to decrypt the data and reduces exposure to threats.

MongoDB also supports flexible authentication and authorization that seamlessly integrates with many identity management systems. Features like role-based access control and fine-grained permissions ensure users only have access to what they are authorized for. Concurrently, intuitive configuration makes these controls easy to implement.

Beyond encryption and access control, MongoDB includes powerful auditing tools to monitor database activity and advanced network security features, such as IP allow-listing and private networking. Together, these capabilities provide comprehensive protection against unauthorized access and help organizations meet strict compliance requirements.

Best of all, these advanced security features are included by default in both MongoDB Atlas and MongoDB Enterprise Advanced at zero cost. MongoDB’s approach simplifies security management while minimizing expenditure. This allows teams to focus on building applications with confidence that their data is protected.

Myth 2: “MongoDB’s multi-cloud capabilities do not set it apart from other databases”

At first glance, the claim that MongoDB is multi-cloud may not sound special. After all, plenty of databases are available through more than one cloud provider - however, this should not be confused with them all being multi-cloud. True multi-cloud supports ‘cross-cloud’ deployments, i.e. the ability to deploy individual nodes of a single cluster across multiple cloud providers. This distinction is often obfuscated by those vendors unable to run their clusters in such a configuration. Support for multi-cloud clusters in Atlas became generally available in October of 2020.

MongoDB Atlas enables deployment not only on Amazon Web Services (AWS), Microsoft Azure, or Google Cloud but also across all three clouds simultaneously with a single cluster. It is possible to set up and configure cross-cloud deployments solely from the Atlas management console. No further configuration is required via the individual cloud providers. This is more than just a convenience; it is a transformative capability that eliminates the boundaries between cloud providers. With MongoDB Atlas, it is as if AWS, Azure, and Google Cloud operate as one unified cloud environment.

Why does this matter?

For starters, deploying a single database cluster across multiple clouds removes the operational complexity of managing data replication and migration between providers. Seamless data mobility can be achieved. The hardest part of any application to move—the data—now becomes the easiest.

Multicloud also enables the creation of application architectures that exploit the best services from multiple cloud providers simultaneously. In addition, cross-cloud deployments deliver unmatched resiliency. With cross-cloud failover, in the event of an outage, data can be automatically switched to another cloud provider in the same geographic region. Thus ensuring uninterrupted service.

Finally, MongoDB Atlas provides the flexibility to meet regional and cloud provider preferences with ease. Atlas spans 115+ supported regions across all three major cloud providers. This makes it easy to meet customer demands or comply with local regulations using a single database.

MongoDB Atlas gives us the ability to run our database on multiple clouds through the same service. With Atlas, we have the freedom from lock-in—each client can choose where they are the most comfortable hosting their data.

Gary Hoberman, CEO and Founder - Unquork

Myth 3: “I get that MongoDB is built for horizontal scaling, but it is so painful to scale”

Horizontal scaling, also known as scale-out, is a core strength of MongoDB. It allows workloads to be distributed by adding more nodes as data and applications expand. However, some beliefs have perpetuated that scaling MongoDB is difficult and complex. The reality? MongoDB makes scaling not just possible, but seamless—whether scaling out horizontally or scaling up vertically.

With MongoDB Atlas, vertical scaling—or scale-up—is simple. By enabling auto-scaling, MongoDB Atlas dynamically adjusts cluster resources to meet workload demands. Adding more RAM, CPU, or storage capacity can be performed automatically and on-demand. This ensures optimal performance without continual manual intervention or oversight.

If you need to move beyond vertical scaling, MongoDB offers three flexible ways to scale horizontally:

• Hashed sharding: Data is distributed randomly across nodes using a hashed shard key. This ensures an even distribution of data and workloads to prevent bottlenecks.

• Ranged sharding: Data is distributed based on ranges of a specific field. This enables fine-grained control over how data is divided. This approach is especially useful for preventing hotspots in workloads.

• Zone sharding: Data is distributed geographically. This enables compliance with data residency requirements and reduces latency by keeping data closer to users.

What happens if the initial sharding strategy does not go as planned? MongoDB addresses this challenge with the ability to refine shard keys and reshard a collection with zero downtime. This ensures data distribution strategies can adapt as needs evolve, all without disrupting applications or users.

Myth 4: “Since MongoDB is built for flexibility, it must not be very performant”

One common misconception about MongoDB is that its flexibility and versatility must come at the expense of performance. After all, can such an agile database—one built for developers to model data however they want—really deliver the speed and efficiency of a performance-first solution? MongoDB is designed to provide both; unmatched flexibility and exceptional performance—all while keeping costs low.

MongoDB’s performance stems from its intelligent architecture and powerful features. Ad hoc queries, indexing, and real-time aggregations make it easy to access and analyze data quickly. How fast are queries? Primary key or indexed queries typically execute in milliseconds. Even complex queries that are not indexed remain efficient. Performance typically is dependent on factors like collection size and machine specifications.

What about workloads like search and analytics? Some developers might assume these would compete for resources and degrade performance on operational tasks. However, MongoDB solves this with workload isolation. This feature ensures that operational and nonoperational workloads are separated. This enables each to run at peak performance without requiring costly and time-consuming extract, transform, and load (ETL) processes.

Network latency? For globally distributed applications, MongoDB’s hedged reads enable the nearest replica nodes to be read from rather than waiting for a response from distant nodes. This reduces latency and ensures applications remain highly responsive.

MongoDB’s real-world performance is backed by incredible use cases:

• Amadeus processes 630 million bookings per year.

• Idealo supports 200,000 queries and 60,000 updates per second.

• Temenos achieves 150,080 transactions per second.

This was before the release of MongoDB 8.0, the most performant version of the database yet. MongoDB 8.0 has delivered:

• 36% faster reads

• 32% faster reads and updates

• 56% faster bulk inserts

• A stunning 200% improvement for time series queries

MongoDB Atlas doesn’t just solve our performance issues. It makes life easier for web developers, who can build and maintain simpler, more straightforward code.

Moutia Khatiri, CTO - Tech Accelerator, L’Oreal

MongoDB Today

MongoDB has evolved far beyond the myths perpetuated during its early years. MongoDB 8.0 delivers robust capabilities across security, durability, availability, and performance. It encrypts sensitive data throughout its lifecycle and enables seamless cross-cloud deployments. It simplifies horizontal and vertical scaling and powers some of the world’s most demanding applications. These capabilities solidify MongoDB’s position as the database of choice for modern applications.

Read about more MongoDB myths and misconceptions in our previous two posts in this series:

• Debunking MongoDB Myths: Enterprise Use Case

• Busting the Top Myths About MongoDB vs Relational Databases

Don't be held back by outdated misconceptions. Experience the innovation and performance of MongoDB. Start using MongoDB Atlas for free today. Or, to learn more about MongoDB, head over to MongoDB University and take our free Intro to MongoDB course.

]]> Mon, 10 Mar 2025 14:00:00 GMT https://www.mongodb.com/blog/post/debunking-mongodb-myths-security-scale-performance https://www.mongodb.com/blog/post/debunking-mongodb-myths-security-scale-performance Maintaining a strong security posture and ensuring compliance with regulations and industry standards are core responsibilities of enterprise security teams. However, satisfying these responsibilities is becoming increasingly complex, time-consuming, and high-stakes.

The rapid evolution of the threat landscape is a key driver of this challenge. In 2024, the percentage of organizations that experienced a data breach costing $1 million or more jumped from 27% to 36%.¹ This was partly fueled by a 180% surge from 2023 to 2024 in vulnerability exploitation by attackers.² Concurrently, regulations are tightening. Laws like the Health Insurance Portability and Accountability Act (HIPAA)³ and the U.S. Securities and Exchange Commission’s cybersecurity regulations⁴ have introduced stricter security requirements. This has raised the bar for compliance.

Thousands of enterprises rely on MongoDB Atlas to protect their sensitive data and support compliance efforts. Encryption plays a crucial role on three levels; securing data at rest, in transit, and in use. However, security teams need more than solely strong encryption. Flexibility and control are essential to align with an organization’s specific requirements.

MongoDB is introducing significant upgrades to MongoDB Atlas encryption to meet these needs. This includes enhanced customer-managed key (CMK) functionality and support for TLS 1.3. This post explores these improvements, along with the planned deprecation of outdated TLS versions, to strengthen organizations’ security postures.

Why customer-managed keys (CMKs) matter

Customer-managed keys (CMKs) are a security and data governance feature that delivers enterprises full control over the encryption keys protecting their data. With CMKs, customers can define and manage their encryption strategy. This ensures they have ultimate authority over access to their sensitive information.

MongoDB Atlas customer key management provides file-level encryption, similar to transparent data encryption (TDE) in other databases. This customer-managed encryption-at-rest feature works alongside always-on volume-level encryption⁵ in MongoDB Atlas. CMKs ensure all database files and backups are encrypted. MongoDB Atlas also integrates with AWS Key Management Service (AWS KMS), Azure Key Vault, and Google Cloud KMS. This ensures customers have the flexibility to manage keys as part of their broader enterprise security strategy.

Customers using CMKs retain complete control of their encryption keys. If an organization needs to revoke access to data due to a security concern or any other reason, it can do so immediately by freezing or destroying the encryption keys. This capability acts as a “kill switch,” ensuring sensitive information becomes inaccessible when protection is critical. Similarly, an organization can destroy the keys to render the data and backups permanently unreadable and irretrievable. This may be applicable should they choose to retire a cluster permanently.

Announcing CMK over private networking

As part of a commitment to deliver secure and flexible solutions for enterprise customers, MongoDB is introducing CMKs over private networking. This enhancement enables organizations to manage their encryption keys without exposing their key management service (KMS) to the public internet.

Using CMKs in MongoDB Atlas previously required Azure Key Vault and AWS KMS to be accessible via public IP addresses prior to today. While functional, this posed challenges for customers who need to keep KMS traffic private. It forced those customers to either expose their KMS endpoints or manage IP allow lists. By using private networking, customers can now:

• Eliminate the need for public IP exposure.
• Simplify network management by removing the need to manage allowed IP addresses. This reduces administrative effort and misconfiguration risk.
• Align with organizational requirements that mandate the use of private networking.

Customer key management over private networking is now available for Azure Key Vault and AWS KMS. Customers can enable and manage this feature for all their MongoDB Atlas projects through the MongoDB Atlas UI or the MongoDB Atlas Administration API. More enhancements are coming for MongoDB customer key management in 2025. These include secretless authentication mechanisms and CMKs for search nodes.

MongoDB Atlas TLS enhancements advance encryption in transit

Securing data in transit is equally vital as a foundation of encryption at rest with CMKs. To address this, MongoDB Atlas enforces TLS by default. This ensures encrypted communication across all aspects of the platform, including client connections. Now MongoDB is reinforcing its TLS implementation with key enhancements for enterprise-grade security.

MongoDB is in the process of rolling out fleetwide support for TLS 1.3 in MongoDB Atlas. The latest version of the cryptographic protocol offers several advantages over its predecessors. This includes stronger security defaults, faster handshakes, and reduced latency. Concurrently, TLS versions 1.0 and 1.1 are being deprecated. The rationale for this is known weaknesses and their inability to meet modern security standards. MongoDB is aligning with industry best practices by standardizing on TLS 1.2 and 1.3. This ensures a secure communication environment for all MongoDB Atlas users.

Additionally, MongoDB now offers custom cipher suite selection, giving enterprises more control over their cryptographic configurations. This feature lets organizations choose the cipher suites for their TLS connections, ensuring compliance with their security requirements.

Achieving encryption everywhere

This post covers how MongoDB secures data at rest with CMKs and in transit with TLS. However, what about data in use while it’s being processed in a MongoDB Atlas instance?

That’s where Queryable Encryption comes in. This groundbreaking feature enables customers to run expressive queries on encrypted data without ever exposing the plaintext or keys outside the client application. Sensitive data and queries never leave the client unencrypted. This ensures sensitive information is protected and inaccessible to anyone without the keys, including database administrators and MongoDB itself.

MongoDB is committed to providing enterprise-grade security that evolves with the changing threat and regulatory landscapes. Organizations now have greater control, flexibility, and protection across every stage of the data lifecycle with enhanced CMK functionality, TLS 1.3 adoption, and custom cipher suite selection.

As security challenges grow more complex, MongoDB continues to innovate to enable enterprises to safeguard their most sensitive data. To learn more about these encryption enhancements and how they can strengthen your security posture, visit MongoDB Data Encryption.

---

¹ PwC, October 2024

² Verizon Data Breach Investigations Report, 2024

³ U.S. Department of Health and Human Services, December 2024

⁴ U.S. Securities and Exchange Commission, 2023

⁵ MongoDB Atlas Security White Paper, Encryption at Rest section page 12

]]> Wed, 05 Mar 2025 15:00:00 GMT https://www.mongodb.com/blog/post/advancing-encryption-in-mongodb-atlas https://www.mongodb.com/blog/post/advancing-encryption-in-mongodb-atlas MongoDB is pleased to introduce its integration with LangChain4j, a popular framework for integrating large language models (LLMs) into Java applications. This collaboration simplifies the integration of MongoDB Atlas Vector Search into Java applications for building AI applications.

The advent of generative AI has opened up many new possibilities for developing novel applications. These advancements have led to the development of AI frameworks that simplify the complexities of orchestrating and integrating LLMs and the various components of the AI stack, where MongoDB plays a key role as an operational and vector database.

Simplifying AI development for Java

The first AI frameworks to emerge were developed for Python and JavaScript, which were favored by early AI developers. However, Java remains widespread in enterprise software. This has led to the development of LangChain4j to address the needs of the Java ecosystem. While largely inspired by LangChain and other popular AI frameworks, LangChain4j is independently developed.

As with other LLM frameworks, LangChain4j offers several advantages for developing AI systems and applications by providing:

• A unified API for integrating LLM providers and vector stores. This enables developers to adopt a modular approach with an interchangeable stack while ensuring a consistent developer experience.

• Common abstractions for LLM-powered applications, such as prompt templating, chat memory management, and function calling, offering ready-to-use building blocks for common AI applications like retrieval-augmented generation (RAG) and agents.

Powering RAG and agentic systems with MongoDB and LangChain4j

MongoDB worked with the LangChain4j open-source community to integrate MongoDB Atlas Vector Search into the framework, enabling Java developers to develop AI-powered applications from simple RAG to agentic applications.

In practice, this means developers can now use the unified LangChain4j API to store vector embeddings in MongoDB Atlas and use Atlas Vector Search capabilities for retrieving relevant context data. These capabilities are essential for enabling RAG pipelines, where private, often enterprise data is retrieved based on relevancy and combined with the original prompt to get more accurate results in LLM-based applications.

LangChain4j supports various levels of RAG, from basic to advanced implementations, making it easy to prototype and experiment before customizing and scaling your solution to your needs.

A basic RAG setup with LangChain4j typically involves loading and parsing unstructured data from documents stored locally or on remote services like Amazon S3 or Azure Storage using the Document API. The process then transforms and splits the data, then embeds it to capture the semantic meaning of the content. For more details, check out the documentation on core RAG APIs.

However, real-world use cases often demand solutions with advanced RAG and agentic systems. LangChain4j optimizes RAG pipelines with predefined components designed to enhance accuracy, latency, and overall efficiency through techniques like query transformation, routing, content aggregation, and reranking. It also supports AI agent implementation through dedicated APIs, such as AI Services and Tools, with function calling and RAG integration, among others. Learn more about the MongoDB Atlas Vector Search integration in LangChain4j’s documentation.

MongoDB’s dedication to providing the best developer experience for building AI applications across different ecosystems remains strong, and this integration reinforces that commitment. We will continue strengthening our integration with LLM frameworks enabling developers to build more-innovative AI applications, agentic systems, and AI agents.

Ready to start building AI applications with Java? Learn how to create your first RAG system by visiting our tutorial: How to Make a RAG Application With LangChain4j.

]]> Tue, 04 Mar 2025 18:30:53 GMT https://www.mongodb.com/blog/post/ai-powered-java-applications-with-mongodb-langchain4j https://www.mongodb.com/blog/post/ai-powered-java-applications-with-mongodb-langchain4j Key Takeaways

• We achieve real-world testing by adopting release candidates (RCs) on our internal production systems before finalizing a release.

• Our diverse internal workloads delivered unique insights. For instance, an internal cluster’s upgrade identified a rare MongoDB server crash and an inefficiency for a specific query shape introduced by a new MongoDB 8.0 feature.

• Issues encountered while testing MongoDB 8.0 internally were fixed proactively before they went out to customers. For example, during an upgrade to an 8.0 RC, one of our internal databases crashed and the issue was fixed in the next RC.

• Prerelease testing uncovered gaps in our automated testing, leading to improved coverage with additional tests.

• Using MongoDB 8.0 internally on mission-critical internal systems demonstrated its reliability. This gave customers confidence that the release could handle their demanding workloads, just as it did for our own engineering teams.

Release jitters

Every software release, whether it’s a new product or an update of an existing one, comes with an inherent risk: what if users encounter a bug that the development team didn’t anticipate? With a mission-critical product like MongoDB 8.0, even minor issues can have a significant impact on customer operations, uptime, and business continuity.

Unfortunately, no amount of automated testing can guarantee how MongoDB will perform when it lands with customers. So how does MongoDB proactively identify and resolve issues in our software before customers encounter them, thereby ensuring a seamless upgrade experience and maintaining customer trust?

Catching issues before you do

To address these challenges, we employ a combination of methods to ensure reliability. One approach is to formally model our system to prove the design is correct, such as the effort we undertook to mathematically model our protocols with lightweight formal methods like TLA+. Another method is to prove reliability empirically by dogfooding.

Dogfooding (🤨)?

Eating your own dog food—aka eating your own pizza, aka “dogfooding”—refers to a development process where you put yourself in customers’ shoes by using your own product in your own production systems. In short: you’re your own customer.

Why dogfood?

• Enhanced product quality: Testing in a controlled environment can’t replicate the edge cases of true-to-life workloads, so real-world scenarios are needed to ensure robustness, reliability, and performance under diverse conditions.

• Early identification of issues: Testing internally surfaces issues earlier in the release process, enabling fixes to be deployed proactively before customers encounter them.

• Build customer empathy: Acting as users provides direct insight into customer pain points and needs. Engineers gain firsthand understanding of the challenges of using their product, informing more customer-centric solutions. Without dogfooding, things like upgrades are taken for granted and customer pain points can be overlooked.

• Boost credibility and trust: Relying on our own software to power critical internal systems reassures customers of its dependability.

Dogfooding at MongoDB

MongoDB has a strong dogfooding culture. Many internal services are built with MongoDB and hosted on MongoDB Atlas, the very same setup we provide our customers. Eating our own dog food is essential to our customer mindset. Because internal teams work alongside MongoDB engineers, acting as users bridges the gap between MongoDB engineers and their customers. Additionally, real-life workloads vet our software and processes in a way automated testing cannot.

Release dogfooding

With the release of MongoDB 8.0, the company decided to take dogfooding one step further. Driven by a company-wide focus on making 8.0 the most performant version of MongoDB yet, we embarked on an ambitious plan to dogfood the release candidates within our own infrastructure.

Before, our release process looked like this:

Figure 1. Releases without real-world testing.

We wanted it to look more like this:

Figure 2. Releases pregamed on internal clusters.

Adding internal testing to the release process allows us to iterate long before we make the product available to customers. Whereas in the past we’d release and fix issues reactively as customers encountered them, using the release internally, before it got into customers’ hands, would uncover edge cases so we could fix them proactively. By acting as our own customers, we remove our real customers from the development cycle and build confidence in the release.

The confidence team

To tackle upgrades effectively, we assembled a cross-functional team of MongoDB engineers, Atlas SREs, and internal service developers. A technical program manager (TPM) was assigned to the effort to track progress and coordinate efforts across the team. Together, we enumerated the databases, scheduled upgrade dates, and assigned directly responsible individuals (DRIs) to each upgrade.

To streamline communication, we created an internal Slack channel and invited everyone on the team to it. We agreed on a playbook: with the support of the team, the assigned DRI would upgrade their cluster and monitor for any issues. If something came up we would create a ticket in an internal Jira project and mention it in Slack for visibility. I took on the role of DRI for Evergreen database upgrades.

Evergreen

My team maintains the database clusters for Evergreen, MongoDB’s bespoke continuous integration (CI) system. Evergreen is responsible for running automated tests at scale against MongoDB, Atlas, the drivers, Evergreen itself, and many other products. At last count, each day Evergreen executes, in parallel, roughly ten years of tests per day and is on the critical path for many teams at the company. Evergreen runs on two separate clusters in Atlas: the application’s main replica set and a smaller one for our background job coordinator, Amboy. In terms of scale, the main replica set contains around 9.5TB of data and handles 1 billion CRUD operations per day, while the Amboy cluster contains about 1TB of data and handles 100 million CRUD operations per day.

Because of Evergreen’s criticality to the development cycle, historically we’ve taken a cautious approach to any operational changes and database upgrades were not a priority. The initiative to dogfood our internal clusters changed our approach—we were going to use 8.0 before it went out to customers. Enabling a feature flag in Atlas made the RC build available in our Atlas project before it was available to customers.

A showstopper

Our first target was the Amboy cluster, which handles background jobs for Evergreen. I clicked the button to upgrade our Amboy cluster and we held our collective breath. Atlas upgrades are rolling. This means an upgrade is applied iteratively to each secondary in the cluster until finally the primary is stepped down and upgraded. Usually this works well since any issues will at most affect just a secondary, but in our case it didn’t work out. The secondaries’ upgrades succeeded, but when the primary was stepped down, each node that won the election to be the next primary crashed. The result was that our cluster had no primary and the Amboy database was unavailable, which threw a monkey-wrench in our application.

We sounded the alarm and an investigation commenced ASAP. Stack traces, logs, and diagnostics were captured and the cluster was downgraded to 7.0. As it turned out, we’d hit an edge case that was triggered by a malformed TTL index specification with a combination of two irregularities:

1. Its expireAfterSeconds was not an integer.

2. It contained a weights field, which is not valid in an index that’s not a text index.

Both irregularities were previously allowed, but became invalid due to strengthened validation checks. When a node steps up to primary, it corrects these malformed index specifications, but in that 8.0 RC if there were two things wrong with an index it would go down an execution path that ended in a segfault. This bug only occurs when a node steps up to primary, which is why it brought down our cluster despite the rolling upgrade. SERVER-94487 was opened to fix the bug and the fix was rolled into the next RC. When the RC was ready, we upgraded the Amboy database again and the upgrade succeeded.

Not a showstopper

Next up was the main database cluster for the Evergreen application. We performed the upgrade, and at first all indications were that the upgrade was a success. However, on further inspection a discontinuous jump had appeared in two of the Atlas monitoring graphs. Before the upgrade our Query Executor graph usually looked like this:

Figure 3. Query Executor graph before the upgrade.

Whereas after the upgrade it looked like this:

Figure 4. Query Executor graph after the upgrade.

This represented roughly a 5x increase in the rate per second of index keys and documents scanned by queries and query plans.

Similarly, the Query Targeting graph looked like this before the upgrade:

Figure 5. Query Targeting graph before the upgrade.

Whereas after the upgrade it looked like this:

Figure 6. Query Targeting graph after the upgrade.

This also represented roughly a 5x increase to the ratio of scanned index keys and documents to the number of documents returned.

Both these graphs indicated there was at least one query that wasn’t using indexes as well as it had been before the upgrade. We got eyes on the cluster and it was determined that a bug in index pruning (a new feature introduced in 8.0) was causing the query planner to remove the most efficient index for a contained $or query shape. This is when a query contains an $or branch that isn’t the root of the query predicate, such as A and (C or B). For the 8.0 release this was listed as a known issue and disabled in Atlas, and index pruning was disabled entirely by the 8.0.1 release until we can fix the underlying issue in SERVER-94741.

Other clusters

Other teams’ clusters followed suit, but their upgrades went off without a hitch. It’s to be expected that the particulars of each dataset and workload would trigger various edge cases. Evergreen’s clusters hit some while the rest did not. This brings out an important lesson: testing against a variegated set of live workloads raises the likelihood we’ll encounter and address all the issues our customers would have encountered.

Continuous improvement

Although we caught these issues before they reached customers, our shift-left mindset motivates us to catch them earlier in the process through automated testing. As part of this effort, we plan to add additional tests focused on upgrades from older versions of the database. The index pruning issue, in particular, was part of the inspiration for us to investigate property based testing–an approach that has already uncovered several new bugs (SERVER-89308). SERVER-92232 will introduce a property based test specifically for index pruning.

What’s next?

All told, the exercise was a success. The 8.0 upgrade reduced Evergreen’s operation execution times by an order of magnitude:

Figure 7. Drastically faster database operations after the upgrade.

For customers, dogfooding uncovered novel issues and gave us the chance to fix them before they could disrupt customer workloads. By the time we cut the release we were confident we were providing our customers a seamless upgrade.

Through the dogfooding process we discovered additional internal teams with services built on MongoDB. And now we’re further leaning in on dogfooding by building out a formal framework that will include those teams and their clusters. For the next release, this will uncover even more insights and provide greater confidence.

Looking ahead, as our CTO aptly put it, "all customers demand security, durability, availability, and performance" from their technology. Our commitment to eating our own dogfood directly strengthens these very pillars. It's a commitment to our customers, a commitment to innovation, and a commitment to making MongoDB the best database in the world.

Join our MongoDB Community to learn about upcoming events, hear stories from MongoDB users, and connect with community members from around the world.

]]> Mon, 03 Mar 2025 15:00:00 GMT https://www.mongodb.com/blog/post/mongodb-8-0-eating-our-own-dog-food https://www.mongodb.com/blog/post/mongodb-8-0-eating-our-own-dog-food On March 26, 2025, MongoDB will start rolling out mandatory multi-factor authentication (MFA) for MongoDB Atlas users. While MFA has long been supported in Atlas, it was previously optional. MongoDB is committed to delivering customers the highest level of security, and the introduction of mandatory MFA adds an extra layer of protection against unauthorized access to MongoDB Atlas.

Note: MFA will require users to provide a second form of authentication, such as a one-time passcode or biometrics. To ensure a smooth transition, users are encouraged to set up their preferred MFA method in advance. This process should take around three minutes to set up. If MFA is not configured by March 26, 2025, users will need to enter a one-time password (OTP) sent to their registered email each time they log in.

Why are we making MFA mandatory?

Stealing users’ credentials is a key tactic in the modern cyberattack playbook. According to a Verizon report, stolen credentials have been involved in 31% of data breaches in the past decade, and credential stuffing is the most common attack type for web applications.¹ Credential stuffing is when attackers use stolen credentials obtained from a data breach on one service to attempt to log in to another service. These breaches are particularly harmful, taking an average of 292 days to detect and contain.² This rise in cyber threats has rendered password-only security inadequate.

Organizations of all sizes trust MongoDB Atlas to safeguard their mission-critical applications and sensitive data. These range from global enterprises to individual developers. Therefore, to strengthen account security and to reduce the risk of unauthorized access, MongoDB is introducing mandatory MFA.

The impact of MFA

A large-scale study by Microsoft measured the effectiveness of MFA to prevent cyberattacks on enterprise accounts. The findings indicated enabling MFA reduces the risk of account compromise by 99.22%. For accounts with previously leaked credentials, MFA still lowered the risk by 98.56%. This makes MFA one of the most effective defenses against unauthorized access.

By default, requiring MFA strengthens the security of all MongoDB Atlas accounts. By reducing the risk of compromised accounts being used in broader attacks, this proactive step protects individual users and enhances MongoDB Atlas’s overall security. Ensuring strong authentication practices across the Atlas ecosystem maintains the integrity of mission-critical applications and sensitive data— and a safer experience for everyone is the result.

Preparing for mandatory MFA

MFA will be a prerequisite for all users when logging into MongoDB services using Atlas credentials. These services include:

• MongoDB Atlas user interface
• MongoDB Support portal
• MongoDB University
• MongoDB Forums

Atlas supports the following MFA methods:

• Security key or biometrics: FIDO2 (WebAuthn) compliant security keys (e.g., YubiKey) or biometric authentication (e.g., Apple Touch ID or Windows Hello)
• One-time password (OTP) and push notifications: Provided through the Okta Verify app
• Authenticator apps: Such as Twilio Authy, Google Authenticator, or Microsoft Authenticator for generating time-based OTPs
• Email: For generating OTPs

MongoDB encourages users to choose phishing-resistant MFA methods, such as security keys or biometrics.

Strengthening security with mandatory MFA

Requiring MFA is a significant step that enhances MongoDB Atlas’s default security. Multi-factor authentication protects users from credential-based attacks and unauthorized access. Making MFA’s additional layer of authentication mandatory ensures greater account security. This safeguards mission-critical applications and data.

To ensure a smooth transition, users are encouraged to set up their preferred MFA method before March 26, 2025. For detailed setup instructions, refer to the MongoDB documentation. And, please visit the MongoDB security webpage and Trust Center to learn more about MongoDB’s commitment to security.

]]> Fri, 28 Feb 2025 15:00:00 GMT https://www.mongodb.com/blog/post/secure-by-default-mandatory-mfa-in-mongodb-atlas https://www.mongodb.com/blog/post/secure-by-default-mandatory-mfa-in-mongodb-atlas Key takeaways

1. As vector embeddings scale into millions, memory usage and query latency surge, leading to inflated costs and poor user experience.
2. By storing embeddings in reduced-precision formats (int8 or binary), you can dramatically cut memory requirements and speed up retrieval.
3. Voyage AI's quantization-aware embedding models are specifically tuned to handle compressed vectors without significant loss of accuracy.
4. MongoDB Atlas streamlines the workflow by handling the creation, storage, and indexing of compressed vectors, enabling easier scaling and management.
5. MongoDB is built for change, allowing users to effortlessly scale AI workloads as resource demands evolve.

Organizations are now scaling AI applications from proofs of concept to production systems serving millions of users. This shift creates scalability, latency, and resource challenges for mission-critical applications leveraging recommendation engines, semantic search, and retrieval-augmented generation (RAG) systems.

At scale, minor inefficiencies compound and become major bottlenecks, increasing latency, memory usage, and infrastructure costs. This guide explains how vector quantization enables high-performance, cost-effective AI applications at scale.

The challenge: Scaling vector search in production

Let’s start by considering a modern voice assistance platform that combines semantic search with natural language understanding. During development, the system only needs to process a few hundred queries per day, converting speech to text and matching the resulting embeddings against a modest database of responses.

The initial implementation is straightforward: each query generates a 32-bit floating-point embedding vector that's matched against a database of similar vectors using cosine similarity. This approach works smoothly in the prototype phase—response times are quick, memory usage is manageable, and the development team can focus on improving accuracy and adding features.

However, as the platform gains traction and scales to processing thousands of queries per second against millions of document embeddings, the simple approach begins to break down.

Each incoming query now requires loading massive amounts of high-precision floating-point vectors into memory, computing similarity scores across an exponentially larger dataset, and maintaining increasingly complex vector indexes for efficient retrieval.

Without proper optimization, the system struggles as memory usage balloons, query latency increases, and infrastructure costs spiral upward. What started as a responsive, efficient prototype has become a bottleneck production system that struggles to maintain its performance requirements while serving a growing user base.

The key challenges are:

• Loading high-precision 32-bit floating-point vectors into memory
• Computing similarity scores across massive embedding collections
• Maintaining large vector indexes for efficient retrieval

Which can lead to critical issues like:

• High memory usage as vector databases struggle to keep float32 embeddings in RAM
• Increased latency as systems process large volumes of high-precision data
• Growing infrastructure costs as organizations scale their vector operations
• Reduced query throughput due to computational overhead

AI workloads with tens or hundreds of millions of high-dimensional vectors (e.g., 80M+ documents at 1536 dimensions) face soaring RAM and CPU requirements. Storing float32 embeddings for these workloads can become prohibitively expensive.

Vector quantization: A path to efficient scaling

The obvious question is: How can you maintain the accuracy of your recommendations, semantic matches, and search queries, while drastically cutting down on compute and memory usage and reducing retrieval latency?

Vector quantization is how.

It helps you store embeddings more compactly, reduce retrieval times, and keep costs under control. Vector quantization offers a powerful solution to scalability, latency, and resource utilization challenges by compressing high-dimensional embeddings into compact representations while preserving their essential characteristics. This technique can dramatically reduce memory requirements and accelerate similarity computations without compromising retrieval accuracy.

What is vector quantization?

Vector quantization is a compression technique widely applied in digital signal processing and machine learning. Its core idea is to represent numerical data using fewer bits, reducing storage requirements without entirely sacrificing the data’s informative value.

In the context of AI workloads, quantization commonly involves converting embeddings—originally stored as 32-bit floating-point values—into formats like 8-bit integers. By doing so, you can substantially decrease memory and storage consumption while maintaining a level of precision suitable for similarity search tasks.

An important point to note is that the quantization mechanism is especially suitable for use cases that involve over 1 million vector embeddings, such as RAG applications, semantic search, or recommendation systems that require tight control of operational costs without a compromise on retrieval accuracy. Smaller datasets with fewer than 1 million embeddings might not see significant gains from quantization procedures. For smaller datasets, the overhead of implementing quantization might outweigh its benefits.

Understanding vector quantization

Vector quantization operates by mapping high-dimensional vectors to a discrete set of prototype vectors or converting them to lower-precision formats. There are three main approaches:

1. Scalar quantization: Converts individual 32-bit floating-point values to 8-bit integers, reducing memory usage of vector values by 75% while maintaining reasonable precision.
2. Product quantization: Compresses entire vectors at once by mapping them to a codebook of representative vectors, offering better compression than scalar quantization at the cost of more complex encoding/decoding.
3. Binary quantization: Transforms vectors into binary (0/1) representations, achieving maximum compression but with more significant information loss.

A vector database that applies these compression techniques must effectively manage multiple data structures:

1. Hierarchical navigable small world (HNSW) graph for navigable search
2. Full-fidelity vectors (32-bit float embeddings)
3. Quantized vectors (int8 or binary)

When quantization is defined in the vector index, the system builds quantized vectors and constructs the HNSW graph from these compressed vectors. Both structures are placed in memory for efficient search operations, significantly reducing the RAM footprint compared to storing full-fidelity vectors alone.

The table below illustrates how different quantization mechanisms impact memory usage and disk consumption. This example focuses on HNSW indexes storing 30 GB of original float32 embeddings alongside a 0.1 GB HNSW graph structure. Our RAM usage estimates include a 10% overhead factor (1.1 multiplier) to account for JVM memory requirements with indexes loaded into page cache, reflecting typical production deployment conditions. Actual overhead may vary based on specific configurations.

Here are key attributes to consider based on the table below:

• Estimated RAM usage: Combines HNSW graph size with either full or quantized vectors, plus a small overhead factor (1.1 for index overhead).
• Disk usage: Includes storage for full-fidelity vectors, HNSW graph, and quantized vectors when applicable.

Notice that while enabling quantization increases total disk usage—because you still store full-fidelity vectors for exact nearest neighbor queries in both cases and rescoring in the case of binary quantization—it dramatically decreases RAM requirements and speeds up initial retrieval.

MongoDB Atlas Vector Search offers powerful scaling capabilities through its automatic quantization system.

As illustrated in Figure 1 below, MongoDB Atlas supports multiple vector search indexes with varying precision levels: Float32 for maximum accuracy, Scalar Quantized (int8) for balanced performance with 3.75× RAM reduction, and Binary Quantized (1-bit) for maximum speed with 24× RAM reduction.

The quantization variety provided by MongoDB Atlas allows users to optimize their vector search workloads based on specific requirements. For collections exceeding 1M vectors, Atlas automatically applies the appropriate quantization mechanism, with binary quantization particularly effective when combined with Float32 rescoring for final refinement.

Figure 1: MongoDB Atlas Vector Search Architecture with Automatic Quantization

Binary quantization with rescoring

A particularly effective strategy is to combine binary quantization with a rescoring step using full-fidelity vectors. This approach offers the best of both worlds: extremely fast lookups thanks to binary data formats, plus more precise final rankings from higher-fidelity embeddings.

• Initial retrieval (Binary)
• Embeddings are stored as binary to minimize memory usage and accelerate the approximate nearest neighbor (ANN) search.
• Hamming distance (via XOR + population count) is used, which is computationally faster than Euclidean or cosine similarity on floats.
• Rescoring
• The top candidate results from the binary pass are re-evaluated using their float or int8 vectors to refine the ranking.
• This step mitigates the loss of detail in binary vectors, balancing result accuracy with the speed of the initial retrieval.

By pairing binary vectors for rapid recall with full-fidelity embeddings for final refinement, you can keep your system highly performant and maintain strong relevance.

The need for quantization-aware models

Not all embedding models perform equally well under quantization. Models need to be specifically trained with quantization in mind to maintain their effectiveness when compressed. Some models—especially those trained purely for high-precision scenarios—suffer significant accuracy drops when their embeddings are represented with fewer bits.

Quantization-aware training (QAT) involves:

• Simulating quantization effects during the training process

• Adjusting model weights to minimize information loss

• Ensuring robust performance across different precision levels

This is particularly important for production applications where maintaining high accuracy is crucial. Embedding models like those from Voyage AI—which recently joined MongoDB—are specifically designed with quantization awareness, making them more suitable for scaled deployments.

These models preserve more of their essential feature information even under aggressive compression. Voyage AI provides a suite of embedding models specifically designed with QAT in mind, ensuring minimal loss in semantic quality when shifting to 8-bit integer or even binary representations.

Figure 2: Embedding model performance comparing retrieval quality (NDCG@10) versus storage costs.

The graph above shows several important patterns that demonstrate why quantization-aware training (QAT) is crucial for maintaining performance under aggressive compression.

The Voyage AI family of models (shown in green) demonstrates strong performance in retrieval quality even under extreme compression. The voyage-3-large model demonstrates this dramatically—when using int8 precision at 1024 dimensions, it performs nearly identically to its float precision, 2048-dimensional counterpart, showing only a minimal 0.31% quality reduction despite using 8 times less storage. This showcases how models specifically designed with quantization in mind can preserve their semantic understanding even under substantial compression.

Even more impressive is how QAT models maintain their edge over larger, uncompressed models. The voyage-3-large model with int8 precision and 1024 dimensions outperforms OpenAI-v3-large (using float precision and 3072 dimensions) by 9.44% while requiring 12 times less storage. This performance gap highlights that raw model size and dimension count aren't the decisive factors —it's the intelligent design for quantization that matters.

The cost implications become truly striking when we examine binary quantization. Using voyage-3-large with 512-dimensional binary embeddings, we still achieve better retrieval quality than OpenAI-v3-large with its full 3072-dimensional float embeddings while using 200 times less storage. To put this in practical terms: what would have cost $20,000 in monthly storage can be reduced to just $100 while actually improving performance.

In contrast, models not specifically trained for quantization, such as OpenAI's v3-small (shown in gray), show a more dramatic drop in retrieval quality as compression increases. While these models perform well in their full floating-point representation (at 1x storage cost), their effectiveness deteriorates more sharply when quantized, especially with binary quantization.

For production applications where both accuracy and efficiency are crucial, choosing a model that has undergone quantization-aware training can make the difference between a system that degrades under compression and one that maintains its effectiveness while dramatically reducing resource requirements.

Read more on the Voyage AI blog.

Impact: Memory, retrieval latency, and cost

Vector quantization addresses the three core challenges of large-scale AI workloads—memory, retrieval latency, and cost—by compressing full-precision embeddings into more compact representations. Below is a breakdown of how quantization drives efficiency in each area.

Figure 3: Quantization Performance Metrics: Memory Savings with Minimal Accuracy Trade-offs

Memory and storage optimization

Quantization techniques dramatically reduce compute resource requirements while maintaining search accuracy for vector embeddings at scale.

• Lower RAM footprint
• Storage in RAM is often the primary bottleneck for vector search systems
• Embeddings stored as 8-bit integers or binary reduce overall memory usage, allowing significantly more vectors to remain in memory.
• This compression directly shrinks vector indexes (e.g., HNSW), leading to faster lookups and fewer disk I/O operations.
• Reduced disk usage in collection with binData
• binData (binary) formats can cut raw storage needs by up to 66%.
• Some disk overhead may remain when storing both quantized and original vectors, but the performance benefits justify this tradeoff.
• Practical gains
• 3.75× reduction in RAM usage with scalar (int8) quantization
• Up to 24× reduction with binary quantization, especially when combined with rescoring to preserve accuracy.
• Significantly more efficient vector indexes, enabling large-scale deployments without prohibitive hardware upgrades.

Retrieval latency

Quantization methods leverage CPU cache optimizations and efficient distance calculations to accelerate vector search operations beyond what's possible with standard float32 embeddings.

• Faster similarity computations
• Smaller data types are more CPU-cache-friendly, which speeds up distance calculations.
• Binary quantization uses Hamming distance (XOR + popcount), yielding dramatically faster top-k candidate retrieval.
• Improved throughput
• With reduced memory overhead, the system can handle more concurrent queries at lower latencies.
• In internal benchmarks, query performance for large-scale retrievals improved by up to 80% when adopting quantized vectors.

Cost efficiency

Vector quantization provides substantial infrastructure savings by reducing memory and computation requirements while maintaining retrieval quality through compression and rescoring techniques.

• Lower infrastructure costs
• Smaller vectors consume fewer hardware resources, enabling deployments on less expensive instances or tiers.
• Reduced CPU/GPU time per query allows resource reallocation to other critical parts of the application.
• Better scalability
• As data volumes grow, memory and compute requirements don’t escalate as sharply.
• Quantization-aware training (QAT) models, such as those from Voyage AI, help maintain accuracy while reaping cost savings at scale.

By compressing vectors into int8 or binary formats, you tackle memory constraints, accelerate lookups, and curb infrastructure expenses—making vector quantization an indispensable strategy for high-volume AI applications.

MongoDB Atlas: Built for Changing Workloads with Automatic Vector Quantization

The good news for developers is that MongoDB Atlas supports “automatic scalar” and “automatic binary quantization” in index definitions, reducing the need for external scripts or manual data preprocessing. By quantizing at index build time and query time, organizations can run large-scale vector workloads on smaller, more cost-effective clusters.

A common question most developers ask is when to use quantization.

Quantization becomes most valuable once you reach substantial data volumes—on the order of a million or more embeddings. At this scale, memory and compute demands can skyrocket, making reduced memory footprints and faster retrieval speeds essential.

Examples of cases that call for quantization include:

• High-volume scenarios: Datasets with millions of vector embeddings where you must tightly control memory and disk usage.
• Real-time responses: Systems needing low-latency queries under high user concurrency.
• High query throughput: Environments with numerous concurrent requests demanding both speed and cost-efficiency.

For smaller datasets (under 1 million vectors), the added complexity of quantization may not justify the benefits. However, for large-scale deployments, it becomes a critical optimization that can dramatically improve both performance and cost-effectiveness.

Now that we have established a strong foundation on the advantages of quantization—specifically the benefits of binary quantization with rescoring— feel free to refer to the MongoDB documentation to learn more about implementing vector quantization. You can also learn more about Voyage AI’s state-of-the-art embedding models on our product page.

]]> Thu, 27 Feb 2025 15:00:00 GMT https://www.mongodb.com/blog/post/why-vector-quantization-matters-for-ai-workloads https://www.mongodb.com/blog/post/why-vector-quantization-matters-for-ai-workloads Across industries—and especially in highly regulated sectors like healthcare, financial services, and government—MongoDB has been a preferred modern database solution for organizations handling large volumes of sensitive data that require strict compliance adherence. In such enterprises, secure access to data via APIs is critical, particularly when information is distributed across multiple MongoDB databases and external data stores. Hasura extends and enhances MongoDB's access control capabilities by providing granular permissions at the column and field level across multiple databases through its unified interface. At the same time, designing a secure API system from scratch to meet this need takes significant development resources and becomes a burden to maintain and update. Hasura solves this problem for enterprises by elegantly serving as a federated data layer, with robust access control policies built-in.

Hasura enforces powerful access control rules across data domains, joins data from multiple sources, and exposes it to the user via a single API. In this blog, we'll explore how Hasura and MongoDB work together to empower teams with granular data access control while simplifying data retrieval across collections.

Team-specific data domains

First, Hasura makes it possible for a business unit or team to own a set of databases and collections, also known as a data domain. Within each domain, a team can connect any number of MongoDB databases and other data sources, allowing the domain to have fine-grained role-based access control (RBAC) and attribute-based access control (ABAC) across all sources. More important though, is the ability to enable relationships that span domains, effectively connecting data from various teams or business units and exposing it to a verified user as necessary. This granular permissioning system means that the right users can access the right data at the right time, without compromising security.

Field-level access control

Hasura’s MongoDB connector also provides a powerful, declarative way to define access control rules at the collection and field level. For each MongoDB collection, roles may be specified for read, create, update, and delete (CRUD) permissions. Within those permissions, access may be further restricted based on the values of specific attributes. By defining these rules declaratively, Hasura makes it easy to implement and reason about complex access control policies.

Joining across collections

In addition to enabling granular access control, Hasura simplifies the retrieval of related data across multiple databases. By inspecting your MongoDB collections, Hasura can automatically create schemas and API endpoints (in GraphQL, REST, etc.) that let you query data along with its relationships. This eliminates the need to manually stitch together data from different collections in your application code. Instead, a graph of related data can be easily retrieved in a single API call, while still having that data filtered through your access control rules.

As companies wrestle with the challenges of secure data access across sprawling database environments, Hasura provides a compelling solution. By serving as a federated data layer on MongoDB and external data, Hasura enables granular access control through a combination of role-based permissions, attribute-based restrictions, and the ability to join data and apply access across sources.

Figure 1. Hasura & MongoDB demo environment

With Hasura’s MongoDB connector, teams can easily implement sophisticated data access policies in a declarative way and provide their applications with secure access to the data they need. This combination of security and simplicity makes Hasura and MongoDB a powerful solution for organizations that strive to modernize, especially those in industries with strict compliance requirements.

Visit the MongoDB Resources Hub to learn more about MongoDB Atlas.

]]> Wed, 26 Feb 2025 15:00:00 GMT https://www.mongodb.com/blog/post/hasura-powerful-access-control-on-mongodb-data https://www.mongodb.com/blog/post/hasura-powerful-access-control-on-mongodb-data 오늘날 채용 환경은 보다 효율적이고 유연하며 투명 방식으로 빠르게게 진화하고 있습니다. 이에 발맞춰, 국내 스타트업 라라잡이 운영하는 구인구직 플랫폼 동네알바는 ‘바른 사람과 바른 일터가 서로 돕고 신뢰하는 세상을 만든다’는 미션 아래 지역 기반 맞춤형 채용 서비스를 제공하며 비정규직 채용 시장에서 신뢰할 수 있는 플랫폼으로 자리잡고 있습니다.

동네알바는 앱 출시 후 단 4개월 만에 10만 명의 사용자를 확보했으며, 현재 연간 200만 명의 아르바이트생과 고용주가 이용하는 플랫폼으로 성장했습니다. 2023년, 한국 최대 취업정보 제공업체인 사람인에 인수된 이후에는 구인구직을 넘어 인사관리까지 가능한 HR SaaS 플랫폼으로 사업을 확장하고 있습니다.

MongoDB Atlas Search로 검색 성능 혁신

라라잡 개발팀은 수백만 명의 사용자에게 원활하고 안정적인 채용 경험을 제공하기 위해 일찌감치 MongoDB를 도입했습니다. 특히, MongoDB Atlas가 제공하는 유연성과 빠른 속도, 확장성을 기반으로 동네알바의 사용자 경험을 지속적으로 개선하고 있습니다.

플랫폼 개선을 위한 첫 단계로, 2023년 5월 MongoDB Atlas Search를 도입했습니다. 방대한 구인 목록과 사용자 쿼리를 신속하게 처리할 수 있는 강력한 검색 엔진이 필요했던 라라잡은 기존의 MongoDB Atlas 환경에서 별도 인프라 구축이나 추가 비용 없이 MongoDB Atlas Search를 손쉽게 적용할 수 있었습니다.

라라잡 백우락 백엔드팀 리드는 “MongoDB Atlas Search 도입 후 검색 속도가 4배, 집계 속도가 15배 향상되는 등 성능이 크게 개선되었고, 이는 사용자 경험 향상으로 이어졌다”며 “MongoDB Atlas Search는 별도의 컬렉션 구성 없이 원하는 구축 방식과 용도에 맞게 적용할 수 있다는 점이 가장 큰 장점”이라고 밝혔습니다.

또한, “MongoDB가 제공하는 비정형 데이터 처리 기능을 활용해, 동네알바 앱 내 구인 목록부터 사용자 프로필까지 다양한 데이터 유형을 효율적으로 관리하고 있다. 이를 통해 아르바이트생을 위한 추천 공고 탐색 서비스를 최적화하고, 단 두 달만에 Atlas Search 기반 컬렉션을 구축해 최적의 검색 환경을 완성했다”고 강조했습니다.

안전하고 직관적인 검색 기능 구현

MongoDB Atlas Search는 이름, 연락처 등 암호화된 데이터의 인덱싱을 지원해 익명 처리된 정보에 대한 안전하면서도 정확한 검색 결과를 제공합니다. 또한, MongoDB의 지리공간 연산자(Geospatial Query Operators)를 활용해 지도 내 반경 검색을 구현, 지역 기반 서비스라는 동네알바의 특성을 극대화했습니다. 이러한 기능을 통해 모바일 환경에 최적화된 직관적인 서비스를 제공하며, 사용자의 편의성을 한층 더 높였습니다.

신뢰할 수 있는 채용 플랫폼으로의 도약

라라잡은 투명하고 신뢰할 수 있는 채용 환경을 조성하는 것을 목표로, 아르바이트생과 고용주가 안심하고 사용할 수 있는 플랫폼을 만들어가고 있습니다. MongoDB는 이러한 기술 혁신을 뒷받침하는 핵심 파트너로서 중요한 역할을 하고 있으며, 향후 AI 기능 도입, 추천 및 모니터링 시스템 향상 등 MongoDB의 다양한 기술을 적극 활용할 계획입니다.

라라잡 백우락 백엔드팀 리드는 “MongoDB의 유연성과 확장성을 바탕으로 동네알바를 사용하는 수백만 명의 사용자를 지원할 뿐만 아니라 근로자와 고용주 모두에게 안전하고 효율적이며 신뢰할 수 있는 환경을 조성하는 플랫폼을 구축해 나갈 계획”이라며 “앞으로도 비정형 데이터를 쉽게 관리해야 하는 곳이라면 어디든 MongoDB를 적극 활용할 것”이라고 포부를 밝혔습니다.

]]> Wed, 26 Feb 2025 01:00:00 GMT https://www.mongodb.com/blog/post/rarajob-connects-2-million-job-seekers-employers-mongodb-atlas-search-kr https://www.mongodb.com/blog/post/rarajob-connects-2-million-job-seekers-employers-mongodb-atlas-search-kr MongoDB is frequently viewed as a go-to database for proof-of-concept (POC) applications. The flexibility of MongoDB’s document model enables teams to rapidly prototype and iterate. This allows for adaptation of the data model as requirements evolve during the early stages of application development. It is common for applications to continuously evolve during initial development. However, moving an application to production requires developers to add validation logic and fully define the data structures.

A frequent assumption is that because MongoDB data models can be flexible, they can not be structured. However, while MongoDB does not require a defined schema, it does support them. MongoDB allows users to precisely calibrate rules and enforcement levels for every component of data. This enables a level of granular control that traditional databases, with their all-or-nothing approach to schema enforcement, struggle to match.

Data model flexibility is not a binary choice between "schemaless" or "strictly enforced." More accurately, it exists on a spectrum in MongoDB. Users can incrementally define schemas in parallel with the overall “hardening” of the application.

MongoDB's approach to data modeling makes it an ideal platform for business-critical applications. It is designed to support the entire application lifecycle; from nascent concepts and initial prototypes, to global rollouts of production environments. Enterprise-grade features like ACID transactions and industry-leading scalability ensure MongoDB can meet the demands of any modern application.

Learning from the past

So why do misconceptions persist regarding MongoDB? These perceptions originated over a decade ago. Teams working with MongoDB back in 2014 or earlier faced challenges when deploying it in production. Applications could slow down under heavy loads, data consistency was not guaranteed when writing to multiple documents, and teams lacked tools to monitor and manage deployments effectively.

As a result, MongoDB gained a perception of being unsuitable for specific use cases or critical workloads. This perception has persisted despite a decade of subsequent development and innovation. Therefore, this is now an inaccurate assessment of today’s preeminent document database. MongoDB has evolved into a mature platform that directly addresses these historical pain points. Today’s MongoDB delivers robust tooling, guaranteed consistency, and comprehensive data validation capabilities.

Myth: MongoDB is a niche database

What are the top use cases for MongoDB? This question is difficult to answer because MongoDB is a general-purpose database that can support any use case.

The document model is the primary driver of MongoDB’s versatility. Documents are similar to JSON objects with data being represented as key-value pairs. Values can be simple types like strings or numbers. However, values can also be arrays or nested objects which allows documents to easily represent complex hierarchical structures. The document model's flexibility allows data to be stored exactly as the application consumes it. This enables highly efficient writing and optimizes data for retrieval without needing to set up standard or materialized views, although both are supported.

While MongoDB is no longer a niche database, it does have advanced capabilities to support niche requirements. The aggregation pipeline provides a powerful framework for data analytics and transformation. Time-series collections store and query temporal data efficiently to support IoT and financial applications. Geospatial indexes and queries enable location-based applications to perform complex proximity calculations. MongoDB Atlas includes native support for vector search. This enabled Cisco to experiment with generative AI use cases and streamline their applications to production.

MongoDB handles the diverse data requirements that power modern applications. The document model provides the foundation for general use. Concurrently, advanced features ensure teams do not need to integrate additional tools as application requirements evolve. The result is a single platform that can grow from prototype to production, handling general requirements and specialized workloads with equal proficiency.

Myth: MongoDB is not suitable for enterprise-grade workloads

A common perception is that MongoDB works well for small applications but falls short at enterprise scale. Ironically, many organizations first consider MongoDB while struggling to scale their relational databases. These organizations have discovered MongoDB’s architecture is specifically designed to support scale-out distributed deployments.

While MongoDB matches relational databases in vertical scaling capabilities, the document model enables a more natural and intuitive approach for horizontal scaling. Related data is stored together in a single document. Therefore, MongoDB can easily distribute complete data units across shards. This contrasts with relational databases. Relational data is split across multiple tables. This makes it difficult to place all related data on the same shard. Horizontal scaling with MongoDB sets an organization up for better performance. Most MongoDB queries need to access only a single shard. Equivalent queries in a relational database often require costly cross-server communication. Telefonica Tech has leveraged horizontal scaling to nearly double their capacity with a 40% hardware reduction.

MongoDB Atlas further automates and simplifies these scaling capabilities through a fully managed service built to meet demanding enterprise requirements. Atlas provides a 99.995% uptime guarantee and availability across AWS, Google Cloud, and Azure in over 100 regions worldwide. This frees teams to focus on rapid development and innovation rather than infrastructure maintenance by offloading the operational complexity of deploying and running databases at scale.

Powering the enterprise applications of today and tomorrow

Over 50,000 customers and 70% of the Fortune 100 rely on MongoDB to power their enterprise applications. Independent industry reports from Gartner and Forrester continue to recognize MongoDB as a leader in the database space. Do not let outdated myths prevent your organization from the competitive advantages of MongoDB's enterprise capabilities.

To learn more about MongoDB, head over to MongoDB University and take our free Intro to MongoDB course.

Read more about customers building on MongoDB.

Read our first blog in this series about myths around MongoDB vs relational databases.

Check out the full video to learn about the other 6 myths that we're debunking in this series.

]]> Tue, 25 Feb 2025 16:00:00 GMT https://www.mongodb.com/blog/post/debunking-mongodb-myths-enterprise-use-cases https://www.mongodb.com/blog/post/debunking-mongodb-myths-enterprise-use-cases

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AI is reshaping industries, redefining customer experiences, and transforming how businesses innovate, operate, and compete. While much of the focus is on frontier models, a fundamental challenge lies in data—how it is stored, retrieved, and made useful for AI applications. The democratization of AI-powered software depends on building on top of the right abstractions, yet today, creating useful, real-time AI applications at scale is not feasible for most organizations.

The challenge isn’t just complexity—it’s trust. AI models are probabilistic, meaning their outputs aren’t deterministic and predictable. This is easily evident in the hallucination problem in chatbots today, and becomes even more critical with the rise of agents, where AI systems make autonomous decisions. Development teams need the ability to control, shape, and ground generated outputs to align with their objectives and ensure accuracy.

AI-powered search and retrieval is a powerful tool that extracts relevant contextual data from specific sources, augmenting AI models to generate reliable and accurate responses or take responsible and safe actions, as seen in the prominent retrieval augmented generation (RAG) approach. At the core of AI-powered retrieval are embedding generation and reranking—two key AI components that capture the semantic meaning of data and assess the relevance of queries and results. We believe embedding generation and reranking, as well as AI-powered search, belong in the database layer, simplifying the stack and creating a more reliable foundation for AI applications. By bringing more intelligence into the database, we help businesses mitigate hallucinations, improve trustworthiness, and unlock AI’s full potential at scale.

The most impactful applications require a flexible, intelligent, and scalable data foundation. That’s why we’re excited to announce the acquisition of Voyage AI, a leader in embedding and reranking models that dramatically improve accuracy through AI-powered search and retrieval. This move isn’t just about adding AI capabilities—it’s about redefining the database for the AI era.

Why this matters: The future of AI is built on better relevance and accuracy in data

AI is probabilistic—it’s not built like traditional software with pre-defined rules and logic. Instead, it generates responses or takes action based on how the AI model is trained and what data is retrieved. However, due to the probabilistic nature of the technology, AI can hallucinate. Hallucinations are a direct consequence of poor or imprecise retrieval—when AI lacks access to the right data, it generates plausible but incorrect information. This is a critical barrier to AI adoption, especially in enterprises and for mission-critical use cases where accuracy is non-negotiable.

This makes retrieving the most relevant data essential for AI applications to deliver high-quality, contextually accurate results. Today, developers rely on a patchwork of separate components to build AI-powered applications. Sub-optimal choices of these components, such as embedding models, can yield low-relevancy data retrieval and low-quality generated outputs. This fragmented approach is complex, costly, inefficient, and cumbersome for developers.

With Voyage AI, MongoDB solves this challenge by making AI-powered search and retrieval native to the database. Instead of implementing workarounds or managing separate systems, developers can generate high-quality embeddings from real-time operational data, store vectors, perform semantic search, and refine results—all within MongoDB. This eliminates complexity and delivers higher accuracy, lower latency, and a streamlined developer experience.

What Voyage AI brings to MongoDB

Voyage AI has built a world-class AI research team with roots at Stanford, MIT, UC Berkeley, and Princeton and has rapidly become a leader in high-precision AI retrieval. Their technology is already trusted by some of the most advanced AI startups, including Anthropic, LangChain, Harvey, and Replit.

Notably, Voyage AI’s embedding models are the highest-rated zero-shot models in the Hugging Face community. Voyage AI’s models are designed to increase the quality of generated output by:

• Enhancing vector search by creating embeddings that better capture meaning across text, images, PDFs, and structured data.

• Improving retrieval accuracy through advanced reranking models that refine search results for AI-powered applications.

• Enabling domain-specific AI with fine-tuned models optimized for different industries such as financial services, healthcare, and law, and use cases such as code generation.

By integrating Voyage AI’s retrieval capabilities into MongoDB, we’re helping organizations more easily build AI applications with greater accuracy and reliability—without unnecessary complexity.

How Voyage AI will be integrated into MongoDB

We are integrating Voyage AI with MongoDB in three phases. In the first phase, Voyage AI’s text embedding, multi-modal embedding, and reranking models will remain widely available through Voyage AI’s current APIs and via the AWS and Azure Marketplaces—ensuring developers can continue to use their best-in-class embedding and reranking capabilities. We will also invest in the scalability and enterprise readiness of the platform to support the increased adoption of Voyage AI’s models.

Next, we will seamlessly embed Voyage AI’s capabilities into MongoDB Atlas, starting with an auto-embedding service for Vector Search, which will handle embedding generation automatically. Native reranking will follow, allowing developers to boost retrieval accuracy instantly. We also plan to expand domain-specific AI capabilities to better support different industries (e.g., financial services, legal, etc.) or use cases (e.g., code generation).

Finally, we will advance AI-powered retrieval with enhanced multi-modal capabilities, enabling seamless retrieval and ranking of text, images, and video. We also plan to introduce instruction-tuned models, allowing developers to refine search behavior using simple prompts instead of complex fine-tuning. This will be complemented by embedding lifecycle management in MongoDB Atlas, ensuring continuous updates and real-time optimization for AI applications.

What this means for developers and businesses

AI-powered applications need more than a database that just stores, processes, and persists data—they need a database that actively improves retrieval accuracy, scales seamlessly, and eliminates operational friction. With Voyage AI, MongoDB redefines what’s required for a database to underpin mission-critical AI-powered applications.

Developers will no longer need to manage external embedding APIs, standalone vector stores, or complex search pipelines. AI retrieval will be built into the database itself, making semantic search, vector retrieval, and ranking as seamless as traditional queries.

For businesses, this translates to faster time-to-value and greater confidence in scaling AI applications. By delivering high-quality results at scale, enterprises can seamlessly integrate AI into their most critical use cases, ensuring reliability, performance, and real-world impact.

Looking ahead: What comes next

This is just the beginning. Our vision is to make MongoDB the most powerful and intuitive database for modern, AI-driven applications.

• Voyage AI’s models will soon be natively available in MongoDB Atlas.

• We will continue evolving MongoDB’s AI retrieval capabilities, making it smarter, more adaptable, and capable of handling a wider range of data types and use cases.

Stay tuned for more details on how you can start using Voyage AI’s capabilities in MongoDB.

To learn more about how MongoDB and Voyage AI are powering state-of-the-art AI search and retrieval for building, scaling, and deploying intelligent applications, visit our product page.

]]> Mon, 24 Feb 2025 14:18:07 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai AI는 산업을 재편하고, 고객 경험을 새롭게 정의하며, 기업의 혁신, 운영, 경쟁 방식을 변화시키고 있습니다. 대부분의 초점이 프론티어 모델에 맞추어져 있지만, 근본적인 과제는 데이터를 저장하고 조회하여 AI 애플리케이션에 유용하게 만드는 방법에 있습니다. AI 기반 소프트웨어의 민주화는 적절한 추상화 위에 구축하는 데 달려 있지만, 현재 대부분의 조직에서 유용한 실시간 AI 애플리케이션을 대규모로 생성하는 것은 불가능합니다.

과제는 단지 복잡성만이 아닌 신뢰입니다. AI 모델은 확률론적이기 때문에 출력이 결정론적이지 않고 예측할 수 없습니다. 이는 오늘날 챗봇의 환각 문제에서 쉽게 확인할 수 있으며, AI 시스템이 자율적으로 의사 결정을 내리는 에이전트의 부상으로 더욱 중요해졌습니다. 개발 팀은 생성된 출력을 목표에 맞게 조정하고 정확성을 보장하기 위해 출력을 제어하고 구체화하며 근거를 마련할 수 있는 능력이 필요합니다.

AI 기반 검색 및 조회는 특정 소스에서 관련 문맥 데이터를 추출하여 신뢰할 수 있고 정확한 응답을 생성하거나 책임감 있고 안전한 조치를 취하도록 AI 모델을 보강하는 강력한 도구입니다. 이는 RAG(검색 증강 생성) 접근 방식에서 두드러지게 나타납니다. AI 기반 검색의 핵심은 데이터의 의미론적 의미를 포착하고 쿼리와 결과의 관련성을 평가하는 두 가지 주요 AI 구성 요소인 임베딩 생성과 순위 재지정입니다. 임베딩 생성과 순위 재지정 및 AI 기반 검색은 데이터베이스 계층에 포함되어 스택을 간소화하고 AI 애플리케이션을 위한 보다 안정적인 기반을 마련한다고 생각합니다. 데이터베이스에 보다 많은 지능을 활용하여 MongoDB는 기업이 환각을 완화하고, 신뢰성을 높이고, AI의 잠재력을 대규모로 최대한 발휘할 수 있도록 지원합니다.

가장 영향력 있는 애플리케이션에는 유연하고 지능적이며 확장 가능한 데이터 기반이 필요합니다. 그래서 AI 기반 검색 및 조회를 통해 정확도를 획기적으로 개선하는 임베딩 및 순위 재지정 모델 분야의 선두주자인 Voyage AI 인수를 발표하게 되어 기쁩니다. 이러한 움직임은 단순히 AI 기능을 추가하는 것이 아니라 AI 시대에 맞춰 데이터베이스를 재정의하는 것입니다.

이것이 중요한 이유: AI의 미래는 데이터의 더 나은 관련성과 정확성을 기반으로 구축됩니다

AI는 확률론적입니다. 사전 정의된 규칙과 논리를 갖춘 기존 소프트웨어처럼 구축되지 않았습니다. 대신 AI 모델의 훈련 방식과 조회된 데이터에 따라 응답을 생성하거나 조치를 취합니다. 그러나 기술의 확률론적 특성으로 인해 AI는 환각을 일으킬 수 있습니다. 환각은 잘못되거나 부정확한 검색의 직접적인 결과로, AI가 적절한 데이터에 액세스하지 못하면 그럴듯하지만 잘못된 정보를 생성합니다. 이는 특히 정확도를 타협할 수 없는 엔터프라이즈 및 미션 크리티컬 사용 사례에서 AI 도입을 가로막는 중대한 장벽입니다.

따라서 가장 관련성이 높은 데이터를 검색하는 것은 AI 애플리케이션이 고품질의 맥락에 맞는 정확한 결과를 제공하는 데 필수적입니다. 오늘날 개발자들은 AI 기반 애플리케이션을 구축하기 위해 개별 구성 요소의 조합에 의존합니다. 임베딩 모델과 같은 이러한 구성 요소를 최적으로 선택하지 않으면 데이터 검색의 관련성이 낮아지고 생성된 출력의 품질이 낮아질 수 있습니다. 이러한 단편적인 접근 방식은 복잡하고 비용이 많이 들며 비효율적이고 개발자에게 번거롭습니다.

MongoDB는 Voyage AI를 통해 데이터베이스에 AI 기반 검색 및 조회 기능을 네이티브로 제공하여 이 과제를 해결합니다. 개발자는 해결 방법을 구현하거나 별도의 시스템을 관리하는 대신 MongoDB 내에서 실시간 운영 데이터로부터 고품질 임베딩을 생성하고, 벡터를 저장하고, 시맨틱 검색을 수행하고, 결과를 정제할 수 있습니다. 이를 통해 복잡성을 제거하고 더 높은 정확도, 더 짧은 지연 시간, 간소화된 개발자 경험을 제공할 수 있습니다.

Voyage AI가 MongoDB에 제공하는 기능

Voyage AI는 Stanford, MIT, UC Berkeley, Princeton에 뿌리를 둔 세계적 수준의 AI 연구팀을 구축하여 고정밀 AI 검색 분야의 리더로 빠르게 성장해 왔습니다. 이들의 기술은 이미 Anthropic, LangChain, Harvey, Replit 등 가장 발전한 AI 스타트업의 신뢰를 받고 있습니다.

특히 Voyage AI의 임베딩 모델은 Hugging Face 커뮤니티에서 가장 높은 평가를 받은 제로샷 모델입니다. Voyage AI의 모델은 다음을 통해 생성된 출력의 품질을 향상하도록 설계되었습니다:

• 텍스트, 이미지, PDF 및 정형 데이터 전반의 의미를 더 잘 포착하는 임베딩을 생성하여 벡터 검색을 향상시킵니다.

• AI 기반 애플리케이션의 검색 결과를 정제하는 고급 순위 재지정 모델을 통해 검색 정확도를 향상시킵니다.

• 금융 서비스, 의료, 법률과 같은 다양한 산업에 최적화된 미세 조정된 모델과 코드 생성과 같은 사용 사례를 통해 분야별 AI를 활성화합니다.

Voyage AI의 검색 기능을 MongoDB에 통합함으로써 조직이 불필요한 복잡성 없이 더 높은 정확도와 신뢰성을 갖춘 AI 애플리케이션을 보다 쉽게 구축할 수 있도록 지원하고 있습니다.

Voyage AI가 MongoDB에 통합되는 방식

3단계에 걸쳐 Voyage AI를 MongoDB와 통합할 예정입니다. 첫 번째 단계에서는 Voyage AI의 텍스트 임베딩, 멀티모달 임베딩 및 순위 재지정 모델이 Voyage AI의 현재 API와 AWS 및 Azure Marketplace를 통해 계속 널리 제공되어 개발자가 동급 최고의 임베딩 및 순위 재지정 기능을 계속 사용할 수 있도록 보장할 것입니다. 또한 Voyage AI 모델 채택 증가를 지원하기 위해 플랫폼의 확장성과 엔터프라이즈 준비성에 투자할 것입니다.

다음으로, 임베딩 생성을 자동으로 처리하는 Vector Search용 자동 임베딩 서비스를 시작으로 Voyage AI의 기능을 MongoDB Atlas에 원활하게 임베딩할 예정입니다. 네이티브 순위 재지정이 뒤따를 예정이므로 개발자는 검색 정확도를 즉시 높일 수 있습니다. 또한 다양한 산업(예: 금융 서비스, 법률 등)이나 사용 사례(예: 코드 생성)를 더 잘 지원하기 위해 분야별 AI 기능을 확장할 계획입니다.

마지막으로, 향상된 멀티모달 기능을 통해 AI 기반 검색을 발전시켜 텍스트, 이미지, 동영상을 원활하게 검색하고 순위를 지정할 수 있도록 할 것입니다. 또한 명령어 조정 모델을 도입하여 개발자가 복잡한 미세 조정 대신 간단한 프롬프트를 사용하여 검색 동작을 개선할 수 있도록 할 계획입니다. 이는 MongoDB Atlas에 수명 주기 관리 기능을 임베딩하여 AI 애플리케이션에 대한 지속적인 업데이트와 실시간 최적화를 보장함으로써 보완될 것입니다.

이것이 개발자와 기업에 의미하는 것

AI 기반 애플리케이션에는 단순히 데이터를 저장, 처리, 보존하는 데이터베이스가 아니라 검색 정확도를 능동적으로 개선하고 원활하게 확장하며 운영상의 마찰을 없애는 데이터베이스가 필요합니다. MongoDB는 Voyage AI를 통해 데이터베이스가 미션 크리티컬 AI 기반 애플리케이션을 지원하는 데 필요한 요건을 재정의합니다.

개발자는 더 이상 외부 임베딩 API, 독립형 벡터 저장소 또는 복잡한 검색 파이프라인을 관리할 필요가 없습니다. AI 검색 기능이 데이터베이스 자체에 내장되어 시맨틱 검색, 벡터 검색, 순위 지정을 기존 쿼리처럼 원활하게 수행할 수 있습니다.

기업에게 이는 가치 실현 시간을 단축하고 AI 애플리케이션 확장에 대한 확신이 높아진다는 것을 의미합니다. 대규모로 고품질의 결과를 제공함으로써 엔터프라이즈는 가장 중요한 사용 사례에 AI를 원활하게 통합하여 신뢰성, 성능 및 실제 영향을 보장할 수 있습니다.

앞으로의 전망: 다음 단계

이것은 시작에 불과합니다. MongoDB의 비전은 MongoDB를 최신 AI 기반 애플리케이션을 위한 가장 강력하고 직관적인 데이터베이스로 만드는 것입니다.

• Voyage AI 모델은 곧 MongoDB Atlas에서 기본적으로 제공될 예정입니다.

• MongoDB의 AI 검색 기능을 계속 발전시켜 더욱 스마트하고 적응력이 뛰어나며 다양한 데이터 유형과 사용 사례를 처리할 수 있도록 만들 것입니다.

MongoDB에서 Voyage AI의 기능을 사용하는 방법에 대한 자세한 내용은 계속 지켜봐 주세요.

MongoDB와 Voyage AI가 지능형 애플리케이션의 구축, 확장, 배포를 위한 최첨단 AI 검색 및 조회를 지원하는 방식에 대해 자세히 알아보려면 제품 페이지를 방문하세요.

]]> Mon, 24 Feb 2025 14:18:07 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-kr https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-kr AI 正在重塑各行各业，重新定义客户体验，并改变企业创新、运营和竞争的方式。尽管大部分关注点在前沿模型上，但一项根本的挑战在于数据 — 如何存储和检索数据并让数据为 AI 应用所用。AI 驱动软件的民主化依赖于在正确的抽象层上进行开发，但目前，对于大多数组织来说，大规模创建有用的实时 AI 应用仍然不可行。

挑战不仅在于复杂性，还在于信任。AI 模型是概率性的，这意味着其输出不具有确定性和可预测性。这在当今聊天机器人的幻觉问题中显而易见，并且随着 AI 智能载体的兴起，AI 系统可以自主做出决策，这一点变得更加重要。开发团队需要能够控制、塑造和调整生成的输出，以符合其目标并确保准确性。

AI 驱动的搜索和检索是一项强大的工具，可以从特定来源提取相关的上下文数据，增强 AI 模型，以生成可靠和准确的响应或采取负责任和安全的行动，这在著名的检索增强生成（RAG）方法中得到了体现。在 AI 驱动的检索中，核心是嵌入生成和重新排序 — 这两个关键的 AI 组件能够捕捉数据的语义含义，并评估问询和结果的相关性。我们认为将生成、重新排序以及 AI 驱动的搜索嵌入数据库层可简化堆栈，从而为 AI 应用奠定更可靠的基础。通过将更多智能引入数据库，我们帮助企业减少幻觉，提高可信度，并在 AI 扩展上释放 AI 的全部潜力。

最具影响力的应用需要一个灵活、智能且可扩展的数据基础。因此，我们很高兴地宣布收购了 Voyage AI，这是一家在嵌入和重新排序模型领域的领导者，通过 AI 驱动的搜索和检索显著提高了准确性。此举不仅旨在增加 AI 功能，更是关乎为 AI 时代重新定义数据库。

为什么这很重要：AI 的未来构建在数据更高的相关性和准确性之上

AI 是概率性的 — 这不像传统软件那样具有预定义的规则和逻辑。相反，它会根据 AI 模型的训练方式和检索到的数据生成响应或采取行动。然而，由于该技术的概率性，AI 可能会出现幻觉。幻觉是检索不佳或不精确的直接后果 — 当 AI 无法访问正确的数据时，它会生成看似合理但不正确的信息。这是 AI 采用的一项关键障碍，尤其是在企业中以及在准确性不可妥协的关键任务用例中。

这使得检索最相关的数据对于 AI 应用程序提供高质量、上下文准确的结果至关重要。如今，开发者依赖于拼凑而成的独立组件来构建 AI 驱动的应用程序。这些组件的次优选择，例如嵌入模型，可能会导致低相关性的数据检索和低质量的生成输出。这种分散的方法对开发者来说既复杂、昂贵、效率低下，又繁琐。

借助 Voyage AI，MongoDB 通过使 AI 驱动的搜索和检索成为数据库的原生功能，解决了这一挑战。开发者无需实施变通方法或管理单独的系统，而是可以从实时操作数据中生成高质量的嵌入，存储向量，执行语义搜索，并优化结果——所有这些都在 MongoDB 中完成。这消除了复杂性，并提供了更高的准确性、更低的延迟和简化的开发者体验。

Voyage AI 为 MongoDB 带来的优势

Voyage AI 已组建了一支以斯坦福大学、麻省理工学院、加州大学伯克利分校和普林斯顿大学为基础的世界级 AI 研究团队，并迅速成为高精度 AI 检索领域的领导者。他们的技术已经被一些最先进的 AI 初创企业所信任，包括 Anthropic、LangChain、Harvey 和 Replit。

值得注意的是，Voyage AI 的嵌入模型是 Hugging Face 社区中评分最高的零样本模型。Voyage AI 的模型旨在通过以下方式提高生成输出的质量：

• 通过创建更好地捕捉文本、图像、PDF 和结构化数据含义的嵌入来增强向量搜索。

• 通过先进的重新排序模型提高检索准确性，以优化 AI 驱动式应用的搜索结果。

• 通过使用针对金融服务、医疗保健、法律等不同行业以及代码生成等使用案例进行优化的微调模型，启用特定领域的 AI。

通过将 Voyage AI 的检索功能集成到 MongoDB 中，我们正在帮助组织更轻松地构建更准确、更可靠的 AI 应用，而不会增加不必要的复杂性。

如何将 Voyage AI 集成到 MongoDB 中

我们将 Voyage AI 与 MongoDB 的集成分为三个阶段。在第一阶段，Voyage AI 的文本嵌入、多模态嵌入和重排序模型将继续通过 Voyage AI 的现有 API 以及 AWS 和 Azure 云市场广泛提供，确保开发者可以继续使用其先进的嵌入和重新排序功能。我们还将投资于平台的可扩展性和企业级就绪能力，以支持 Voyage AI 模型的更广泛采用。

接下来，我们会将 Voyage AI 的功能无缝嵌入到 MongoDB Atlas 中，首先推出用于 Vector Search 的自动嵌入服务，该服务将自动处理嵌入生成。然后将进行原生重新排序，使开发人员能够立即提高检索准确性。我们还计划扩展特定领域的 AI 功能，以更好地支持不同行业（例如，金融服务、法律等）或用例（例如，代码生成）。

最后，我们将通过增强的多模态功能推进 AI 驱动的检索，实现文本、图像和视频的无缝检索和排序。我们还计划引入指令调整模型，允许开发者使用简单的提示而不是复杂的微调来优化搜索行为。这将通过在 MongoDB Atlas 中嵌入生命周期管理来实现补充，确保 AI 应用的持续更新和实时优化。

这对开发者和企业意味着什么？

AI 驱动的应用需要的不仅仅是一个存储、处理和持久化数据的数据库，而是还需要一个能够主动提高检索准确性、无缝扩展并消除操作摩擦的数据库。借助 Voyage AI，MongoDB 重新定义了支撑任务关键型 AI 驱动的应用的数据库要求。

开发者将不再需要管理外部嵌入 API、独立运行的实例向量存储或复杂的搜索管道。AI 检索将内建到数据库中，实现与传统查询一样的无缝语义搜索、向量检索和排序。

对于企业来说，这意味着能够更加信心十足地扩展 AI 应用，并加快价值实现速度。通过在大规模扩展交付高质量的结果，企业可以将 AI 无缝集成到其最关键的用例中，确保可靠性、性能和实际影响。

展望未来：接下来会发生什么

这仅仅是个开始。我们的愿景是将 MongoDB 打造成最强大且直观的数据库，适用于现代 AI 驱动的应用程序。

• Voyage AI 的模型将很快在 MongoDB Atlas 中原生可用。

• 我们将继续提升 MongoDB 的 AI 检索能力，使其更智能、更具适应性，并能够处理更广泛的数据类型和应用场景。

请继续关注最新动态，详细了解如何在 MongoDB 中开始使用 Voyage AI 功能。

要了解更多关于 MongoDB 和 Voyage AI 如何为构建、扩展和部署智能应用提供最先进的 AI 搜索和检索功能的信息，请访问我们的产品页面。

]]> Mon, 24 Feb 2025 14:18:00 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-cn https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-cn A AI está remodelando setores, redefinindo as experiências dos clientes e transformando a forma como as empresas inovam, operam e competem entre si. Embora grande parte do foco esteja nos modelos de ponta, um desafio fundamental está nos dados: como eles são armazenados, extraídos e se tornam úteis para aplicativos de AI. A democratização de softwares baseados em AI depende de um desenvolvimento a partir dos aspectos abstratos certos, mas, atualmente, criar aplicativos de AI úteis, em tempo real e em escala não é viável para a maioria das organizações.

O desafio não é apenas a complexidade, mas sim a confiança. Modelos de AI são probabilísticos, o que significa que seus resultados não são determinísticos e previsíveis. Isso é fácil de notar no problema de alucinação dos chatbots atuais, e fica ainda mais crucial com o surgimento de agentes, em que os sistemas de AI tomam decisões de forma autônoma. As equipes de desenvolvimento precisam ter a capacidade de controlar, moldar e fundamentar os resultados gerados para se alinharem com seus objetivos e garantir a precisão.

A busca e recuperação impulsionada por AI é uma ferramenta poderosa que extrai dados contextuais relevantes de fontes específicas, aprimorando modelos de AI para gerar respostas confiáveis e precisas ou tomar atitudes responsáveis e seguras, como visto na abordagem proeminente de geração aumentada por recuperação (RAG). No centro da recuperação impulsionada por AI estão a geração de incorporações e a reclassificação: dois componentes principais da AI que captam o significado semântico dos dados e avaliam a relevância de queries e resultados. Acreditamos que a geração de incorporações e a reclassificação, assim como a pesquisa impulsionada por AI, devem estar na camada de banco de dados, simplificando a pilha e criando uma base mais confiável para aplicativos de AI. Ao trazer mais inteligência para o banco de dados, ajudamos as empresas a mitigar alucinações, melhorar a confiabilidade e liberar todo o potencial da AI em grande escala.

Os aplicativos mais impactantes exigem uma base de dados flexível, inteligente e dimensionável. É por isso que estamos animados em anunciar a aquisição da Voyage AI, líder em modelos de incorporação e reclassificação que melhoram drasticamente a precisão por meio de pesquisa e recuperação impulsionadas por AI. Essa mudança não se trata apenas de adicionar funcionalidades de AI, mas também de redefinir o banco de dados para a era da AI.

Por que isso importa: o futuro da AI é criado com base em uma maior relevância e precisão dos dados

A AI é probabilística; não é criada como o software tradicional, com regras e lógica predefinidas. Em vez disso, ela gera respostas ou toma atitudes com base em como o modelo de AI é treinado e em quais dados são extraídos. No entanto, devido à natureza probabilística da tecnologia, a AI pode alucinar. As alucinações são uma consequência direta da extração inadequada ou imprecisa. Quando a AI não tem acesso aos dados corretos, ela gera informações plausíveis, mas incorretas. Essa é uma barreira crucial para a adoção de AI, especialmente em empresas e para casos de uso fundamentais, em que não se pode abrir mão da precisão.

Isso torna essencial a recuperação dos dados mais relevantes para que os aplicativos de AI entreguem resultados de alta qualidade e precisos quanto ao contexto. Hoje, os desenvolvedores dependem de um conjunto de componentes separados para criar aplicativos com tecnologia de AI. Escolhas inferiores desses componentes, como modelos de incorporação, podem resultar em recuperação de dados de baixa relevância e resultados de baixa qualidade. Essa abordagem fragmentada é complexa, cara, ineficiente e complicada para os desenvolvedores.

Com a Voyage AI, o MongoDB resolve esse desafio ao tornar a pesquisa e recuperação com tecnologia de AI nativas do banco de dados. Em vez de implementar soluções alternativas ou gerenciar sistemas separados, os desenvolvedores podem gerar incorporações de alta qualidade a partir de dados operacionais em tempo real, armazenar vetores, realizar pesquisa semântica e refinar os resultados. Tudo dentro do MongoDB. Isso elimina a complexidade e proporciona maior precisão, menor latência e uma experiência de desenvolvedor mais simplificada.

O que o Voyage AI traz para o MongoDB

A Voyage AI montou uma equipe de pesquisa em AI de excelência com origens em Stanford, MIT, UC Berkeley e Princeton, e rapidamente se tornou líder em recuperação de AI de alta precisão. A tecnologia deles já garantiu a confiança de algumas das startups de AI mais avançadas, como Anthropic, LangChain, Harvey e Replit.

Em especial, os modelos de incorporações da Voyage AI são os modelos zero-shot com a melhor classificação na comunidade Hugging Face. Os modelos da Voyage AI são projetados para melhorar a qualidade do resultado gerado:

• Aprimorando a pesquisa vetorial por meio da criação de incorporações que captam melhor o significado em textos, imagens, PDFs e dados estruturados.

• Aprimorando a precisão da recuperação por meio de modelos avançados de reclassificação que refinam os resultados de pesquisa para aplicativos impulsionados por AI.

• Habilitando AI específica de domínio com modelos ajustados e otimizados para diferentes setores, como serviços financeiros, de saúde e jurídicos, e casos de uso como geração de código.

Ao integrar as funcionalidades de recuperação da Voyage AI ao MongoDB, estamos ajudando as organizações a criar aplicativos de AI com maior facilidade, precisão e confiabilidade, sem complexidade desnecessária.

Como a Voyage AI será integrada ao MongoDB

Estamos integrando a Voyage AI ao MongoDB em três fases. Na primeira fase, os modelos de incorporação de texto, incorporação multimodal e reclassificação da Voyage AI continuarão amplamente disponíveis através das APIs atuais da Voyage AI e dos Marketplaces da AWS e Azure, garantindo que os desenvolvedores possam continuar a utilizar suas funcionalidades de incorporação e reclassificação de ponta. Também investiremos na escalabilidade e na prontidão empresarial da plataforma para dar suporte à crescente adoção dos modelos da Voyage AI.

Em seguida, integraremos totalmente as funcionalidades da Voyage AI ao MongoDB Atlas, começando com um serviço de incorporação automática para o Vector Search, que gerenciará a geração de incorporações automaticamente. A reclassificação nativa virá em seguida, permitindo que os desenvolvedores aumentem instantaneamente a precisão da recuperação. Também planejamos expandir as funcionalidades de AI específicas de domínio para oferecer melhor suporte a diferentes setores (por exemplo, serviços financeiros, jurídicos etc.) ou casos de uso (por exemplo, geração de código).

Por fim, avançaremos na recuperação com tecnologia de AI com funcionalidades multimodais aprimoradas, permitindo a recuperação e classificação total de textos, imagens e vídeos. Também planejamos introduzir modelos ajustados por instruções, permitindo que os desenvolvedores refinem o comportamento de pesquisa usando prompts simples em vez de ajustes complexos. Isso será complementado pela incorporação do gerenciamento do ciclo de vida no MongoDB Atlas, assegurando atualizações contínuas e otimização em tempo real para aplicativos de AI.

Como isso afeta desenvolvedores e empresas

Aplicativos com tecnologia de AI precisam de mais do que um banco de dados que apenas armazene, processe e mantenha dados; eles precisam de um banco de dados que melhore ativamente a precisão da recuperação, dimensione sem interrupções e elimine o atrito operacional. Com a Voyage AI, o MongoDB redefine o que é necessário para que um banco de dados sustente aplicativos fundamentais com tecnologia de AI.

Os desenvolvedores não precisarão mais gerenciar APIs de incorporação externas, armazenamentos de vetores autônomos ou pipelines de pesquisa complexos. A recuperação de AI será criada no banco de dados, tornando a pesquisa semântica, a recuperação de vetores e a classificação tão integradas quanto as queries tradicionais.

Para as empresas, isso se traduz em um tempo menor para obter valor e maior confiança no dimensionamento de aplicativos de AI. Ao fornecer resultados de alta qualidade em escala, as empresas podem integrar perfeitamente a AI aos seus casos de uso mais críticos, garantindo confiabilidade, desempenho e impacto no mundo real.

Olhando para o futuro: o que vem a seguir

Esse é apenas o começo. Nossa meta é fazer o MongoDB se tornar o banco de dados mais potente e intuitivo para aplicativos modernos impulsionados por AI.

• Os modelos da Voyage AI estarão disponíveis em breve, de forma nativa, no MongoDB Atlas.

• Continuaremos a evoluir as funcionalidades de recuperação de AI do MongoDB, tornando-as mais inteligentes, mais adaptáveis e capazes de lidar com uma gama mais ampla de tipos de dados e casos de uso.

Logo traremos mais detalhes sobre como você pode começar a usar as funcionalidades da Voyage AI no MongoDB.

Saiba mais sobre como o MongoDB e a Voyage AI estão impulsionando a pesquisa e recuperação de AI de última geração para criar, dimensionar e implantar aplicativos inteligentes acessando nossa página de produtos.

]]> Mon, 24 Feb 2025 14:18:00 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-br https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-br L'AI sta rimodellando i settori, ridefinendo le esperienze dei clienti e trasformando il modo in cui le aziende innovano, operano e competono. Sebbene gran parte dell'attenzione sia rivolta ai modelli di frontiera, una sfida fondamentale risiede nei dati: come vengono archiviati, recuperati e resi utili per le applicazioni di intelligenza artificiale. La democratizzazione dei software basati sull'AI dipende dalla pratica di sviluppare sulle astrazioni corrette, tuttavia oggi creare applicazioni AI utili e in tempo reale su larga scala non è fattibile per la maggior parte delle organizzazioni.

La sfida non rappresentata solo dalla complessità, ma anche dalla fiducia. I modelli di AI sono probabilistici, il che significa che i loro risultati non sono deterministici e prevedibili. Questo aspetto è facilmente ravvisabile nel problema delle allucinazioni nei chatbot odierni e diventa ancora più critico con l'ascesa degli agenti, dove i sistemi AI prendono decisioni autonome. I team di sviluppo devono avere la capacità di controllare, modellare e ancorare i risultati generati per allinearli ai loro obiettivi e garantire l'accuratezza.

La ricerca e il recupero basati sull'AI sono strumenti potenti che estraggono dati contestuali rilevanti da fonti specifiche, migliorando i modelli di AI per generare risposte affidabili e accurate o intraprendere azioni responsabili e sicure, come si vede nell'importante approccio Retrieval Augmented Generation (RAG). Al centro del recupero basato sull'AI ci sono la generazione di embedding e il reranking, due componenti chiave dell'AI che catturano il significato semantico dei dati e valutano la rilevanza delle query e dei risultati. Crediamo che l'integrazione della generazione di embedding e del reranking, così come la ricerca basata sull'AI, debbano essere parte del layer del database, semplificando lo stack e creando una base più affidabile per le applicazioni AI. Introducendo maggiore intelligenza nel database, aiutiamo le aziende a ridurre le allucinazioni, migliorare l'affidabilità e sfruttare appieno il potenziale dell'AI su larga scala.

Le applicazioni di maggior impatto richiedono una base dati flessibile, intelligente e scalabile. Ecco perché siamo entusiasti di annunciare l'acquisizione di Voyage AI, leader nei modelli di embedding e reranking che migliorano notevolmente la precisione attraverso la ricerca e il recupero basati sull'AI. Questa mossa non riguarda solo l'aggiunta di funzionalità di intelligenza artificiale, ma anche la ridefinizione del database per l'era dell'AI.

Perché questo è importante: il futuro dell'AI si basa su una maggiore pertinenza e accuratezza dei dati

L'AI è probabilistica: non viene sviluppata come i software tradizionali con regole e logiche predefinite. Al contrario, genera risposte o intraprende azioni in base a come il modello di AI è stato addestrato e ai dati recuperati. Tuttavia, a causa della natura probabilistica della tecnologia, l'AI può generare allucinazioni. Le allucinazioni sono una conseguenza diretta di un recupero scarso o impreciso: quando l'AI non ha accesso ai dati corretti, genera informazioni plausibili ma errate. Si tratta di un ostacolo importante all'adozione dell'AI, specialmente nelle aziende e per i casi d'uso mission-critical dove la precisione è imprescindibile.

Questo rende il recupero dei dati più rilevanti essenziale per le applicazioni AI, al fine di fornire risultati di alta qualità e contestualmente accurati. Oggi, gli sviluppatori si affidano a un mosaico di componenti separati per creare applicazioni basate sull'AI. Scelte non ottimali di questi componenti, come i modelli di embedding, possono portare a un recupero dei dati di bassa pertinenza e alla generazione di output di bassa qualità. Questo approccio frammentato è complesso, costoso, inefficiente e macchinoso per gli sviluppatori.

Con Voyage AI, MongoDB risolve questa sfida rendendo la ricerca e il recupero basati sull'intelligenza artificiale nativi al database. Invece di implementare soluzioni alternative o gestire sistemi separati, gli sviluppatori possono generare embedding di alta qualità dai dati operativi in tempo reale, memorizzare vettori, eseguire ricerche semantiche e perfezionare i risultati, tutto all'interno di MongoDB. Questo elimina la complessità e offre una maggiore precisione, una minore Latency e un'esperienza semplificata per gli sviluppatori.

In che modo Voyage AI migliora MongoDB

Voyage AI ha messo insieme un team di ricerca sull'AI di alto livello, con membri formatisi in istituti come Stanford, MIT, UC Berkeley e Princeton, ed è rapidamente diventato un leader nel recupero ad alta precisione basato sull'AI. La loro tecnologia è già stata scelta da alcune delle startup di AI più avanzate, tra cui Anthropic, LangChain, Harvey e Replit.

In particolare, i modelli di embedding di Voyage AI sono i modelli zero-shot più apprezzati nella comunità Hugging Face. I modelli di Voyage AI sono progettati per migliorare la qualità dell'output generato da:

• Migliorare la ricerca vettoriale creando embedding che catturano meglio il significato attraverso testi, immagini, PDF e dati strutturati.

• Migliorare l'accuratezza del recupero attraverso modelli avanzati di reranking che affinano i risultati di ricerca per applicazioni basate sull'AI.

• Abilitare l'AI specifica per il dominio con modelli perfezionati e ottimizzati per diversi settori, come financial services, sanità e diritto, e per casi d'uso come la generazione di codice.

Integrando le capacità di recupero di Voyage AI in MongoDB, stiamo aiutando le organizzazioni a sviluppare applicazioni di AI con maggiore precisione e affidabilità, senza inutili complessità.

In che modo Voyage AI sarà integrato in MongoDB

Stiamo integrando Voyage AI con MongoDB in tre fasi. Nella prima fase, i modelli di embedding testuale, embedding multimodale e reranking di Voyage AI rimarranno ampiamente disponibili tramite le attuali API di Voyage AI e tramite i marketplace di AWS e Azure, garantendo agli sviluppatori di continuare a utilizzare le loro migliori funzionalità di embedding e reranking. Investiremo anche nella scalabilità e nella prontezza aziendale della piattaforma per supportare l'aumento dell'adozione dei modelli di Voyage AI.

Successivamente, integreremo in maniera fluida le capacità di Voyage AI in MongoDB Atlas, iniziando con un servizio di embedding automatico per Vector Search, che gestirà automaticamente la generazione di embedding. Seguirà il reranking nativo, consentendo agli sviluppatori di migliorare immediatamente l'accuratezza del recupero. Abbiamo anche intenzione di espandere le funzionalità di AI specifiche del dominio per supportare meglio diversi settori (ad esempio, financial services, settore giuridico, ecc.) o casi d'uso (ad esempio, generazione di codice).

Infine, ottimizzeremo il recupero basato sull'AI con capacità multimodali migliorate, consentendo un recupero e una classificazione perfetti di testo, immagini e video. Abbiamo anche in programma di introdurre modelli ottimizzati tramite istruzioni, che consentano agli sviluppatori di perfezionare il comportamento di ricerca utilizzando semplici prompt anziché complessi lavori di ottimizzazione. Questo processo sarà completato dall'integrazione della gestione del ciclo di vita in MongoDB Atlas, garantendo aggiornamenti continui e ottimizzazione in tempo reale per le applicazioni AI.

Cosa significa questo per gli sviluppatori e le imprese

Le applicazioni basate sull'AI necessitano di più di un database che si limiti a memorizzare, elaborare e conservare i dati: hanno bisogno di un database che migliori attivamente la precisione del recupero, che scali senza difficoltà e che elimini gli attriti operativi. Con Voyage AI, MongoDB ridefinisce i requisiti dei database per supportare applicazioni mission-critical basate sull'AI.

Gli sviluppatori non avranno più bisogno di gestire API di embedding esterne, archivi vettoriali autonomi o pipeline di ricerca complesse. Il recupero basato sull'AI sarà integrato nel database stesso, rendendo la ricerca semantica, il recupero vettoriale e il posizionamento fluidi come le query tradizionali.

Per le aziende, ciò si traduce in un tempo di realizzazione più veloce e in una maggiore fiducia nella scalabilità delle applicazioni AI. Fornendo risultati di alta qualità su larga scala, le aziende possono integrare senza problemi l'AI nei loro casi d'uso più critici, garantendo affidabilità, prestazioni e impatto nel mondo reale.

Uno sguardo al futuro: cosa succederà dopo

Questo è solo l'inizio. La nostra visione è fare di MongoDB il database più potente e intuitivo per le applicazioni moderne basate sull'AI.

• I modelli di Voyage AI saranno presto disponibili nativamente in MongoDB Atlas.

• Continueremo a sviluppare le funzionalità di recupero basate sull'AI di MongoDB, rendendole più intelligenti, adattabili e in grado di gestire una gamma più ampia di tipi di dati e casi d'uso.

Seguici per ulteriori dettagli su come puoi iniziare a utilizzare le funzionalità di Voyage AI in MongoDB.

Per saperne di più su come MongoDB e Voyage AI stanno potenziando la ricerca e il recupero basati sull'AI per la creazione, la scalabilità e l'implementazione di applicazioni intelligenti, visita la nostra pagina del prodotto.

]]> Mon, 24 Feb 2025 14:18:00 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-it https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-it L’IA transforme les secteurs, redéfinit l’expérience client et modifie la manière dont les entreprises innovent, fonctionnent et gèrent la concurrence. Si les modèles d’avant-garde concentrent l’essentiel de l’attention, les données n’en demeurent pas moins un enjeu fondamental : de part leur façon d'être stockées, récupérées et préparées à l'usage pour les applications d’IA. La démocratisation des logiciels basés sur l’IA dépend de la capacité à s’appuyer sur les bonnes abstractions. Pourtant, aujourd’hui, la création d’applications d’IA utiles et en temps réel n’est pas à la portée de toutes les organisations.

Le défi n’est pas seulement la complexité, mais aussi la confiance. Les modèles d’IA sont probabilistes, ce qui signifie que leurs résultats ne sont ni déterministes ni prévisibles. Le problème de l’hallucination dans les chatbots actuels le montre clairement et devient encore plus critique avec l’essor des agents, où les systèmes d'IA prennent des décisions autonomes. Les équipes de développement doivent pouvoir contrôler, modeler et rectifier les résultats générés afin de les aligner sur leurs objectifs et en garantir l’exactitude.

La recherche et la récupération basées sur l’IA sont des outils puissants qui permettent d’extraire des données contextuelles pertinentes à partir de sources spécifiques, augmentant les modèles d’IA pour générer des réponses fiables et précises ou prendre des mesures responsables et sûres, comme le montre l’approche de la génération augmentée par récupération (RAG). La récupération basée sur l’IA repose sur deux composants clés de l’IA, la génération d’embeddings et le reclassement, qui permettent de capturer la signification sémantique des données et d’évaluer la pertinence des requêtes et des résultats. Nous pensons que la génération d’embeddings et le reclassement, de même que la recherche basée sur l’IA, doivent se situer au niveau de la couche de base de données de façon à simplifier la pile et à créer une base plus fiable pour les applications d’IA. En intégrant davantage d’intelligence dans la base de données, nous aidons les entreprises à réduire les hallucinations, à accroître la fiabilité et à exploiter pleinement le potentiel de l’IA à l’échelle.

Les applications les plus percutantes nécessitent un socle de données flexible, intelligent et évolutif. C’est pourquoi nous sommes ravis d’annoncer l’acquisition de Voyage AI, un leader des modèles d’embedding et de reclassement qui améliorent considérablement la précision grâce à la recherche et à la récupération basées sur l’IA. Cette action ne consiste pas seulement à ajouter des capacités d’IA, mais à redéfinir la base de données pour l’ère de l’IA.

Pourquoi est-ce important ? L’avenir de l’IA repose sur une meilleure pertinence et précision des données

L’IA est probabiliste : elle n’est pas construite comme un logiciel traditionnel avec des règles et une logique prédéfinies. Au lieu de cela, elle génère des réponses ou prend des mesures en fonction de la manière dont le modèle d’IA est entraîné et les données récupérées. Cependant, en raison de la nature probabiliste de la technologie, l’IA peut halluciner. Les hallucinations sont une conséquence directe d’une récupération de données mal conçue ou imprécise. Lorsque l’IA n’a pas accès aux données appropriées, elle génère des informations plausibles mais incorrectes. Il s’agit d’un obstacle majeur à l’adoption de l’IA, notamment dans les entreprises et pour les cas d’utilisation critiques où la précision est un impératif.

Par conséquent, pour que les applications d’IA fournissent des résultats de haute qualité et contextuellement précis, il est essentiel de récupérer les données les plus pertinentes. Aujourd’hui, les développeurs s’appuient sur un ensemble de composants distincts pour créer des applications basées sur l’IA. Un choix sous-optimal de ces composants, tels que les modèles d’embedding, peut entraîner une récupération de données peu pertinente et des résultats générés de faible qualité. Cette approche fragmentée est complexe, coûteuse, inefficace et fastidieuse pour les développeurs.

Avec Voyage AI, MongoDB résout ce défi en intégrant de façon native la recherche et la récupération assistées par l’IA à la base de données. Au lieu d’implémenter des solutions de contournement ou de gérer des systèmes distincts, les développeurs peuvent générer des embeddings de haute qualité à partir de données opérationnelles en temps réel, stocker des vecteurs, effectuer une recherche sémantique et affiner les résultats, le tout dans MongoDB. Cette approche élimine la complexité, augmente la précision, réduit la latence et offre une expérience de développement simplifiée.

Ce que Voyage AI apporte à MongoDB

Voyage AI a constitué une équipe de recherche en IA de renommée mondiale rassemblant des universitaires de Stanford, du MIT, de l’UC Berkeley et de Princeton. L’entreprise est rapidement devenue un leader de la récupération de haute précision assistée par l’IA. Sa technologie est déjà adoptée par certaines des startups les plus avancées en IA, notamment Anthropic, LangChain, Harvey et Replit.

En particulier, les modèles d’embedding de Voyage AI sont les modèles zero-shot les mieux notés de la communauté Hugging Face. Les modèles de Voyage AI sont conçus pour améliorer la qualité des résultats générés par :

• Amélioration de la recherche vectorielle en créant des embeddings qui capturent mieux le sens du texte, des images, des PDF et des données structurées.

• Amélioration de la précision de la récupération grâce à des modèles de reclassement avancés qui affinent les résultats de recherche pour les applications alimentées par l’IA.

• IA spécifique au domaine avec des modèles ajustés et optimisés pour divers secteurs comme les services financiers, la santé et le droit, ainsi que pour des cas d’utilisation comme la génération de code.

En intégrant les capacités de récupération de Voyage AI dans MongoDB, nous aidons les organisations à créer plus facilement des applications d’IA avec une plus grande précision et fiabilité, sans complexité superflue.

Comment Voyage AI sera intégré dans MongoDB

L’intégration de Voyage AI dans MongoDB se déroulera en trois phases. Dans la première phase, les modèles d’embedding de texte, d’embedding multimodal et de reclassement de Voyage AI resteront largement disponibles via les API actuelles de Voyage AI et les places de marché AWS et Azure, garantissant ainsi aux développeurs de pouvoir continuer à utiliser leurs fonctionnalités d’embedding et de reclassement de qualité. Nous investirons également dans l’évolutivité et la préparation de la plateforme à l’entreprise afin de soutenir l’adoption des modèles de Voyage AI.

Ensuite, nous intégrerons de manière fluide les fonctionnalités de Voyage AI dans MongoDB Atlas, en commençant par un service d’embedding automatisé pour Vector Search, qui assurera la génération automatique d’embeddings. Des fonctions natives de reclassement suivront, permettant aux développeurs d’améliorer instantanément la précision de la récupération. Nous prévoyons également d’étendre les capacités d’IA spécifiques à un domaine pour mieux soutenir divers secteurs (services financiers, juridiques, etc.) ou cas d’utilisation (p. ex., génération de code).

Enfin, nous allons faire progresser la récupération assistée par l’IA avec des capacités multimodales améliorées, permettant une récupération et un classement fluides de textes, d’images et de vidéos. Nous prévoyons également d’introduire des modèles réglés par instructions, permettant aux développeurs d’affiner le comportement de recherche à l’aide de prompts simples au lieu d’un réglage complexe. Cela sera complété par l’intégration de la gestion du cycle de vie dans MongoDB Atlas, assurant des mises à jour continues et une optimisation en temps réel pour les applications d’IA.

Implications pour les développeurs et les entreprises

Les applications alimentées par l’IA nécessitent plus qu’une simple base de données qui stocke, traite et conserve les données ; elles ont besoin d’une base de données qui améliore activement la précision de la récupération, assure une répartition transparente et élimine les frictions opérationnelles. Avec Voyage AI, MongoDB redéfinit ce qui est nécessaire pour qu’une base de données soutienne des applications critiques alimentées par l’IA.

Les développeurs n’auront plus besoin de gérer des API d’embedding externes, des magasins de vecteurs autonomes ou des pipelines de recherche complexes. La récupération par IA sera intégrée directement dans la base de données, rendant la recherche sémantique, la récupération vectorielle et le classement aussi fluides que les requêtes traditionnelles.

Pour les entreprises, cela signifie un délai de rentabilisation plus rapide et une plus grande confiance dans la mise à l’échelle des applications d’IA. En fournissant des résultats de haute qualité à l’échelle, les entreprises peuvent intégrer l’IA de manière transparente dans leurs cas d’utilisation les plus critiques, garantissant ainsi la fiabilité, la performance et un impact sur le monde réel.

Ce que nous prévoyons ensuite

Ce n’est que le début. Notre vision est de faire de MongoDB la base de données la plus puissante et intuitive pour les applications modernes alimentées par l’IA.

• Les modèles de Voyage AI seront bientôt disponibles de façon native dans MongoDB Atlas.

• Nous continuerons à faire évoluer les capacités d’extraction d’IA de MongoDB, en les rendant plus intelligentes, plus adaptables et capables de gérer un plus large éventail de types de données et de cas d’utilisation.

Restez à l’écoute pour plus de détails sur la façon dont vous pouvez commencer à utiliser les capacités de Voyage AI dans MongoDB.

Pour en savoir plus sur la manière dont MongoDB et Voyage AI optimisent les fonctionnalités de recherche et de récupération basées sur l’IA pour créer, mettre à l’échelle et déployer des applications intelligentes, consultez notre page produit.

]]> Mon, 24 Feb 2025 14:18:00 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-fr https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-fr La IA está transformando las industrias, redefiniendo las experiencias de los clientes y cambiando cómo las empresas innovan, operan y compiten. Aunque gran parte de la atención se centra en los modelos de vanguardia, un desafío fundamental radica en los datos: cómo se almacenan, se recuperan y se hacen útiles para las aplicaciones de IA. La democratización del software impulsado por IA depende de construir sobre las abstracciones correctas; sin embargo, hoy en día, crear aplicaciones de IA útiles y en tiempo real a escala no es factible para la mayoría de las organizaciones.

El desafío no es solo la complejidad, sino la confianza. Los modelos de IA son probabilísticos, lo que significa que sus resultados no son deterministas ni predecibles. Esto queda fácilmente en evidencia en el problema de las alucinaciones en los chatbots actuales, y se vuelve aún más crítico con el auge de los agentes, donde los sistemas de IA toman decisiones autónomas. Los equipos de desarrollo necesitan la capacidad de controlar, dar forma y fundamentar los resultados generados para alinearse con sus objetivos y garantizar la precisión.

La búsqueda y recuperación impulsadas por IA es una herramienta potente que extrae datos contextuales relevantes de fuentes específicas, mejorando los modelos de IA para generar respuestas confiables y precisas o tomar acciones responsables y seguras, como se observa en el destacado enfoque de generación aumentada por recuperación (RAG). En el núcleo de la recuperación impulsada por IA están la generación de incrustación y reclasificación, dos componentes clave de la IA que capturan el significado semántico de los datos y evalúan la relevancia de las consultas y los resultados. Creemos que la generación de incrustación y reclasificación, así como la búsqueda potenciada por IA, deben integrarse en la capa de base de datos, simplificando la arquitectura y creando una base más confiable para las aplicaciones de IA. Al incorporar más inteligencia en la base de datos, ayudamos a las empresas a mitigar las alucinaciones, mejorar la confiabilidad y liberar todo el potencial de la IA a gran escala.

Las aplicaciones más impactantes requieren una base de datos flexible, inteligente y escalable. Por eso nos complace anunciar la adquisición de Voyage AI, un líder en modelos de incrustación y reclasificación que mejoran drásticamente la precisión mediante la búsqueda y recuperación impulsadas por IA. Este movimiento no solo se trata de agregar capacidades de IA, sino de redefinir la base de datos para la era de la IA.

Por qué esto importa: El futuro de la IA se construye sobre una mejor relevancia y precisión en los datos

La IA es probabilística; no se construye como el software tradicional con reglas y lógica predefinidas. En su lugar, genera respuestas o toma medidas basándose en cómo se entrena el modelo de IA y qué datos se recuperan. Sin embargo, debido a la naturaleza probabilística de la tecnología, la IA puede tener alucinaciones. Las alucinaciones son una consecuencia directa de una recuperación deficiente o imprecisa: cuando la IA no tiene acceso a los datos correctos, genera información plausible, pero incorrecta. Esta es una barrera crítica para la adopción de la IA, especialmente en las empresas y para casos de uso críticos donde la precisión no es negociable.

Esto hace que la recuperación de los datos más relevantes sea esencial para que las aplicaciones de IA ofrezcan resultados de alta calidad y precisos en su contexto. Hoy en día, los desarrolladores dependen de un conjunto de componentes separados para desarrollar aplicaciones basadas en IA. Las elecciones subóptimas de estos componentes, como los modelos de incrustación, pueden resultar en una recuperación de datos de baja relevancia y en la generación de resultados de baja calidad. Este enfoque fragmentado es complejo, costoso, ineficiente y engorroso para los desarrolladores.

Con Voyage AI, MongoDB resuelve este desafío al hacer que la búsqueda y recuperación impulsadas por IA sean nativas de la base de datos. En lugar de implementar soluciones alternativas o gestionar sistemas separados, los desarrolladores pueden generar incrustaciones de alta calidad a partir de datos operativos en tiempo real, almacenar vectores, realizar búsquedas semánticas y refinar resultados, todo dentro de MongoDB. Esto elimina la complejidad y proporciona una mayor precisión, menor latencia y una experiencia de desarrollador optimizada.

Lo que Voyage AI aporta a MongoDB

Voyage AI ha formado un equipo de investigación de IA de clase mundial con sede en Stanford, MIT, UC Berkeley y Princeton, y se ha convertido rápidamente en un líder en la recuperación de IA de alta precisión. Su tecnología ya cuenta con la confianza de algunas de las startups de IA más avanzadas, incluidas Anthropic, LangChain, Harvey y Replit.

En particular, los modelos de incrustación de Voyage AI son los modelos zero-shock mejor calificados en la comunidad Hugging Face. Los modelos de Voyage AI están diseñados para mejorar la calidad del resultado generado:

• Al mejorar la búsqueda vectorial creando incrustaciones que capturen mejor el significado en texto, imágenes, PDF y datos estructurados.

• Al mejorar la precisión de recuperación mediante modelos avanzados de reclasificación que optimizan los resultados de búsqueda para aplicaciones basadas en IA.

• Al habilitar la IA específica del dominio con modelos ajustados y optimizados para diferentes sectores, como los servicios financieros, la atención médica y el derecho, y casos de uso, como la generación de código.

Al integrar las capacidades de recuperación de Voyage AI en MongoDB, estamos ayudando a las organizaciones a crear aplicaciones de IA con mayor precisión y confiabilidad, sin complejidad innecesaria.

Cómo se integrará Voyage AI en MongoDB

Estamos integrando Voyage AI con MongoDB en tres fases. En la primera fase, los modelos de incrustación de texto, incrustación multimodal y reclasificación de Voyage AI seguirán estando ampliamente disponibles a través de las API actuales de Voyage AI y a través de los mercados de AWS y Azure, asegurando que los desarrolladores puedan continuar utilizando sus capacidades de incrustación y reclasificación de clase mundial. También invertiremos en la escalabilidad y la preparación empresarial de la plataforma para apoyar la mayor adopción de los modelos de Voyage AI.

Luego, integraremos sin problemas las capacidades de Voyage AI en MongoDB Atlas, comenzando con un servicio de incrustación automática para Vector Search, que manejará la generación de incrustaciones automáticamente. La reclasificación nativa seguirá, permitiendo a los desarrolladores mejorar instantáneamente la precisión de la recuperación. También tenemos previsto ampliar las capacidades de IA específicas de cada dominio para ofrecer un mejor soporte a diferentes sectores (por ejemplo, servicios financieros, legales, etc.) o casos de uso (por ejemplo, generación de código).

Finalmente, avanzaremos en la recuperación impulsada por IA con capacidades multimodales mejoradas, permitiendo una recuperación y clasificación fluida de texto, imágenes y videos. También planeamos introducir modelos ajustados a las instrucciones, lo que permitirá a los desarrolladores refinar el comportamiento de búsqueda mediante indicaciones simples en lugar de un ajuste complejo. Esto se complementará mediante la integración de la gestión del ciclo de vida en MongoDB Atlas, asegurando actualizaciones continuas y optimización en tiempo real para aplicaciones de IA.

Qué significa esto para los desarrolladores y las empresas

Las aplicaciones impulsadas por IA necesitan más que una base de datos que solo almacene, procese y persista datos: necesitan una base de datos que mejore activamente la precisión de recuperación, escale sin problemas y elimine la fricción operativa. Con Voyage AI, MongoDB redefine lo que se requiere para que una base de datos sustente aplicaciones críticas impulsadas por IA.

Los desarrolladores ya no necesitarán gestionar API de incrustación externas, almacenes de vectores independientes o flujos de búsqueda complejos. La recuperación de IA se integrará en la base de datos misma, haciendo que la búsqueda semántica, la recuperación de vectores y la clasificación sean tan fluidas como las consultas tradicionales.

Para las empresas, esto se traduce en un tiempo más rápido para obtener valor y una mayor confianza al escalar aplicaciones de IA. Al proporcionar resultados de alta calidad a gran escala, las empresas pueden integrar a la perfección la IA en sus casos de uso más críticos, asegurando la confiabilidad, el rendimiento y el impacto en el mundo real.

Con miras al futuro: qué sigue

Esto es solo el comienzo. Nuestra visión es convertir a MongoDB en la base de datos más potente e intuitiva para aplicaciones modernas impulsadas por IA.

• Los modelos de Voyage AI pronto estarán disponibles de forma nativa en MongoDB Atlas.

• Continuaremos evolucionando con las capacidades de recuperación de IA de MongoDB, haciéndolas más inteligentes, más adaptables y capaces de manejar una gama más amplia de tipos de datos y casos de uso.

Manténgase pendiente para más detalles sobre cómo puede comenzar a utilizar las capacidades de Voyage AI en MongoDB.

Para obtener más información sobre cómo MongoDB y Voyage AI están impulsando la búsqueda y recuperación de IA de última generación para construir, escalar e implementar aplicaciones inteligentes, visite nuestra página de productos.

]]> Mon, 24 Feb 2025 14:18:00 GMT https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-la https://www.mongodb.com/blog/post/redefining-database-ai-why-mongodb-acquired-voyage-ai-la