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MongoDB Announces Leadership Transition
November 3, 2025
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Thesys Builds the Future of Generative UI on MongoDB Atlas
As generative AI is reshaping the entire user experience of how software is built and used, developers are constantly seeking new types of infrastructure platforms—ones that will make it seamless to build, manage, and even scale intelligent applications.
Secure MongoDB Atlas With Automated IP Access List Policies
Editor’s note: This post is the first in a four-part series exploring how MongoDB Atlas Resource Policies help you strengthen database security and enforce consistent guardrails across your organization. Future posts will cover topics like defense-in-depth, blocking wildcard IP access, and restricting access by cloud provider or region.
Enterprise-Level, Scalable AI with Morphik and MongoDB
As AI continues to revolutionize how large enterprises operate, the most crucial startups are those capable of turning massive amounts of unstructured information into actionable intelligence. Morphik, one of the fastest-growing AI knowledge platforms for enterprises, uses MongoDB to deliver secure, high-performance, multitenant systems that power real-world automation at scale.
Revolutionize Asset Maintenance with MongoDB and MaintainX
We’re excited to announce that MongoDB and MaintainX are joining forces to help manufacturers achieve excellence in maintenance operations. This joint solution enables a digital thread from raw production data to maintenance execution.
Automated, Secure, and Scalable: Reference Implementation for Atlas on Azure
Today’s cloud-driven world is characterized by the requirement to scale—and to do so massively and faster than ever. But standing up secure and compliant environments on the cloud is complex and requires huge efforts, as teams have to figure out networking, policies, identity, and governance requirements across numerous projects.
Unlocking Financial Services Document Intelligence with Agentic AI and MongoDB
Driven by rising customer expectations and the demand for greater efficiency, accuracy, and agility, the financial services industry is undergoing a profound transformation. Gone are the days of painstaking manual document reviews, and welcome instead to the era of agentic AI, where intelligent systems and a robust data foundation redefine how financial data is processed and understood. Powered by MongoDB’s flexible, scalable platform, organizations can seamlessly manage multimodal data to unlock insights, automate workflows, and stay ahead in this evolving landscape.
Cars24 Improves Search For 300 Million Users With MongoDB Atlas
The Indian multinational online car marketplace Cars24 serves 300 million users globally. The company offers services that span sales, insurance, maintenance, financing, and more, reshaping the entire car ownership journey. Speaking at MongoDB .local Bengaluru in July 2025 , Pradeep Sharma, Head of Technology at Cars24, shared how MongoDB has been a key driver of Car24’s digital transformation journey. Specifically, he highlighted two recent use cases that show how MongoDB Atlas has helped Cars24 scale, improve its search capabilities, and reduce its architectural complexity. Matching the growing scale with simplified and expanded search Cars24 has operations in multiple countries, and a diverse customer base. Over the years, the company has used customer data, behavior analytics, and operational workflows to build, evolving from being a platform for buying and selling cars, to an end-to-end ecosystem, supported by a hub of interconnected systems. At the start of its journey, Cars24 relied on legacy databases for managing and searching data, such as Postgres. Their relational database set-up would store information, synchronize the data to a separate “bolt-on” search engine (such as Elasticsearch), manually indexing it, and then querying the index. While initially effective for a small application ecosystem, these processes became bottlenecked as the organization’s services grew. Multiple engineering teams piped data into a single search index, which often resulted in synchronization challenges and overwhelming administrative overhead. Cars24 faced three core limitations with this setup: Lower developer productivity: Exponential effort was spent maintaining pipelines and synchronizing procedures. Developers had little bandwidth for building business features or innovation. Architectural complexity: Ensuring data sync consistency required multiple pipelines and race logic. This led to inefficiencies in real-time dashboard updates for agents. Operational overhead: Maintaining separate systems for database and search—alongside provisioning, patching, scaling, and monitoring—strained resources. Seeking an integrated approach, Cars24 embraced MongoDB Atlas, hosted on Google Cloud . MongoDB Atlas would serve as a single, consistent, modern database and embedded search solution, powered by Apache Lucene. MongoDB Atlas Search also enabled Cars24 to run queries directly in the database. This eliminated the need to synchronise data between systems while delivering real-time results. This unified approach allowed the company’s developers to transition from managing complex synchronization mechanisms to building applications. Furthermore, the reduced administrative overhead enabled Cars24 to consolidate the team’s efforts, and to streamline query execution across the ecosystem. Thanks to MongoDB Atlas and MongoDB Atlas Search, Cars24 was able to: Avoid "synchronization tax”: Switching to MongoDB Atlas eliminated the need for data synchronization and the additional tooling this mandated. Real-time searches can be performed from a single interface and workflow. Deliver new search features faster: By using a single, unified API across database and search operations, new features can be delivered rapidly. Work with a fully managed platform: With MongoDB Atlas, Cars24’s engineers can focus more on application development and building products, rather than thinking about managing indexes, syncing, and more. Following this successful migration, Cars24 decided to also use MongoDB Atlas to replace one of its legacy databases, ArangoDB. The switch to MongoDB Atlas eliminated major roadblocks for other critical search capabilities. From ArangoDB to MongoDB: Streamlined operations and 50% cost savings As Cars24 scaled new services globally, it encountered limitations with its geospatial search solution, which was based on ArangoDB. This included performance bottlenecks, weak transactions as it was difficult to guarantee consistent data operations, and a limited ecosystem which meant that scaling developer onboarding and troubleshooting became increasingly onerous. Moving to MongoDB Atlas enabled Cars24 to transition its geospatial services, consolidating its data storage and search capabilities under a single, versatile platform. “We now have a highly available architecture, and an amazing team at MongoDB that has our back,” said Sharma. MongoDB offered a proven architecture for high availability, scalability, and real-world production readiness: Enhanced scalability: MongoDB’s ability to scale massive workloads supports Cars24’s growing global presence. Reliable transactions: MongoDB provides robust multi-document ACID transactions across shards, meeting mission-critical needs. Streamlined operations: MongoDB offers a single platform that is not limited to a database only. By consolidating its geospatial search workload under MongoDB, Cars24 has reduced maintenance and operational overhead. Not only did Cars24 cut costs in half by moving to MongoDB, but the widespread market adoption of MongoDB Atlas also means that Cars24 can continue to rapidly onboard developers familiar with MongoDB, a recruiting priority for Cars24’s growing development team. “To give you an idea, one of our business units had a developer team of less than 10 about a year ago. Now they are a triple-digit team,” said Sharma. “If we are going to keep introducing new developers, for a product coming up or scaling up, it becomes very important to focus on the community skills and support provided by our technology partner.” “Now that we have moved from ArangoDB to MongoDB Atlas, our developers are the happiest,” he added. Cars24 is now looking to consolidate even more of its application and data workflows under MongoDB Atlas. With the growing number of developers joining Cars24’s engineering teams, plans are to utilize MongoDB Atlas further to enhance productivity, scalability, and data-driven insights. Visit the MongoDB Atlas Learning Hub to learn more about Atlas. To learn more about MongoDB Atlas Search, visit our product page .
Innovating with MongoDB | Customer Successes, October 2025
It’s officially fall! The start of every new season is a perfect time to consider change and new beginnings. While fall might make you think about pumpkin spice and newly chilly evenings, I’m thinking about the latest round of transformations that MongoDB’s customers are embracing to thrive in an AI-powered world. In all seriousness, legacy systems and technical debt are huge challenges: the cost of tech debt has been estimated at almost $4 trillion dollars. That’s trillion with a T! Legacy systems can slow down innovation, create bottlenecks, and make it tough to deliver the seamless, real-time experiences customers increasingly expect. But companies are finding that modernizing their applications isn't just about fixing what's broken—modernization enables them to move faster and innovate for end-users. That’s why I'm incredibly excited to share the recent launch of MongoDB’s Application Modernization Platform (AMP). This AI-powered program is designed to help enterprises move beyond outdated infrastructures to embrace a flexible, data-driven future. AMP is a comprehensive approach to modernization that combines smart AI tooling with proven methodologies, enabling businesses to transform their applications from the ground up, moving from legacy monoliths to a more flexible, microservices-based architecture. In this roundup, we're spotlighting customers who understand the strategic importance of modernization. You'll see how Wells Fargo is using MongoDB to power a new credit card platform, how CSX is ensuring business continuity during a critical migration, how Intellect Design is modernizing its wealth management platform, and how Deutsche Telekom is transforming its B2C digital channels. With MongoDB, customers are showing how integral a modern database is to powering the next generation of applications—and succeeding in the AI era. Wells Fargo Wells Fargo sought to modernize its mainframe-dependent credit card platform to provide a faster, more seamless customer experience and handle an exponential increase in transaction data. The company's legacy system was costly to manage and lacked the scalability needed for its "Cards 2.0" initiative. To solve this, Wells Fargo built an operational data store (ODS) using MongoDB. This new platform allowed them to adopt reusable APIs, streamline integrations, and move from a monolithic architecture to flexible microservices. The ODS now serves 40% of traffic from external vendors, handling more than 7 million transactions with sub-second service. By leveraging MongoDB, Wells Fargo was able to jumpstart its mainframe modernization and create curated data products to serve real-time, personalized financial services. CSX CSX , a major U.S. railroad company, sought to modernize its critical operations platform, RTOP, by migrating it to the cloud. The challenge was to maintain the platform's 24/7 availability with minimal disruption to its mission-critical, near real-time operations during the transition. To solve this, CSX selected MongoDB Atlas on Azure and partnered with MongoDB Professional Services . Leveraging the Cluster-to-Cluster Sync (mongosync) feature, the team was able to facilitate continuous data synchronization and complete the entire migration in just a few hours. The move to MongoDB Atlas has equipped CSX with a more scalable and resilient platform. This modernization effort established a blueprint for migrating other critical applications and helped CSX continue its digital transformation journey toward becoming America’s best-run railroad. Intellect Design Intellect Design , a global fintech company, sought to modernize its wealth management platform to overcome legacy system bottlenecks and multihour batch processing delays. The company's rigid relational database architecture limited its ability to scale and innovate. To solve this, the company partnered with MongoDB, using our AMP methodology and generative AI tools. This transformation reengineered the platform's core components, resulting in an 85% reduction in onboarding workflow times, allowing clients to access critical portfolio insights faster than ever. This initiative is the first step in Intellect Design's long-term vision to integrate its entire application suite into a unified, AI-driven service. By leveraging MongoDB Atlas's flexible schema and powerful native tools, the company is now better positioned to deliver smarter analytics and advanced AI capabilities to its customers. Watch Intellect AI’s MongoDB.local Bengaluru keynote presentation to learn how AMP helped them transform outdated systems into scalable, modern solutions. Deutsche Telekom Deutsche Telekom , a leading telecommunications company, sought to modernize its B2C digital channels, which were fragmented by outdated legacy systems. The company needed to create a unified digital experience for its 30 million customers while improving developer productivity. By leveraging MongoDB Atlas as part of its Internal Developer Platform, Deutsche Telekom built a robust data infrastructure to unify customer data and power its new digital services. This approach allowed the company to retire legacy systems and reduce its reliance on physical shops and call centers. The transition to MongoDB Atlas led to a massive surge in digital engagement, with daily customer interactions rising from under 50,000 to approximately 1.5 million. The company's customer data platform now handles up to 15 times the load of legacy systems, supporting large-scale loyalty programs and transforming the customer experience. Video spotlight: Bendigo Bank Before you go, watch how Bendigo and Adelaide Bank modernized their core banking technology using MongoDB Atlas and generative AI. Bendigo and Adelaide Bank reduced the migration time for legacy applications from 80 hours to just five minutes. This innovative approach allowed them to quickly modernize their systems and better serve their 2.5 million customers. Want to get inspired by your peers and discover all the ways we empower businesses to innovate for the future? Visit MongoDB’s Customer Success Stories hub to see why these customers, and so many more, build modern applications with MongoDB.
The 10 Skills I Was Missing as a MongoDB User
When I first started using MongoDB, I didn’t have a plan beyond “install it and hope for the best.” I had read about how flexible it was, and it felt like all the developers swore by it, so I figured I’d give it a shot. I spun it up, built my first application, and got a feature working. But I felt like something was missing. It felt clunky. My queries were longer than I expected, and performance wasn’t great; I had the sense that I was fighting with the database instead of working with it. After a few projects like that, I began to wonder if maybe MongoDB wasn’t for me. Looking back now, I can say the problem wasn’t MongoDB, but was somewhere between the keyboard and the chair. It was me. I was carrying over habits from years of working with relational databases, expecting the same rules to apply. If MongoDB’s Skill Badges had existed when I started, I think my learning curve would have been a lot shorter. I had to learn many lessons the hard way, but these new badges cover the skills I had to piece together slowly. Instead of pretending I nailed it from day one, here’s the honest version of how I learned MongoDB, what tripped me up along the way, and how these Skill Badges would have helped. Learning to model the MongoDB way The first thing I got wrong was data modeling. I built my schema like I was still working in SQL– every entity in its own collection, always referencing instead of embedding, and absolutely no data duplication. It felt safe because it was familiar. Then I hit my first complex query. It required data from various collections, and suddenly, I found myself writing a series of queries and stitching them together in my code. It worked, but it was a messy process. When I discovered embedding, it felt like I had found a cheat code. I could put related data together in one single document, query it in one shot, and get better performance. That’s when I made my second mistake. I started embedding everything. At first, it seemed fine. However, my documents grew huge, updates became slower, and I was duplicating data in ways that created consistency issues. That’s when I learned about patterns like Extended References, and more generally, how to choose between embedding and referencing based on access patterns and update frequency. Later, I ran into more specialized needs, such as pre-computing data, embedding a subset of a large dataset into a parent, and tackling schema versioning. Back then, I learned those patterns by trial and error. Now, they’re covered in badges like Relational to Document Model , Schema Design Patterns , and Advanced Schema Patterns . Fixing what I thought was “just a slow query” Even after I got better at modeling, performance issues kept popping up. One collection in particular started slowing down as it grew, and I thought, “I know what to do! I’ll just add some indexes.” I added them everywhere I thought they might help. Nothing improved. It turns out indexes only help if they match your query patterns. The order of fields matters, and whether you cover your query shapes will affect performance. Most importantly, just because you can add an index doesn’t mean that you should be adding it in the first place. The big shift for me was learning to read an explain() plan and see how MongoDB was actually executing my queries. Once I started matching my indexes to my queries, performance went from “ok” to “blazing fast.” Around the same time, I stopped doing all my data transformation in application code. Before, I’d pull in raw data and loop through it to filter, group, and calculate. It was slow, verbose, and easy to break. Learning the aggregation framework completely changed that. I could handle the filtering and grouping right in the database, which made my code cleaner and the queries faster. There was a lot of guesswork in how I created my indexes, but the new Indexing Design Fundamentals covers that now. And when it comes to querying and analyzing data, Fundamentals of Data Transformation is there to help you. Had I had those two skills when I first started, I would’ve saved a lot of time wasted on trial and error. Moving from “it works” to “it works reliably” Early on, my approach to monitoring was simple: wait for something to break, then figure out why. If a performance went down, I’d poke around in logs. If a server stopped responding, I’d turn it off and on again, and hope for the best. It was stressful, and it meant I was always reacting instead of preventing problems. When I learned to use MongoDB’s monitoring tools properly, that changed. I could track latency, replication lag, and memory usage. I set alerts for unusual query patterns. I started seeing small problems before they turned into outages. Performance troubleshooting became more methodical as well. Instead of guessing, I measured. Breaking down queries, checking index use, and looking at server metrics side by side. The fixes were faster and more precise. Reliability was the last piece I got serious about. I used to think a working cluster was a reliable cluster. But reliability also means knowing what happens if a node fails, how quickly failover kicks in, and whether your recovery plan actually works in practice. Those things you can now learn in the Monitoring Tooling , Performance Tools and Techniques, and Cluster Reliability skill badges. If you are looking at deploying and maintaining MongoDB clusters, these skills will teach you what you need to know to make your deployment more resilient. Getting curious about what’s next Once my clusters were stable, I stopped firefighting, and my mindset changed. When you trust your data model, your indexes, your aggregations, and your operations, you get to relax. You can then spend that time on what’s coming next instead of fixing what’s already in production. For me, that means exploring features I wouldn’t have touched earlier, like Atlas Search , gen AI, and Vector Search . Now that the fundamentals are solid, I can experiment without risking stability and bring in new capabilities when a project actually calls for them. What I’d tell my past self If I could go back to when I first installed MongoDB, I’d keep it simple: Focus on data modeling first. A good foundation will save you from most of the problems I ran into. Once you have that, learn indexing and aggregation pipelines. They will make your life much easier when querying. Start monitoring from day one. It will save you a lot of trouble in the long run. Take a moment to educate yourself. You can only learn so much from trial and error. MongoDB offers a myriad of resources and ways to upskill yourself. Once you have established that base, you can explore more advanced topics and uncover the full potential of MongoDB. Features like Vector Search, full-text search with Atlas Search, or advanced schema design patterns are much easier to adopt when you trust your data model and have confidence in your operational setup. MongoDB Skill Badges cover all of these areas and more. They are short, practical, and focused on solving real problems you will face as a developer or DBA, and most of them can be taken over your lunch break. You can browse the full catalog at learn.mongodb.com/skills and pick the one that matches the challenge you are facing today. Keep going from there, and you might be surprised how much more you can get out of the database once you have the right skills in place.
Smarter AI Search, Powered by MongoDB Atlas and Pureinsights
We’re excited to announce that the integration of MongoDB Atlas with the Pureinsights Discovery Platform is now generally available—bringing to life a reimagined search experience powered by keyword, vector, and gen AI. What if your search box didn’t just find results, but instead understood intent? That’s exactly what this integration delivers! Beyond search: From matching to meaning Developers rely on MongoDB’s expansive knowledge ecosystem to find answers fast. But even with a rich library of technical blogs, forum threads, and documentation, traditional keyword search often falls short—especially when queries are nuanced, multilingual, or context-driven. That’s where the MongoDB-Pureinsights solution shines. Built on MongoDB Atlas and orchestrated by the Pureinsights Discovery platform, this intelligent search experience starts with the fundamentals: fast, accurate keyword results, powered by MongoDB Atlas Search . But as queries grow more ambiguous—say, “tutorials for AI”—the platform steps up. MongoDB Atlas Vector Search with Voyage AI , available as an embedding and reranking option (now part of MongoDB), goes beyond literal keywords to interpret intent—helping applications deliver smarter, more relevant results. The outcome: smarter, semantically aware responses that feel intuitive and accurate—because they are. What’s more, with generative answers enabled, the platform synthesizes information across MongoDB’s ecosystem (blog content, forums, and technical docs) to deliver clear, contextual answers using state-of-the-art language models. But it's not just pointing you to the right page. Instead, the platform is providing the right answer, with citations, ready to use. It’s like embedding a domain-trained AI assistant directly into your search bar. “As organizations look to move beyond traditional keyword search, they need solutions that combine speed, relevance, and contextual understanding,” said Haim Ribbi, Vice President, Global CSI, VAR & Tech Partner at MongoDB. “MongoDB Atlas provides the foundation for smarter discovery, and this collaboration with Pureinsights shows how easily teams can deliver gen AI-powered search experiences using their existing content.” Built for users everywhere But intelligence alone doesn’t make it transformational. What sets this experience apart is its adaptability. Whether you’re a developer troubleshooting in Berlin or a product owner building in São Paulo, the platform tailors responses to your preferences. Prefer concise summaries or deep technical dives? Want to translate answers in real time? Need responses that reflect your role and context? You’re in control. From tone and length to language and specificity, this is a search that truly understands you—literally and figuratively. Built on MongoDB. Elevated by Voyage AI. Delivered by Pureinsights. At the core of this solution is MongoDB Atlas, which unifies fast, scalable data access to structured content through Atlas Search and Atlas Vector Search. Looking ahead, by integrating with Voyage AI’s industry-leading embedding models, MongoDB Atlas aims to make semantic search and retrieval-augmented generation (RAG) applications even more accurate and reliable. While currently in private preview, this enhancement signals a promising future for developers building intelligent, AI-powered experiences. Pureinsights handles the orchestration layer. Their Discovery Platform ingests and enriches content, blends keyword, vector, and generative search into a seamless UI, and integrates with large language models like GPT-4. The platform supports multilingual capabilities, easy deployment, and enterprise-grade scalability out of the box. While generative answers are powered by integrated large language models (LLMs) and may vary by deployment, the solution is enterprise-ready, cloud-native, and built to scale. Bringing intelligent discovery to your own data Watch the demo video to see AI-powered search in action across 4,000+ pages of MongoDB content—from community forums and blog posts to technical documentation. While the demo features MongoDB’s content, the solution is built to adapt. You can bring the same AI-powered experience to your internal knowledge base, customer support portal, or developer hub—no need to build from scratch. Visit our partner page to learn more about MongoDB and Pureinsights and how we’re helping enterprises build smarter, AI-powered search experiences. Apply for a free gen AI demo using your enterprise content.