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MongoDB Atlas Expands Cloud Availability to Mexico
March 24, 2025
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MongoDB Atlas is Now Available as a Microsoft Azure Native Integration
Since 2019, MongoDB and Microsoft Azure have striven to make it easy for enterprises to launch cutting-edge, modern applications. Key to this effort—and to enabling organizations everywhere to make an impact with AI—has been our work integrating MongoDB Atlas with the Microsoft Intelligent Data Platform. Our aim is to give developers a streamlined, fully integrated experience that they’ll love to use. So I’m very happy to announce the public preview of MongoDB Atlas as an Azure Native Integration (ANI). This latest step in MongoDB’s collaboration with Microsoft means that enterprise customers will be able to easily create and manage MongoDB Atlas organizations while also consolidating billing for Atlas within the Azure console, empowering them to interact with MongoDB Atlas as if it were a first-party service from Azure. I am also pleased to announce MongoDB Atlas on Azure Service Connector, one of several new integrations set to follow directly from the MongoDB Atlas as an Azure Native Integration announcement. Azure Service Connector makes it easy for developers to securely connect Azure compute services to backing services like databases, now including MongoDB Atlas. MongoDB’s mission has always been to empower our customers to move fast with data. With MongoDB Atlas as a native integrated service to Azure, we’re unlocking new possibilities for organizations to harness real-time insights, scale globally, and to accelerate their AI-driven roadmaps—all while reducing operational overhead. With Azure’s robust ecosystem of AI and analytics tools, teams can build and innovate with greater confidence, ultimately transforming how they serve their customers and shaping the future of software. "Integrating MongoDB Atlas as a Microsoft Azure Native Integration marks a significant milestone in our partnership with MongoDB. This integration empowers our customers to seamlessly manage their MongoDB Atlas resources within the Azure ecosystem, including unified billing and robust security features,” said Sandy Gupta, Vice President, Global Software Companies Ecosystem, Microsoft. “By simplifying operations and reducing technical complexity, we are enabling organizations to innovate faster and deliver exceptional value to their customers." Why this matters: Accelerated development & seamless operations This streamlined approach reduces technical and organizational complexity, with organizations benefiting from integrated billing, consolidated support, and simplified deployment. Connecting a database platform to external services typically requires juggling multiple portals, credentials, and security configurations. Starting today, with MongoDB Atlas as an Azure Native Integration, organizations can: Create and manage Atlas organizations directly within Azure, including the Azure Portal UI and CLI/SDK/ARM. Enjoy consolidated billing for both Azure and MongoDB Atlas. Access Azure’s AI services, data analytics, and more—all while harnessing the flexible, scalable power of MongoDB Atlas. It’s worth dwelling for a minute on the simplified onboarding and billing component of ANI, one of the biggest benefits of this integration for customers. As an Azure Native Integration, users can create their MongoDB Atlas organization and select their company billing plan directly from Azure, automatically applying the Azure billing plan to the Atlas Organization. This is made possible by leveraging Azure's comprehensive suite of billing and cost management tools, providing enterprises with enhanced control and visibility over their expenditures. Benefits of using MongoDB Atlas and Microsoft Azure together This latest MongoDB Atlas integration on Azure builds on a strong foundation of technical collaboration. Together, MongoDB Atlas on Azure already delivers a powerful set of integrations that offer customers and development teams a wide range of benefits, including: Unified workloads: MongoDB Atlas offers a single platform that supports a range of workloads, from transactional, time series, and search, to real-time analytics. With native integration on Azure, teams can quickly build across a wide variety of data-driven use cases. This can range from e-commerce transactions to generative AI applications, all without any re-architecting. Streamline AI integration: Accelerate machine learning (ML) workflows and generative AI projects with minimal configuration. Organizations can connect to Azure AI Foundry, Azure OpenAI Service, Microsoft Fabric, or Azure Databricks for advanced analytics, and MongoDB Atlas automatically scales in response to dynamic workloads. End-to-end security and compliance: MongoDB Atlas integrates with Microsoft Entra ID (formerly Azure AD), Azure Key Vault, and Azure private link for secure single sign-on, encryption key management, and private networking, respectively. With Microsoft Purview, organizations can meet stringent governance and compliance requirements, and teams remain agile without sacrificing enterprise-grade security. Scalability and global footprint: Azure’s extensive regional coverage enables organizations to deploy MongoDB Atlas in 40+ Azure regions worldwide. This ensures data remains close to users for low-latency, high-performance applications. How to deploy MongoDB Atlas as an Azure Native Integration 1. Search for MongoDB Atlas in the Azure Portal and the Azure Marketplace. 2. Create a MongoDB Atlas Organization and choose an Azure billing plan. That’s it! You’ve successfully created an Atlas Organization. From your new Atlas Organization, you can start taking advantage of other Azure services already integrated into MongoDB Atlas: Configure security and network settings using existing Azure Virtual Networks and Azure Private Link, as required. Begin building AI capabilities into applications by connecting to Azure AI Foundry, Azure Databricks, or Microsoft Fabric. Get started with deploying MongoDB Atlas as an Azure Native Integration through our quick start guide .
Introducing Database Digest: Building Foundations for Success
Today at MongoDB .local Toronto , I’m excited to share the first issue of Database Digest —MongoDB’s new digital magazine that explores the critical role of data in powering modern applications. This inaugural issue explores modern data architecture, and shows how—when the right data foundation meets emerging technologies—pioneering companies are fundamentally reimagining what's possible. 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. Figure 1. Modern database demands. 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. Visit mongodb.com/library/database-digest to download your copy and join us on this journey into the future of data.
What’s New From MongoDB at Google Cloud Next 2025
At Google Cloud Next '25, MongoDB is excited to celebrate a deepening collaboration with Google Cloud, focused on delivering cutting-edge solutions that empower developers, enterprises, and startups alike. 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. Stop by Booth #1240 at Google Cloud Next or join one of MongoDB’s featured sessions to explore these advancements and discover how MongoDB and Google Cloud are shaping the future of AI and data-driven applications. Head over to our MongoDB Atlas Learning Hub to boost your MongoDB skills.
Announcing the 2025 MongoDB PhD Fellowship Recipients
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.
Redefining the Database for AI: Why MongoDB Acquired Voyage AI
This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . 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 .
2024 William Zola Award for Community Excellence Recipient
We are thrilled to announce the 2024 recipient of MongoDB’s prestigious William Zola Award for Community Excellence: Mateus Leonardi! Each year, the William Zola Award for Community Excellence recognizes an outstanding community contributor who embodies the legacy of William Zola, a lead technical services engineer at MongoDB who passed away in 2014. Zola had an unwavering commitment to user success, and believed deeply in understanding and catering to users' emotions while resolving their technical problems. His philosophy is a guiding force for MongoDB’s community to this day. This award comes with a cash prize and travel sponsorship to attend a regional MongoDB.local event . Mateus Leonardi embodies these leadership values within the MongoDB community. In 2023, he led the launch and leadership of a new MongoDB User Group (MUG) in Florianópolis/Santa Catarina, Brazil. With his co-leaders, he has organized six successful MUG events, and has formed partnerships with local organizations and universities. He has also expanded his impact by becoming a MongoDB Community Creator , and has actively shared his MongoDB expertise outside of the user group. For example, in 2024 Mateus volunteered as a subject matter expert for MongoDB certifications. This involved being a panelist on several critical exam development milestones—from defining the knowledge and skills needed to earn credentials, to developing exam blueprints, to writing exam questions and verifying the technical accuracy of MongoDB certifications. Based on this record of support for our community, Mateus was also honored as a MongoDB Community Champion in 2024. Mateus excels as a dynamic leader and community advocate, extending invitations to fellow Brazilian MongoDB community leaders to speak at MUGs and local events. He also organizes and delivers talks at numerous events, including those for local university students. Ultimately, Mateus goes above and beyond in everything he commits to, and has demonstrated a genuine commitment to enhancing both the local MongoDB community and the broader developer community in his area. Here’s what Mateus Leonardi had to say about what the MongoDB community means to him: Q: Could you tell our readers a little about your day-to-day work? Mateus: As Head of Engineering at HeroSpark, my mission is to empower our team to innovate with quality and consistency. I work to create an environment where efficiency and constant evolution are natural in our day-to-day, always focusing on solutions that benefit both our team and our customers. Q: How does MongoDB support you as a developer? Mateus: MongoDB has been instrumental in our journey to enable adaptability without compromising quality. In work, MongoDB gives us that same flexibility in development, allowing us to quickly adapt to market changes while maintaining high performance and controlled costs. This combination gives us the confidence to innovate sustainably. Q: Why is being a leader in the MongoDB community important to you? Mateus: My sixteen-plus-year career has been marked by people who have helped me grow, and now it's my turn to give back. I view my role in the community as a way to multiply knowledge and create a positive impact. Technology has transformed my life, and through the MongoDB community, I can help others transform their realities too. Q: What has the community taught you? Mateus: The community has taught me that true learning happens when we share experiences. The act of teaching makes us better learners. Each interaction in the community allows us to reflect on our limitations and grow collectively. I've learned that the most rewarding thing is not the destination, but a journey shared with other developers. Congratulations to Mateus Leonardi, recipient of the 2024 William Zola Award for Community Excellence! To learn more about the MongoDB Community, please visit the MongoDB Community homepage .
MongoDB Named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems
I’m pleased to announce that MongoDB has been named a Leader in the 2024 Gartner® Magic Quadrant™ for Cloud Database Management Systems (DBMSs) for the third consecutive year. In our view, this recognition cements MongoDB’s status as the only pure-play database provider in the cloud database management system category, underscoring MongoDB’s innovation, execution, and customer-centric approach. According to Gartner, “The cloud DBMS market remains as vibrant as ever and is transforming in important ways, especially in the use of gen AI and how DBMSs interact with other data management components. This Magic Quadrant will help data and analytics leaders make the right cloud DBMS choices in this essential market.” We believe this continued recognition by Gartner is a testament to MongoDB’s commitment to serving developers, as well as the investments we’ve made in our unified platform and integrated services. Driving innovation for enterprises MongoDB's mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. 2024 was a year of innovation and accolades at MongoDB, and I’m proud to share some of its highlights: In October, we released MongoDB 8.0 , the best performing version of MongoDB yet. MongoDB 8.0 is over 30% faster than the previous version of the database, it’s more secure than ever, horizontal scaling is faster and easier (at a lower cost), and MongoDB 8.0 gives teams greater control for optimizing database performance. We also launched—and grew—the MongoDB AI Applications Program (MAAP) . With MAAP, MongoDB offers customers a full AI stack and an integrated set of professional services to help them keep pace with innovation, identify the best AI use cases, and to help them future-proof AI investments. MongoDB became a founding member of the U.S. Artificial Intelligence Safety Institute Consortium . Established by the U.S. Department of Commerce’s National Institute of Standards and Technology, the Consortium supports the development and deployment of safe and trustworthy AI. MongoDB released hundreds of features and enhancements to accelerate innovation, manage costs, and simplify building applications at scale. MongoDB was recognized as the most loved vector database in Retool’s State of AI report —for the second consecutive year. The Gartner Magic Quadrant for cloud database management systems “Gartner defines the cloud database management systems (DBMSs) market as solutions designed to store, manipulate, and persist data, primarily delivered as Software-as-a-Service (SaaS). These systems must support transactional, analytical, and hybrid workloads while enabling enterprises to innovate across multi-cloud, hybrid, and intercloud ecosystems.” 1 It’s our opinion that this recognition by Gartner is a testament to MongoDB’s strong ability to execute and support customers today, as well as MongoDB’s comprehensive product vision that positions our platform to support tomorrow's operational workloads. What is the Magic Quadrant, and what is a Leader? “A Gartner Magic Quadrant is a culmination of research in a specific market, giving you a wide-angle view of the relative positions of the market’s competitors. By applying a graphical treatment and a uniform set of evaluation criteria, a Magic Quadrant helps you quickly ascertain how well technology providers are executing their stated visions and how well they are performing against Gartner’s market view.” 2 According to Gartner, “Leaders execute well against their current vision and are well positioned for tomorrow.” Overall, Magic Quadrants can help you “get quickly educated about a market’s competing technology providers and their ability to deliver on what end-users require now and in the future.” Powering innovation at scale with MongoDB Atlas Enterprises choose MongoDB Atlas because it gives them the freedom and agility they need to succeed in a rapidly evolving digital landscape. MongoDB Atlas’s multi-cloud architecture—including availability across Amazon Web Services, Google Cloud, and Microsoft Azure—ensures customers can design for unmatched scale and resilience. By automating functions like scaling and performance optimization , and giving them the ability to leverage industry-first capabilities like MongoDB Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data), with MongoDB Atlas customers can spend less time managing infrastructure and more time delivering experiences. MongoDB Atlas’s integrated capabilities to support multi-modal data types and use cases—like full-text and vector search , stream processing , and data federation —accelerate innovation, helping enterprises quickly respond to market changes, power AI-driven insights, and deliver meaningful digital experiences to their end users—all without the burden of operational complexity. Modernizing and building for the future In our opinion, the Gartner Magic Quadrant provides organizations with a clear and accessible evaluation framework to identify solutions that fit their needs, today and tomorrow. The placement of MongoDB in the Leader quadrant for Cloud Database Management Systems—for the third year in a row!—validates the efforts MongoDB has made to help developers and organizations take advantage of their most valuable resource, their data. I talk to MongoDB customers frequently, and many say the same thing: in today’s digital-first economy, AI-powered applications and scalable data infrastructure aren’t just advantages, they’re absolute necessities. They say that the time to act is now, and they’re looking for solutions that will help them innovate, streamline, and seize the AI-driven future. And when it comes to modernizing their operations, they consistently point to MongoDB as their go-to partner. Begin your cloud journey with MongoDB Atlas today. Contact our sales team or register for a free account to begin building! And to learn how MongoDB can help accelerate your AI journey, visit the MongoDB AI Applications Program page. Footnotes Gartner, Magic Quadrant for Cloud Database Management Systems, Henry Cook, Ramke Ramakrishnan, et al., 18 December 2024 GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and MAGIC QUADRANT is a registered trademark of Gartner, Inc. and/or its affiliates and are used herein with permission. All rights reserved. Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose. 1 Gartner Peer Insights, Cloud Database Management Systems, December 2024 https://www.gartner.com/reviews/market/cloud-database-management-systems 2 Gartner Research Methodologies, Gartner Magic Quadrant, 20 December 2024 https://www.gartner.com/en/research/methodologies/magic-quadrants-research
MongoDB’s 2024 Year in Review
It’s hard to believe that another year is almost over! 2024 was a transformative year for MongoDB, and it was marked by both innovation and releases that further our commitment to empowering customers, developers, and partners worldwide. So without further ado, let’s dive into MongoDB’s 2024 highlights. We’ll also share our executive team’s predictions of what 2025 might have in store. A look back at 2024 MongoDB 8.0: The most performant version of MongoDB ever In October we released MongoDB 8.0 , the fastest, most resilient, secure, and reliable version of MongoDB yet. Architectural optimizations in MongoDB 8.0 have significantly improved the database’s performance, with 36% faster reads and 59% higher throughput for updates. Our new architecture also makes horizontal scaling cheaper and faster. Finally, working with encrypted data is easier than ever, thanks to the addition of range queries in Queryable Encryption (which allows customers to encrypt, store, and perform queries directly on data). Whether you’re a startup building your first app, or you’re a global enterprise managing mission-critical workloads, MongoDB 8.0 offers unmatched power and flexibility, solidifying MongoDB’s place as the world’s most popular document database. Learn more about what makes 8.0 the best version of MongoDB ever on the MongoDB 8.0 page . Delivering customer value with the MongoDB AI Applications Program AI applications have become a cornerstone of modern software, and MongoDB is committed to equipping customers with the technology, tools, and support they need to succeed on their AI journey. That’s why we launched the MongoDB AI Applications Program (MAAP) in 2024, a comprehensive program designed to accelerate the development of AI applications. By offering customers resources like access to AI specialists, an ecosystem of leading AI and tech companies, and AI architectural best practices supported by integrated services, MAAP helps solve customers’ most pressing business challenges, unlocks competitive advantages, and accelerates time to value for AI investments. Overall, MAAP’s aim is to set customers on the path to AI success. Visit the MongoDB AI Applications Program page or watch our session from AWS re:Invent to learn more! Advancing AI with MongoDB Atlas Vector Search In 2024, MongoDB further cemented its role in the AI space with enhancements to MongoDB Atlas Vector Search . Recognized in 2024 (for the second consecutive year!) as one of the most loved vector databases , MongoDB continues to provide a scalable, unified, and secure platform for building cutting-edge AI use cases. Recent advancements like vector quantization in Atlas Vector Search help deliver even more value to our customers, enabling them to scale applications to billions of vectors at a lower cost. Head over to our Atlas Vector Search quick start guide to get started with Atlas Vector Search today, or visit our AI resources hub to learn more about how MongoDB can power AI applications. Search Nodes: Performance at scale Search functionality is indispensable in modern applications, and with Atlas Search Nodes, organizations can now optimize their search workloads like never before. By providing dedicated infrastructure for Atlas Search and Vector Search workloads, Search Nodes ensure high performance (e.g., a 40–60% decrease in query times), scalability, and reliability, even for the most demanding use cases. As of this year , Search Nodes are generally available across AWS, Google Cloud, and Microsoft Azure. This milestone underscores MongoDB’s commitment to delivering powerful solutions that scale alongside our customers’ needs. To learn more about Search Nodes, check out our documentation or watch our tutorial . Looking ahead: MongoDB’s 2025 predictions After the excitement of the past few years, 2025 will be defined by ensuring that technology investments deliver tangible value. Organizations remain excited about the potential AI and emerging technologies hold to solve real business challenges, but are increasingly focused on maintaining a return on investment. “Enterprises need to innovate faster than ever, but speed is no longer the only measure of success. Increasingly, organizations are laser-focused on ensuring that their technology investments directly address critical business challenges and provide clear ROI and competitive advantage—whether it’s optimizing supply chains, delivering hyper-personalized customer experiences, or scaling operations efficiently,” said Sahir Azam, Chief Product Officer at MongoDB. “In 2025, I expect to see organizations make significant strides in driving this innovation and efficiency by applying AI to more production use cases and by maturing the way they leverage their data to build compelling and differentiated customer experiences.” Indeed, we expect to see organizations make more strategic investments in emerging technologies like gen AI—innovating with a sharp focus on solving business challenges. “In 2025, we can expect the focus to shift from ‘what AI can do’ to ‘what AI should do,’ moving beyond the hype to a clearer understanding of where AI can provide real value and where human judgment is still irreplaceable,” said Tara Hernandez, VP of Developer Productivity at MongoDB. “As we advance, I think we’ll see organizations begin to adopt more selective, careful applications of AI, particularly in areas where stakes are high, such as healthcare, finance, and public safety. A refined approach to AI development will be essential—not only for producing quality results but also to build trust, ensuring these tools genuinely support human goals rather than undermining them.” With more capable, accessible application development tools and customer-focused programs like MAAP at developers’ fingertips, 2025 is an opportunity to make a data-driven impact faster than ever before. "Right now, organizations have an opportunity to leverage their data to reimagine how they do business, to more effectively adapt to a changing world, and to revolutionize our quality of life,” said Andrew Davidson, SVP of Products at MongoDB. “By harnessing our latest technologies, developers can build a foundation for a transformative future." Head over to our updates page to learn more about the new releases and updates from MongoDB in 2024. Keep an eye on our events page to learn what's to come from MongoDB in 2025!
The MongoDB AI Applications Program: Delivering Customer Value
When people ask me about MongoDB, I tell them that they’ve probably interacted with MongoDB without realizing it. In fact, many of the world’s leading companies—including 70% of the Fortune 100—are powered by MongoDB. Everything we do at MongoDB is about serving our customers, but that often happens in the background, where our work is invisible to many users. In my case, that means building an ecosystem of partners who enable customer innovation. A recent example is how MongoDB teamed up with Amazon Web Services (AWS) and Amazon Bedrock to help Base39 —a Brazilian fintech provider—automate loan analysis, decreasing decision time from three days to one hour, and reducing cost per loan analysis by 96%. And there’s the Indian company IndiaDataHub, which joined the MongoDB AI Applications Program (MAAP) to access AI expertise, in-depth support, and a full spectrum of technologies to enhance AI functionality within IndiaDataHub’s analytics platform. This includes connecting relevant data in MongoDB with Meta's AI models to perform sentiment analysis on text datasets. I could go on and on—after all, tens of thousands of MongoDB’s customers have success stories like these. Enabling customer success is precisely why we launched MAAP last summer, and why the program has evolved since. Customers tell us that they want to take advantage of AI, but they’re unsure how to navigate a fast-moving market, how to control costs, and how to unlock business value from their AI investments. So with MAAP, MongoDB offers customers a full AI stack and an integrated set of professional services to help them keep pace with the latest innovations, identify the best AI use cases, and to help them future-proof AI investments. With today’s announcement , Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured have joined the 22 companies that now comprise the MAAP partner network. Which means that the MAAP ecosystem (which was founded with Accenture, Anthropic, Anyscale, Arcee AI, AWS, Cohere, Credal, Fireworks AI, Google Cloud, gravity9, LangChain, LlamaIndex, Microsoft Azure, Nomic, PeerIslands, Pureinsights, and Together AI) offers additional cutting-edge AI integration and solutions to customers—and more ways to set them on the path to AI success. CentralReach: Making an impact on autism with AI More than 150 customers have already gotten involved with MAAP, but I’m particularly excited to share the work of CentralReach . CentralReach provides an AI-powered electronic medical record (EMR) platform that is designed to improve outcomes for children and adults diagnosed with autism and related intellectual and developmental disabilities (IDD). Prior to working with MongoDB and MAAP, CentralReach was looking for an experienced partner to further connect and aggregate its more than 4 billion financial and clinical data points across its suite of solutions. CentralReach leveraged MongoDB’s document model to aggregate the company’s diverse forms of information from assessments to clinical data collection, so the company could build rich AI-assisted solutions on top of its database. Meanwhile, MAAP partners helped CentralReach to design and optimize multiple layers of its comprehensive buildout. All of this will enable CentralReach to support initiatives such as value-based outcome measurement, clinical supervision, and care delivery efficacy. With these new data layers in place, providers will be able to make substantial improvements to their clinical delivery to optimize care for all those they serve. “As a mission-driven organization, CentralReach is always looking to innovate on behalf of the clinical professionals—and the more than 350,000 autism and IDD learners—that we serve globally,” said Chris Sullens, CEO of CentralReach. “So being able to lean on MongoDBs database technology and draw on the collective expertise of the MAAP partner network—in addition to MongoDB’s tech expertise and services—to help us improve outcomes for our customers and their clients worldwide has been invaluable.” Working backward from customer needs The addition of Capgemini, Confluent, IBM, QuantumBlack, AI by McKinsey, and Unstructured to the MAAP partner network offers customers additional technology and AI support options. It also builds on MongoDB’s larger partner ecosystem , which is designed to give customers flexibility and choice. By working closely with our partners on product launches, integrations, and real-world challenges, MongoDB has been able to bring a better understanding of the challenges facing customers—and to give them the resources and confidence to move forward with groundbreaking technology like AI . Examples of support MAAP has offered customers include: Guidance on chunking strategies for an AI-native healthcare provider providing patient recommendations based on complex data sources Collaboration on advanced retrieval techniques to improve response accuracies for a large consultancy to automate manual research Evaluation of embedding models for multi-modal data stores for a well-known automaker developing diagnostic applications Guidance on architectures for complex agentic workflows for a mature enterprise technology provider augmenting customer service workflows One way we offer this support is through the MAAP Center of Excellence (CoE). The MAAP CoE comprises AI technical experts from across MongoDB and the MAAP partner ecosystem who collaborate with customers to understand their challenges, technical requirements, and timelines. The MAAP CoE can then recommend custom full-stack architectures and implementation best practices, optimized for the customer’s specific use case and requirements. Indeed, customization is intrinsic to MAAP: MongoDB and our MAAP partners will meet customers wherever they are to help them achieve their goals. For example, if an organization wants to fully own its AI application development, MongoDB and partners can provide guidance and expertise. And in cases where customers want hands-on support, we can help speed projects with professional services. Ultimately, we want MAAP customers—and anyone who works with MongoDB’s partner ecosystem at large—to feel empowered to own their application development, and to transform challenges into opportunities. Let’s build the next big thing together! To learn more about building AI-powered apps with MongoDB, see MongoDB’s AI Resources Hub , the Partner Ecosystem Catalog , or visit the MAAP page . And check out our partner Confluent’s own blog post about MAAP!
New Course for Building AI Applications with MongoDB on AWS
Developers everywhere want to expand the limits of what they can build with new generative AI technologies. But the AI market and its offerings have evolved so quickly that for many developers, keeping up can feel overwhelming. As we’ve entered the AI era, MongoDB and Amazon Web Services (AWS) have built upon our eight year partnership to deliver technology integrations—like MongoDB Atlas’s integrations with Amazon Bedrock and Amazon Q Developer (formerly CodeWhisperer)—that simplify the process of building and deploying gen AI applications. By combining MongoDB’s integrated operational and vector database capabilities with AWS’s AI infrastructure solutions, our goal is to make it easier for our developer community to innovate with AI. So, to help developers get started, we’re launching a new, free MongoDB Learning Badge focused on Building AI Applications with MongoDB on AWS . Building AI with MongoDB on AWS This is MongoDB University’s first AWS Learning Badge, and with it, we’ve focused on teaching developers how Amazon Bedrock and Atlas work together—including how to create a knowledge base in Amazon Bedrock, configure a knowledge base to use Atlas, inspect how a query is answered, create an Agent to answer questions based on data in Atlas, and configure guardrails that support responsible agentic behavior. In short, developers will learn how to remove the heavy lifting of infrastructure configuration and integration so they can get up and running with innovative new semantic search and RAG applications faster. Amazon Bedrock is a fully managed service from AWS that offers a choice of high-performing foundation models from leading AI companies via a single API, along with a broad set of capabilities organizations need to build secure, high-performing AI applications. Developers can connect Bedrock to MongoDB Atlas for blazing-fast vector searches and secure vector storage with minimal coding. With the integration, developers’ can use their proprietary data alongside industry-leading foundation models to launch AI applications that deliver hyper-intelligent and hyper-relevant results. Tens of thousands of customers are running MongoDB Atlas on AWS, and many have already embarked successfully on cutting-edge AI journeys. Take Scalestack for example, which used MongoDB Atlas Vector Search to build a RAG-powered AI copilot, named Spotlight, and is now using Bedrock’s customizable models to enhance Spotlight’s relevance and performance. Meanwhile, Base39 —a Brazilian fintech provider—used MongoDB Atlas and Amazon Bedrock to automate loan analysis, decreasing decision time from three days to one hour and reducing cost per loan analysis by 96%. Badge up with MongoDB MongoDB Learning Badges are a powerful way to demonstrate your dedication to continuous learning. These digital credentials not only validate your educational accomplishments but also stand as a testament to your expertise and skill. Whether you're a seasoned developer, an aspiring data scientist, or an enthusiastic student, earning a MongoDB badge can elevate your professional profile and unlock new opportunities in your field. Learn, prepare, and earn Complete the Learning Badge Path and pass a brief assessment to earn your badge. Upon completion, you'll receive an email with your official Credly badge and digital certificate, ready to share on social media, in email signatures, or on your resume. Additionally, you'll gain inclusion in the Credly Talent Directory, where you will be visible to recruiters from top employers. Millions of builders have been trained through MongoDB University courses—join them and get started building your AI future with MongoDB Atlas and AWS. And if you’re attending AWS re:Invent 2024, come find MongoDB at Booth #824. The first 100 people to receive their learning badge will receive a special gift! Start learning today
MongoDB is a Leader in The Forrester Wave™: Translytical Data Platforms
We’re pleased to announce that MongoDB has been recognized as a Leader in the recently released Forrester Wave™: Translytical Data Platforms, Q4 2024. The report—which highlights “Leaders, Strong Performers, Contenders, and Challengers” and is “an assessment of the top vendors in the market”—notes that “MongoDB is an excellent choice for organizations looking to enhance their document and NoSQL platforms with real-time insights by leveraging translytical capabilities.” What are translytical capabilities? So what are translytical capabilities? In short, modern applications use a growing number of data types for transactional, operational, and analytical uses. Developers can silo different data types and workloads into separate systems, but this causes architectural complexity and reduced agility for teams. A better approach—and one that speeds development—is to leverage a single platform that can store and use multiple data types for different purposes. Forrester defines these “translytical data platforms” as “next-generation data solutions built on a single database engine to seamlessly support transactional, operational, and analytical workloads without compromising data integrity, performance, or real-time analytics.” That’s why we built MongoDB Atlas as a modern database. It brings data like documents, vectors, streaming, and time-series together in one system so that you can run transactional, operational, and analytics workloads in one place. How Forrester measured translytical capabilities To measure providers, Forrester evaluated 15 of the most significant translytical data platform vendors against 26 criteria. These criteria span current offering and strategy, to market presence. Being recognized as a Leader is based on an organization’s scores in both current offering and strategy categories for criteria like vision and innovation. Forrester gave MongoDB the highest possible scores across nine criteria, including: Multimodel 1 Search Development Tools / API Scale optimization Streaming Platform management Roadmap Adoption Number of customers According to the report, “MongoDB continues to expand its translytical market share by delivering new capabilities that enhance automation, intelligent memory tiering, and multimodel support, including vector, streaming, analytics, and integrated transactions.” “Developers have been telling us for years that they need easy ways to work with all their data in one place,” said Jim Scharf, Chief Technology Officer at MongoDB. “That’s what continues to drive our strategy of making MongoDB Atlas the modern database. We’re excited to be recognized as a Leader in the new The Forrester Wave™: Translytical Data Platforms, and we will continue to support our customers’ growing needs for their data.” What are MongoDB customers doing with translytical capabilities? The Forrester report notes that organizations “use MongoDB to support real-time analytics, customer intelligence, the Internet of Things (IoT), and AI applications.” So, let’s look at a few examples in action. Companies like Ignition started using MongoDB just for operational data—but, over time, expanded into using Atlas Vector Search for AI use cases. Meanwhile, Bosch Digital makes their IoT data easier to work with by bringing multiple data sources together in a single platform. And, Keller Williams uses MongoDB Charts to bring their analytics to where their transactional data is, making it faster to gather insights for their product teams. Overall, customers are attracted to MongoDB because of how developer-friendly the platform is, and because it simplifies their lives by bringing their data together. Access your complimentary copy of The Forrester Wave™: Translytical Data Platforms, Q4 2024 here . Interested in starting your own translytical journey? Sign up for a free MongoDB Atlas account today! 1 Multimodel is defined as support for storing and using various data types.
Introducing Two MongoDB Generative AI Learning Badges
Want to boost your resume quickly? MongoDB is introducing two new Learning Badges , Building gen AI Apps and Deploying and Evaluating gen AI Apps. Unlike high-stakes certifications, which cover a large breadth and depth of subjects, these digital credentials are focused on specific topics, making them easier and quicker to earn. Best of all, they’re free! The Building Gen AI Applications with MongoDB Learning Badge validates users’ knowledge of developing gen AI applications using MongoDB Atlas Vector Search. It recognizes your understanding of semantic search and how to build chatbots with retrieval-augmented generation (RAG), MongoDB, and Langchain. The Deploying and Evaluating Gen AI Applications with MongoDB Learning Badge validates users’ knowledge of optimizing the performance and evaluating the results of gen AI applications. It recognizes your understanding of chunking strategies, performance evaluation techniques, and deployment options within MongoDB for both prototyping and production stages. Learn, prepare, and earn To earn your badge, simply complete the Learning Badge Path and take a short assessment at the end. Once you pass the short assessment, you'll receive an email with your official Credly badge and digital certificate. You can share it on social media, in email signatures, or on digital resumes. Additionally, you'll gain inclusion in the Credly Talent Directory , where you will be visible to recruiters from top employers and can open up new career opportunities. Learning paths are like curated roadmaps that guide you through essential concepts and skills needed for the assessment. Each badge has its own learning path: Building Gen AI Apps Learning Badge Path: This learning path guides you through the foundations of building a gen AI application with MongoDB Atlas Vector Search. You'll learn what semantic search is and how you can leverage it across a variety of use cases. Then you'll learn how to build your own chatbot by creating a RAG application with MongoDB and Langchain. Deploying and Evaluating Gen AI Apps Learning Badge Path: This learning path will help you take a gen AI application from creation to full deployment, with a focus on optimizing performance and evaluating results. You'll explore chunking strategies, performance evaluation techniques, and deployment options in MongoDB for both prototyping and production stages. We recommend completing the Building gen AI Apps Learning Badge Path before beginning this path. Badge up with MongoDB MongoDB Learning Badges offer a valuable opportunity to showcase your commitment to continuous learning and expertise in specific topics. These digital credentials not only recognize your educational achievements but also serve as a testament to your knowledge and skills. Whether you're a seasoned developer, an aspiring data scientist, or an enthusiastic student, earning a MongoDB badge can significantly enhance your profile and open up new opportunities in your field. Start earning your badges today—it’s quick, effective, and free! Visit MongoDB Learning Badges to begin your journey toward becoming a gen AI application expert and boosting your career prospects.