MongoDB Blog
Announcements, updates, news, and more
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.
From Local to Global: Scalable Edge Apps with RxDB + MongoDB
Modern organizations operate in increasingly complex environments. Teams are often distributed across cities, countries, or even continents, and many work in situations where connectivity is intermittent or completely unavailable. Construction crews in remote locations, healthcare workers in rural clinics, field engineers on offshore sites, and logistics operators managing warehouses all rely on digital tools to capture, share, and analyze data. Any interruption in access or performance can create inefficiencies, data inconsistencies, and even operational risks.
Announcing the MongoDB Plugin for Firebase Genkit
We’re thrilled to introduce the MongoDB Plugin for Genkit, designed to accelerate your AI-powered applications with advanced search and database tooling—all within the Genkit ecosystem. Whether you're building chatbots, intelligent assistants, or recommendation engines, this plugin brings together MongoDB’s cutting-edge search capabilities and Genkit’s AI workflows, enabling seamless vector, full-text, and hybrid search with zero hassle.
Personalized Retail Media Platforms—Powered by MongoDB
Retailers today have access to a wealth of data that is an invaluable, unique asset that only they possess—such as first-party and customer interaction data. With the rapid evolution of digital commerce and the depreciation of third-party cookies, retail media networks (RMNs) have emerged as a critical channel for monetization and customer engagement. Through the strategic use of shopper data (which spans both digital and physical retail channels), retailers are empowered to provide brands with a unique opportunity to engage in direct advertising with in-market consumers, achieving an unprecedented level of precision and conversion. You can find more information on this in our blog post on
Analyze Query Shapes With MongoDB and Datadog
In July 2025, MongoDB introduced Query Shape Insights for MongoDB Atlas. This feature provides customers with powerful tools to understand query performance trends at a granular level. Almost immediately upon launch, enterprise customers asked an important question: “Can we view these insights directly in Datadog, where we already monitor our applications?”
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 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.
Charting a New Course for SaaS Security: Why MongoDB Helped Build the SSCF
The way companies everywhere work is powered by SaaS. From collaboration tools to critical infrastructure, organizations rely on SaaS applications to drive their business forward. But this widespread adoption has created a significant security blind spot. How can you ensure every one of these applications is configured securely when they all offer different settings, capabilities, and levels of visibility? This inconsistency creates friction, wastes resources, and ultimately, exposes businesses to unnecessary risk. At MongoDB, we believe that securing the SaaS ecosystem is a shared responsibility. That's why we were proud to collaborate with the Cloud Security Alliance (CSA) and industry leaders like GuidePoint Security to develop a new standard—the SaaS Security Capability Framework (SSCF) . The problem: A gap in cloud security For years, the majority of security assessments have focused on the SaaS provider's organizational security, often through frameworks like SOC 2 or ISO 27001. While essential, these frameworks don't always address a critical question: what security capabilities are available to the SaaS customer within the application? This gap means that security teams face a chaotic landscape. Every new SaaS app brings a different set of configurable controls for logging, identity management, and data access. This makes it nearly impossible to implement and track consistent security policies at scale, leading to a burdensome assessment process for everyone involved. The solution: A common framework for SaaS security The SSCF was created to solve this problem by establishing a clear, technical set of customer-facing security controls that SaaS vendors should provide. The framework is designed to empower customers by ensuring they have the tools they need to operate applications securely at scale on their side of the Shared Security Responsibility Model (SSRM). The framework helps with many use cases, but three key audiences stand out: For risk management teams: The SSCF provides a clear baseline to use during vendor assessments, simplifying procurement. For SaaS security teams: It offers a checklist for implementing the security features enterprises expect, streamlining the security program. For SaaS vendors: The SSCF standardizes assessment responses, reducing the overhead of custom questionnaires and helping vendors meet customer requirements. The SSCF focuses on six critical domains, aligned with CSA’s Cloud Control Matrix, providing specific and actionable controls for each: Change Control and Configuration Management (CCC): Ensuring you can programmatically query and get documentation on all security configurations. Data Security and Privacy Lifecycle Management (DSP): Giving customers control over features like disabling file uploads to prevent malicious code. Identity and Access Management (IAM): Providing robust, modern controls for user access, including SSO enforcement, non-human identity (NHI) governance, and a dedicated read-only security auditor role. Interoperability and Portability (IPY): Giving administrators control over mass data exports and visibility into application integrations. Logging and Monitoring (LOG): Defining a clear set of comprehensive requirements for machine-readable logs with mandatory fields for effective threat detection and forensics. Security Incident Management (SEF): Requiring a simple, effective way for vendors to notify a designated customer security contact during an incident. MongoDB's commitment to a more secure ecosystem Our involvement in creating the SSCF stems from our deep commitment to the security of our customers' data and the broader developer community. We believe that robust security shouldn't be an afterthought; it must be built in and easy to consume. The principles outlined in the SSCF—like strong identity controls and comprehensive logging—are philosophies we already built into our own data platform. Strong security capabilities allow our customers to build and innovate faster and more securely, knowing they have a reliable foundation. And personally, as a co-chair of the CSA SSCF, I’ve seen great excitement and engagement on the part of our working group—which helped me realize how many companies are affected by this lack of consistency. The SSCF is a vital step toward creating a more trusted, efficient, and secure global SaaS ecosystem. We are thrilled to have been a part of this foundational work and will continue to champion this standard that empowers developers and security teams alike. Visit our security page to learn more about how MongoDB helps protect your data.