MongoDB Blog
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AI Is Changing What Customers Need From a Database. MongoDB 8.3 Is Built for It
Today, we announced at .local London that MongoDB 8.3 is built for the speed AI demands—and our customers can't afford to wait. The data layer has to move at AI speed The old contract between databases and the applications on top of them was simple: databases improve slowly, and architectures evolve around them. AI has changed that contract. The workloads our customers are shipping today—agents retrieving at sub-100ms, retry storms hitting in milliseconds, multi-region deployments that can't trade compliance for latency—were edge cases 18 months ago. Now they're the baseline. MongoDB 8.3, generally available today, is our fourth significant release in 19 months. These releases compound. Customers running on 8.0 have seen 36% faster reads and 59% higher throughput for updates. 8.3 adds another 35% to write throughput, 45% to reads, and 15% to ACID transactions over 8.0 — without changing a line of application code. Enterprises like Adobe, running the most demanding AI in production, have made the requirements clear: sub-100ms retrieval, sub-second context updates, zero downtime. That's what MongoDB Atlas is built for. That's the commitment: when the data platform keeps pace, our customers can focus on shipping. MongoDB.local London Core Blog 2026 - Image 1 media Run anywhere. Stay secure. Where you run your agents isn't just an infrastructure decision anymore. Now, it's a critical compliance and security decision as well. While most platforms force a trade-off between global reach and necessary control, with 130 regions across AWS, Google Cloud, and Microsoft Azure, Atlas doesn’t force you to compromise. Atlas even enables clusters spanning multiple providers simultaneously. Avalara and Iron Mountain both took the cloud-agnostic path, modernizing on Atlas so they could meet their customers wherever they ran. The deployment shape changes. The data layer underneath doesn't. What's shifted in the last year is the pressure on both ends. Customers want retrieval and embedding capabilities closer to their users, in more places, on more clouds. They also want more authority over the residency of their data. Those two demands used to be in tension. They don't have to be. Cross-region connectivity for AWS PrivateLink, generally available today, is the clearest example. Traffic between Atlas clusters in different AWS regions stays on the AWS private backbone, with no public internet exposure. Security and compliance teams get the guarantees they need. Engineering teams design around fewer edge cases. Nobody has to make a trade-off. Built to keep pace Every capability in this post addresses friction that technical leaders have been engineering around for years. They solve different problems, but share one objective: to eliminate the infrastructure trade-offs that slow down production of AI. The AI workloads our customers will run 18 months from now will look different from those today. That's not a risk. That's the point. Four significant releases in 19 months isn't a marketing number. It's a signal about how seriously we take the current pace of change, and our commitment to staying ahead of it for our 65,200+ customers. Getting agents to retrieve the right information, accurately and at speed, is where embeddings and memory come in. Pablo Stern covers that in his blog, The Bottleneck in Enterprise AI Isn't the Model. It's the Data.
New Research Reveals Overcoming Legacy Tech Issues Key to AI Success
This guest post comes from IDC’s Dr. William Lee, Senior Research Director, Service Provider and Core Infrastructure Research. MongoDB commissioned IDC to explore the connection between legacy infrastructure, data challenges, and AI across Asia Pacific, and today we’re happy to share that work. For more, see the full MongoDB-sponsored IDC InfoBrief, Modernizing Legacy: Winning in the Age of AI, Doc #AP242555-IB, April 2026. AI ambition is everywhere across Asia/Pacific. But ambition alone does not determine success. Organizations are discovering that AI outcomes are directly tied to the quality, accessibility, and modernity of their underlying technology stack and associated data technology foundations. Organizations that have managed to stay abreast of technical and data management changes across the application and infrastructure stacks, by embedding modernization into their organizational DNA, are experiencing 3x more digital revenue growth than those that are bound up in technical and data debt. To better understand this connection, IDC surveyed 1,400 organizations across eight Asia/Pacific markets. The findings reveal that modernization is no longer a side initiative. It is the core of a sustainable AI strategy. The AI readiness divide: Leaders versus mainstream IDC’s latest Asia/Pacific Modernization Survey, sponsored by MongoDB, identifies two distinct groups: The Mainstream Cohort: organizations still burdened by technical debt, siloed data, and skills gaps The Leaders Cohort: organizations that have embedded modernization into their strategy and experience the business results to match This divide is not theoretical. It is measurable in business performance. Organizations in the Leaders Cohort generate nearly three times more digital revenue than their peers. The difference is not simply higher AI spending. Leaders modernize core infrastructure, align executive support with transformation goals, and invest in skills development alongside technology. They treat AI readiness as an enterprise capability—not a standalone initiative. APAC IDC Blog Image 1 media The rigidity trap: Technical debt and AI failure risk A significant portion of Asia/Pacific organizations remain constrained by legacy architectures. According to IDC’s research, 43% report that their existing architecture is a major obstacle, making it difficult to build new applications without extensive modernization. This rigidity creates what IDC refers to as data debt—siloed, redundant, outdated, and poor-quality data that undermines AI performance and increases operational cost, and is in addition to the growing levels of technical debt that are being accumulated by organizations due to the slow modernization of older applications. When AI systems are trained on fragmented or inconsistent data: Outcomes become unreliable Bias risks increase Operational costs rise Business trust erodes IDC predicts that CIOs who fail to launch data debt remediation initiatives will face 50% higher AI failure rates and rising costs by 2027. Yet one-third of all enterprises continue to rely on legacy relational databases. Many such databases have been implemented in support of a wide array of business applications where business leaders expect they can use AI. Yet the legacy RDBMS-type databases are not capable of delivering on the dynamic, rapidly evolving, high-volume real-time demands that AI requires. Organizations that are unable to move to AI-ready application stacks are being left behind by those that have already made the switch. The gap between AI investment and infrastructure readiness is widening. APAC IDC Blog Image 2 media Legacy drag: The real business impact The consequences of technical debt are already visible. 95% of organizations report project delays 90% have experienced failed modernization initiatives 89% acknowledge technical debt as a major modernization obstacle In addition, organizations cite weak security integration, limited engagement with the business users, and outdated workflows as compounding challenges. Modernization failures are rarely just technical. They are organizational and structural. What sets leaders apart The Leaders Cohort does not operate in a constraint-free environment. Instead, they respond differently. IDC defines Leaders as organizations—across both digital-native and traditional industries—that have broken free from legacy rigidity and embedded modernization into ongoing operations. Their distinguishing characteristics include: Continuous, multi-pronged approaches to addressing legacy systems Alignment between executive leadership, funding, and AI outcomes Investment in modernization as a long-term capability, not a one-off project Strong focus on AI and modern application development skills The result is not just better IT performance. Leaders grow digital revenue faster and are positioned to extract value from AI initiatives earlier and more consistently than their peers. Cloud-centric data management: A strategic enabler Modern data platforms are central to this shift. In IDC’s research, 38% of Asia/Pacific organizations identify cloud-centric data management platforms as their top modernization investment priority for 2026. The motivation is clear: support hybrid architectures and AI workloads without introducing additional complexity. While AI enablement is a universal requirement, Leaders distinguish themselves by prioritizing: Security and compliance Flexibility across structured and unstructured data Scalable architectures aligned to modern AI toolchains This capability is increasingly critical. Much of today’s AI-relevant data—including content, sensor outputs, and customer interactions—resides in unstructured formats that traditional architectures struggle to integrate effectively. Handling both structured and unstructured data seamlessly has become a competitive differentiator. Modernization as a continuous strategy IDC’s perspective is clear: modernization is no longer a technology refresh cycle. It is a strategic operating model. Successful organizations approach modernization across three dimensions: People Leaders invest deliberately in AI and modern application development skills. They recognize resistance to change as a strategic risk and actively manage it. Process They adopt cloud-native approaches rather than repeating short-term “lift-and-shift” migrations that simply relocate technical debt. They use structured prioritization frameworks to embed modernization into business-as-usual operations. Technology They modernize to data platforms that support scalability, diverse data types, rapid feature development, and alignment with contemporary AI ecosystems. The ROI equation: Risk of action versus risk of inaction Modernization is often perceived as expensive and risky. However, IDC’s analysis suggests that the risk of inaction is frequently underestimated, and this study affirms that those who invest effectively, and continuously, into their application modernization program are experiencing both better ROI and higher digital revenues! Organizations that modernize report: Significant reductions in reporting time Double-digit productivity improvements Meaningful cost savings Hundreds of thousands of dollars in quantified cost reductions While full application rewrites and database modernization demand greater upfront investment than lift-and-shift migrations, they can deliver up to three times the long-term benefit. For CIOs and business leaders, the decision should not be framed as modernization cost versus status quo stability. It is modernization investment versus escalating AI failure risk. The Path forward: Legacy systems are not permanent Overcoming legacy is often perceived to be as significant a risk as taking on new technologies. IDC notes that many CIOs across the region focus on risk avoidance as a priority. In contrast, business leaders are seeking innovative solutions that drive new business opportunities, and so CIOs must balance their risk aversion concerns with the business demands. IDC’s research shows that legacy migrations that are under-funded pose significantly higher risk, and deliver lower returns, than those that are sufficiently funded from the outset. Organizations that proactively address technical debt, modernize systems, and align leadership and funding around AI-enabled outcomes will increasingly separate themselves from the pack. Those that delay will face structural disadvantages: Growing technical debt Escalating modernization costs Underperforming AI systems Slower digital revenue growth IDC’s research shows that the next wave of AI advantage in Asia/Pacific will not be determined solely by model sophistication. It will be determined by architectural foundations. Ultimately, without modernization, there can be no sustainable AI strategy.
Introducing MongoDB Agent Skills and Plugins for Coding Agents
Software engineering is evolving into agentic engineering. According to the Stack Overflow Developer Survey 2025, 84% of respondents use or plan to use AI tools in their development, up from 76% the previous year. At this rate, the tooling needs to keep pace. Last year, we introduced the MongoDB MCP Server to give agents the connectivity they need to interact with MongoDB, helping them generate context-aware code. But connectivity was only the start. Agents are generalists by design, and they don't inherently know the best practices and design patterns that real-world production systems demand. Today, we're addressing this by introducing official MongoDB Agent Skills: structured instructions, best practices, and resources that agents can discover and apply to generate more reliable code across the full development lifecycle, from schema design and performance optimization to implementing advanced capabilities like AI retrieval. To bring this directly into the tools you use, we're also launching plugins for Claude Code, Cursor, Gemini CLI, and VS Code, combining the MongoDB MCP Server and Agent Skills in a single, ready-to-use package. Turning coding agents into MongoDB experts Coding agents are great at producing working code, but they still make common mistakes in production systems, often defaulting to relational thinking that doesn't translate well to MongoDB, such as: Over-normalizing schemas, ignoring MongoDB's document-oriented strengths. Underusing compound indexes, causing performance bottlenecks at scale. Misusing indexes and search indexes, overlooking the consistency trade-off for high-performance full-text search. Because these pitfalls mirror common human errors, they are naturally reflected in agent outputs. MongoDB Agent Skills address this by providing expert guidance to agents, like schema design heuristics, indexing strategies, query patterns, and operational safeguards, enabling agents to ship more reliable, more consistent code faster. Agent Skills were introduced by Anthropic as an open standard and have since been adopted by the leading AI development tools, including Claude Code, Cursor, Codex, and more. This initial release covers the full application development lifecycle on MongoDB, from connection management and schema design to guidance on implementing advanced capabilities. We will continue to update and expand our skills library based on user needs. Figure 1. MongoDB Agent Skills. Scaling agentic engineering with MongoDB As organizations embrace agentic software engineering, existing processes and workflows must be reimagined. The MongoDB MCP Server and MongoDB Agent Skills are built for this shift and work best together, giving builders and agents the tools to move fast without sacrificing guardrails or control. The MongoDB MCP Server serves as the connectivity layer for your MongoDB deployments. It manages authentication and defines exactly what agents can access and do. Combined with MongoDB’s native authorization, it ensures agents operate with only the permissions they need, while giving teams governance through configurable controls like disabling specific tools. Agent Skills ensure agents follow best practices from the start, reducing architectural risk, accelerating implementation, and raising the baseline quality of every agent-generated code. While some skills can be used independently, others work in conjunction with the MongoDB MCP Server for workflows that require it. To simplify setup, the MCP Server and skills are now packaged together as plugins and extensions for Claude Code, Cursor, Gemini CLI, and VS Code, bringing these capabilities directly into your preferred tools. Figure 2. MongoDB for Claude plugin in action. We also encourage you to build your own skills as your agentic workflows mature. Whether enforcing internal naming conventions, custom data modeling patterns, or team-specific workflows, skills give you a practical way to codify institutional knowledge and ensure every agent and every developer works from the same playbook. How to get started Whether you’re using Claude Code, Cursor, Gemini CLI, or other AI development tools, you can install the MongoDB MCP Server and Agent Skills in seconds. For example, in Claude Code, install the plugin that bundles both: Code Snippet /plugin marketplace add mongodb/agent-skills /plugin install mongodb@mongodb-plugins For Cursor, Gemini CLI, and VS Code extensions, refer to their respective documentation. You can also install the skills for most coding agents using the Vercel Skills CLI (requires Node.js): Code Snippet npx skills add mongodb/agent-skills If you prefer, you can manually clone the GitHub repository and copy the skills into the appropriate folder for your agent. Similarly, to install the MongoDB MCP Server, use the following command: Code Snippet npx mongodb-mcp-server@latest setup Agentic engineering is changing how teams work, and it is changing fast. Agents need the context and guidance to meet the standards of real-world production applications. With the official MongoDB Agent Skills and plugins, builders can move faster with confidence, and organizations can adopt coding agents knowing that MongoDB best practices are embedded directly into every workflow. Next Steps Ship faster, more reliable apps on MongoDB with Agent Skills. Install for Claude Code, Cursor, Gemini CLI and VS Code!
Observability and OpenTelemetry: Introducing MongoDB Atlas Log Integration
In high-stakes enterprise environments, outages do not wait for business hours, and neither do IT/Network Operators. A latency spike hits the dashboard, and metrics signal that the database is under pressure. The cause? Indeterminate. Meanwhile, the business impact is immediate: orders fail to process, customers can’t access accounts, transactions stall, and critical records become temporarily unavailable. Every minute of uncertainty translates into lost revenue, frustrated users, and escalating pressure. Teams often fall back on a familiar—yet time-consuming—ritual: logging into their data platform, exporting large log files, extracting compressed archives, and manually searching through thousands of lines of entries to identify the issue. What should be a quick diagnosis becomes a manual context-switching investigation. By the time the problematic query, configuration issue, or audit event is identified, users have already experienced the disruption—and the business has absorbed the cost. MongoDB believes the database should be the heartbeat of a digital business. So we’re introducing a new log integration that brings MongoDB Atlas system and audit logs directly into external observability and storage platforms. This enhancement helps bridge the gap between metrics and meaning when it matters most. Flexible log delivery for modern observability workflows Now database operators, DevOps pros, and IT Operations teams alike can send MongoDB system and audit logs—including mongod, mongos, and audit logs—directly to the tools they already rely on: Datadog, Splunk, Google Cloud Storage, Azure Blob Storage, or Amazon S3. Beyond native integrations, MongoDB supports sending logs via OpenTelemetry (OTel), the open-source standard for collecting and transmitting telemetry data. This enables customers to export MongoDB logs to any observability or logging backend that supports OTel. By using a vendor-neutral, standards-based protocol, MongoDB fits seamlessly into modern observability architectures. This eliminates lock-in and preserves flexibility as tooling strategies evolve. Enabling real-time clarity Modern enterprises generate rich system logs essential for debugging and compliance. However, when these logs are siloed, operational inefficiencies grow. Manual log access introduces friction, delays resolution, and creates a visibility gap between metrics and logs. MongoDB’s new log integration transforms that experience with: Accelerated troubleshooting: Send logs in near real-time to observability platforms like Datadog, Splunk, or OpenTelemetry-compatible backends, enabling teams to quickly identify issues and reduce manual operational steps that slow incident resolution. Unified telemetry: Correlate MongoDB logs with application traces and infrastructure metrics in existing observability platforms, helping teams quickly understand how database behavior impacts overall system performance. Simplified compliance: Automatically route audit logs to secure long-term storage such as Amazon S3, helping organizations meet regulatory and audit requirements without manual log management. Figure 1. Atlas Log Integration configuration options for delivering MongoDB logs to observability and storage platforms. image Real-world use cases How does this look in practice for modern application, operations, and engineering teams? Here are a few examples. table The criticality of observability As applications scale, the database becomes the most critical layer of an organization’s technology stack. Missing or siloed visibility leads to costly downtime and fragmented decision-making. This log integration is available for dedicated M10+ clusters. An external sink can be configured in minutes: Navigate to the Project Integrations page in the MongoDB Atlas UI. Select the intended destination: Datadog, Splunk, Google Cloud, Microsoft Azure, Amazon S3, or any OTel log endpoint. Enter the required credentials and select the desired logs to send: mongod, mongos, or audit. Note: Atlas Search logs are also currently available via private preview. Figure 2. MongoDB Atlas logs integrated into an OpenTelemetry observability pipeline. image One observability strategy, built to scale For teams that need fast, MongoDB-centric visibility, MongoDB Atlas continues to offer powerful native tools like Query Insights and the Query Profiler. These capabilities are designed to surface what is happening inside a user’s clusters with minimal friction. However, as organizations scale, database insights can not live in isolation. MongoDB Atlas’s log integration extends observability systematically to the data plane. This enables MongoDB logs to flow into the observability platforms teams already use across engineering, security, IT operations, and compliance. With native integrations and an OpenTelemetry-compatible endpoint, teams can route logs wherever they are needed. This enables rapid troubleshooting, stronger auditability, and confident scaling without blind spots.
Innovating with MongoDB | Customer Successes, February 2026
Who says that winter is when things slow down? MongoDB has had a busy start to the year, with a steady stream of announcements and product features—all against the backdrop of an industry moving at warp speed. It's been a lot, and it's been a blast! For example, the energy at January’s MongoDB.local San Francisco—where we announced capabilities to help teams ship production AI faster—was infectious. MongoDB isn’t just starting a new chapter in AI; we’re rewriting the book in real time. The next generation of AI companies isn't just looking for a temporary place to store data; they’re looking to build on a generational modern data platform. Indeed, the most innovative founders are moving away from rigid, legacy systems and embracing a single, fluid foundation that can grow with them. At MongoDB.local SF, our message was clear: Choose your data platform strategically in order to ship faster. From our new Voyage 4 models to the general availability of our Intelligent Assistant, we are obsessed with anticipating what developers need next. This assistant is particularly impactful because it embeds MongoDB-specific expertise directly into Compass and MongoDB Atlas, allowing developers to troubleshoot performance without the "context-switching" that traditionally slows them down. In this issue, I’m thrilled to spotlight four startups who are building the future on the right foundation. You’ll see how Modelence and Thesys are using our flexible document model to eliminate 'operational drag,' allowing them to iterate on AI-native workflows in real time. And then there’s Heidi and Emergent Labs, who both are proving that when you simplify your codebase with a unified platform, you can turn a plan into shipped code at record speeds. I’ve highlighted their journeys below so you can see exactly how these leaders are setting a new pace and changing their trajectory with MongoDB. Modelence Modelence aims to modernize backend infrastructure for the era of AI-assisted development. Traditional relational databases and manual systems create significant operational drag, as their rigid schemas and heavy migrations cannot keep pace with agent-native workflows. These legacy systems struggle with the high-velocity requirements of intelligent coding agents, which must iterate on data structures in real time without causing system downtime. To build a stable foundation for automation, Modelence integrated MongoDB Atlas as its core data layer. The platform utilizes the flexible document model to align with how intelligent systems think, allowing specifications and runtime events to coexist. This "fit" enables per-tenant isolation and managed credentials, ensuring automated changes remain safe and traceable without the tangle of relational joins. Standardizing on MongoDB Atlas helped Modelence raise $3 million dollars in its Seed round. The company now moves from planning to running features in minutes, achieving faster iteration loops and fewer regressions. Thesys Thesys aims to empower developers by making generative user interfaces—adaptive, real-time components—accessible to everyone. Previously, developers faced the friction of static chat bubbles and hardcoded dashboards that failed to visually represent complex AI outputs. These traditional interfaces forced teams to rebuild UI layers for every use case, which kills user engagement. To solve these orchestration challenges, Thesys integrated MongoDB Atlas as the operational backbone for its C1 API middleware. The platform utilizes the document model to manage complex entities within a single, high-performance data layer. By removing the friction of mapping unstructured LLM outputs to rigid schemas, engineering teams can now ship updates weekly. Through the MongoDB for Startups program, Thesys successfully accelerated its go-to-market timeline. By offloading operational management to MongoDB Atlas, Thesys now maintains the agility to evolve its data layer alongside emerging AI trends, ensuring its intelligent interfaces remain high-performing as they scale globally. Emergent Labs Emergent Labs sought to democratize software development through “vibe coding,” a platform where AI agents build applications from natural language prompts. The company’s initial use of PostgreSQL caused significant friction, as AI agents frequently failed during schema migrations when non-technical users iteratively changed their application requirements. By switching to MongoDB Atlas, Emergent Labs provided its agents with a flexible, document-based architecture that matches the JSON data they naturally produce. This eliminated the PostgreSQL migration loops, allowing agents to modify data structures on the fly and deploy isolated, production-ready databases in minutes. The transition has powered the creation of nearly 2 million applications across 180 countries in just four months. With MongoDB Atlas, the platform now supports complex builds of up to 300,000 lines of code, doubling deployment rates and allowing non-technical entrepreneurs to launch sophisticated tools without traditional engineering resources. Heidi Heidi aims to reclaim clinician time by automating administrative tasks. Previously, clinicians spent 40% of their shifts on paperwork, reducing time for patient care. To manage this at scale, Heidi initially used Amazon DocumentDB, but faced critical limitations including mandatory downtime for scaling, high latency, and a lack of native search functionalities essential for complex AI workloads. To eliminate these bottlenecks, Heidi migrated to MongoDB Atlas for its flexible schema and built-in AI capabilities. Integrating MongoDB Vector Search enables Heidi to perform RAG without "bolt-on" databases, streamlining vector and semantic search under a single API. This technical fit enables developers to unify diverse medical data while meeting stringent healthcare security and regulatory requirements. Since migrating, Heidi has supported 81 million consultations, returning 18 million hours to the frontline. By offloading management to MongoDB Atlas, Heidi ensures its platform remains high-performing while empowering practitioners to focus on their primary mission: providing compassionate patient care. Video Spotlight Before you go, watch TinyFish Co-founder and CEO, Sudheesh Nair, explain how “nano agents” are transforming web-based research. Learn how TinyFish extracts actionable intelligence from unstructured internet data using MongoDB and Voyage AI.
MongoDB.local San Francisco 2026: Ship Production AI, Faster
Today at MongoDB.local San Francisco, we announced capabilities that collapse the distance between AI prototype and production. Building AI applications means solving real problems: keeping conversational context clean and queryable, retrieving the right information from thousands of past interactions, connecting AI agents to your data without custom plumbing. These aren't theoretical challenges, they're the friction points that slow teams down every day. The AI era demands more from your data platform. MongoDB gives you everything you need to build quickly. Voyage AI: the best gets better Embedding models can make or break AI search experiences. We're proud that voyage-3-large has been the world's top-performing embedding model on Hugging Face's RTEB benchmark since its inception. But we didn’t rest on our laurels. There’s a new model at the top of the charts. Today, we're pleased to announce that the Voyage 4 model family is now generally available. The best just got better. The voyage-4 series models operate in a shared embedding space, allowing for cross-model compatibility and unprecedented flexibility to optimize for accuracy, speed, or cost. This release also includes voyage-4-nano, our first open-weight model available on HuggingFace, perfect for local development. Additionally, we're launching the new voyage-multimodal-3.5 model, which has been specifically trained to support video content alongside text and images. For developers building multimodal AI applications, this represents a significant leap forward in handling diverse content types within a single retrieval system. Best of all, upgrading is remarkably straightforward—you can simply change the model parameter to "voyage-multimodal-3.5" in your API call, instantly unlocking video capabilities without needing to refactor your existing codebase or change your application architecture. Finally, we’re announcing the public preview of the Embedding and Reranking API on MongoDB Atlas, providing API support for Voyage AI models. While enabling standalone usage of the models with any technology stack, the API benefits from the robust security and scalability standards of MongoDB. By bringing critical components into a single control plane and interface, it eliminates the need to manage separate vendors and significantly reduces operational overhead. Automated Embedding, convenience built into MongoDB Community Persistence matters. An AI with amnesia isn’t helpful; users need systems to remember context from minutes, hours, and weeks ago. Every interaction is a goldmine of preferences, patterns, and behavior that should make the next interaction smarter. But storing conversation history in a database isn't enough. Simple storage solves nothing if you can't retrieve the right information at the right time. The real challenge is intelligent retrieval: finding relevant context across thousands of past interactions, filtered by metadata and user attributes, without your system buckling under production load. This is where vector search becomes critical—enabling semantic search that captures meaning, not just keywords, while operating on your real-time operational data. And this is where MongoDB's approach eliminates a major pain point: the need to sync data between separate systems for vectors and application data. Until now, generating and storing these vectors required overhead—development time, infrastructure management, and cognitive load. No longer. We're introducing Automated Embedding for MongoDB Community Edition in public preview. MongoDB Community Edition now handles the complexity of managing embedding models automatically, giving developers high-accuracy semantic search in the database while maintaining flexibility to use any LLM provider or orchestration framework. Automated Embedding offers one-click automatic embedding directly inside MongoDB, which eliminates the need to sync data and manage external models. It’s an easy way to get high quality embedding natively. Best-in-class retrieval shouldn't require infrastructure work—Automated Embedding in MongoDB Vector Search delivers on that promise. Automated Embedding in MongoDB Vector Search is available now in Community Edition, with Atlas access coming soon. Precise text filtering for advanced search use cases Today, we announced the launch of Lexical Prefilters for Vector Search. This addresses a long-standing request from developers building semantic search interfaces who need advanced text filtering alongside vector operations. The new syntax enables powerful text filtering capabilities—fuzzy matching, phrase search, wildcards, and geospatial filtering—as prefilters for vector search. This leverages full text analysis capabilities while maintaining the semantic power of vector search. We've introduced a new vector data type in $search index definitions and a vectorSearch operator within the $search aggregation stage to make this work seamlessly. This replaces the knnBeta operator with a cleaner, more powerful approach. For teams already using lexical and vector search together, this provides a simplified migration path with significantly expanded capabilities. Intelligent assistance wherever you work MongoDB’s intelligent assistant is generally available in MongoDB Compass. The assistant provides in-app guidance for debugging connection errors, optimizing query performance, and learning best practices, all without leaving your development environment. You can even query your database using natural language through read-only database tools that require your approval before execution, allowing for deeper contextual awareness of your data. The assistant was built to address real friction: developers switching between multiple tools and documentation tabs, waiting for support responses, or getting generic advice from general-purpose AI chatbots that don't understand MongoDB-specific contexts. Now, tailored guidance is available instantly, right where you're working. The modernized Atlas Data Explorer interface brings the Compass experience directly into the Atlas web UI, addressing a critical gap for teams with security policies that restrict desktop application usage. Users can now perform sophisticated query development, optimization, bulk operations, and complex aggregations—all with AI assistance—across all MongoDB Atlas clusters in a unified web interface. Whether you're troubleshooting a connection issue, optimizing a slow query, or learning how to structure an aggregation pipeline, the intelligent assistant delivers MongoDB-specific expertise without context switching. Try the intelligent assistant in the modernized Atlas Data Explorer now. The engine behind MongoDB Search and Vector Search is now available under SSPL Finally, mongot, the engine powering MongoDB Search and Vector Search, is now publicly available under SSPL. While still in preview, after years of development and investment, we're making the source code of this core technology available to the community, expanding our unified search architecture beyond Atlas to every MongoDB deployment. mongot runs separately from mongod, MongoDB's core database process, and is the foundation that makes powerful search native to MongoDB. Releasing mongot under SSPL means full transparency for security audits and debugging complex edge cases. Developers can dive into mongot's architecture, understand how search and vector operations work under the hood, and help shape the future of search at MongoDB. A modern data platform that evolves with your needs These announcements reflect our commitment to anticipating what developers need as AI development matures. Vector search, time series, stream processing, queryable encryption, Atlas itself—we've consistently delivered on emerging requirements. "If you're building an early-stage company that is going to scale very rapidly, you need a database solution that isn't going to break under the load of a huge volume of users," said Eno Reyes, Co-founder and CTO of Factory. "You need a fast-moving team with a reliable solution, and there really is one option in this space—and it's MongoDB." Rabi Shanker Guha, CEO of Thesys, put it this way: “MongoDB helps us move fast in an ever-changing world. The best database is the one you don’t have to think about—it just works exactly where and how you need it. That’s MongoDB for us.” Ship faster, scale confidently Each capability we announced today addresses real friction in the AI development workflow and in the developer experience. We're not asking developers to choose between structured data and vectors, between performance and flexibility, or between rapid iteration and production readiness. The promise is straightforward: ship faster, scale confidently, and focus on what makes your AI application unique—not on managing database infrastructure. In an ecosystem crowded with point solutions and retrofitted legacy systems, MongoDB is a modern data platform built for the long haul.
That’s a Wrap: MongoDB’s 2025 in Review & 2026 Predictions
It’s nearly the end of the year—again! That means it’s time for an end-of-year blog post that expresses disbelief at the passage of time. Which, as the saying goes, flies when you’re having fun. And definitely when you’re as busy as MongoDB was in 2025. It was a big year for the company—and more importantly, for the tens of thousands of customers and millions of developers who rely on MongoDB’s modern data platform for their most mission-critical workloads. At MongoDB, everything we do starts with our obsession with customers and their needs, and if there’s a theme to MongoDB’s 2025, it was (and will continue to be) enabling customer innovation and helping them succeed in the AI era. So here are a few highlights of how MongoDB acted on behalf of customers in 2025. From the acquisition of Voyage AI to customer success across industries, a lot happened in 2025. Let’s go!* *Read to the end for 2026 thoughts. 2025: The (MongoDB) year that was Voyage AI, modernization, and search In February, MongoDB announced the acquisition of Voyage AI, a pioneer in embedding and reranking models, to enhance the accuracy of AI applications. Integrating Voyage AI's advanced retrieval technology with MongoDB’s modern, AI-ready data platform addresses a critical challenge: LLM model hallucinations caused by a lack of context. By improving retrieval accuracy for specialized domains like finance and law, the integration enables businesses to deploy AI for mission-critical use cases. To learn more, see the MongoDB Voyage AI page. Then, in September, we launched MongoDB AMP, an AI-powered Application Modernization Platform. AMP is designed to accelerate the transformation of legacy applications through a combination of AI-powered tooling, a proven delivery framework, and expert guidance (tools, techniques, and talent) to help enterprises reduce technical debt and modernize 2-3 times faster. Want more? Sure you do! Check out this short video. MongoDB also announced the addition of search and vector search capabilities to MongoDB Community Edition and MongoDB Enterprise Server. This allows developers to build and test AI-native applications, including those using retrieval-augmented generation (RAG), in local or on-premises environments. Previously exclusive to MongoDB Atlas, these features enable secure, hybrid deployments where sensitive data can remain on-premises while still leveraging advanced search tools. Here’s a (slightly less short) video about search and vector search on Enterprise Server. Growing and scaling with MongoDB As noted, everything we do at MongoDB starts with our obsession with customers. 2025 was another banner year for customer success and innovation—we were inspired by what organizations of every shape and size, across industries and geographies, built with MongoDB in 2025. Here are just two of the many stories our customers shared in 2025; much more can be found in my colleague Katie Palmer’s blog series, Innovating with MongoDB. Factory By combining the Atlas modern data platform with Voyage AI’s high-performance embeddings, the AI-native startup Factory—which uses AI agents called Droids to accelerate software development lifecycles for organizations—consolidated its fragmented tech stack. This enabled superior code retrieval, simplified operations, and provided the scalability needed to process billions of tokens daily. McKesson McKesson, a global pharmaceutical distributor, replaced its monolithic legacy infrastructure with MongoDB Atlas to meet strict drug tracing mandates. By adopting our modern cloud data platform, McKesson scaled its operations 300x, managing tracking data for 1.2 billion containers annually without latency, and ensuring compliance and patient safety while reducing developer complexity. For more, check out the video of McKesson at MongoDB.local NYC from September. From niche NoSQL to enterprise powerhouse As senior MongoDB engineer and Technical Fellow Ashish Kumar put it earlier this year, “through a sustained and deliberate engineering effort,” MongoDB has gone from a (seemingly) niche NoSQL solution to a trusted enterprise standard, and now delivers “the high availability, tunable consistency, ACID transactions, and robust security that enterprises demand.” A new era of leadership The face of MongoDB has also changed—our CFO, Mike Berry, joined the company in April, and Dev Ittycheria stepped down as CEO in November, after more than 11 years leading the company (including its 2017 IPO). In a LinkedIn post about his role, new MongoDB CEO CJ Desai noted that the company is “at the forefront of a new data revolution, unlocking the next wave of productivity and intelligence.” “Having spent my career building and scaling technology platforms, I’ve always been drawn to companies defined by clarity of vision, relentless organic innovation, and a customer-first culture. MongoDB exemplifies all three,” said Desai. We couldn’t agree more. Onward! Reading the 2026 tea leaves So what might 2026 bring (for MongoDB and tech at large)? Here are a handful of our leaders’ predictions: “As much as people want to talk about Artificial General Intelligence (AGI), we’re still in the phase where most AI use cases automate redundant tasks but benefit from human-in-the-loop checks. Organizations that use AI to complete work that historically is a drain on human resources—but then uses people to carefully verify what AI builds, apply governance frameworks, and maintain accountability across the data lifecycle—will be more successful.” —Pete Johnson, Field CTO, AI, MongoDB “After years of inflated expectations and unsustainable spending, the AI industry is trapped in a bubble where companies reflexively attempt to deploy LLMs at every problem, driving up costs with minimal to no return. Businesses that break free from this spending cycle are the ones that understand the need to ground LLM responses in factual data and learn from prior mistakes. We believe the best way to do this will be with highly accurate embedding models and rerankers for reliable data retrieval.” —Frank Liu, Staff Product Manager, MongoDB "In 2026, cloud independence will evolve from strategic preference to existential imperative across enterprises of every scale. The outages and disruptions of recent years have exposed a fundamental truth: in an always-on digital economy—where commerce, mobility, governance, and even public safety depend on uninterrupted access to cloud services—single-provider reliance is no longer a calculated risk, but a systemic vulnerability. Compounding this is the inexorable rise of data sovereignty. Regulatory regimes worldwide now demand precise jurisdictional control over data residency, rendering rigid cloud commitments incompatible with compliance at global scale. The defining competitive advantage will belong to organizations that transcend fragile prevention theater and engineer true infrastructural resilience: architectures inherently portable, data frictionlessly mobile, and operations autonomously sustained across heterogeneous clouds through AI-orchestrated redundancy. In short, the winners will not merely mitigate downtime—they will design systems that render the concept obsolete." —Ben Cefalo, SVP, Head of Core Products, MongoDB Happy holidays and happy New Year, everyone!
MongoDB Announces Leadership Transition
Dev Ittycheria, President and Chief Executive Officer, shared the following message with MongoDB employees this morning. This is the hardest email I have ever had to write to all of you. If you have not seen the announcement, I have decided to retire as CEO. Effective November 10, 2025, Chirantan “CJ” Desai will become the new CEO of MongoDB. This was not an easy decision for me. The process to get to this point has been deeply emotional, as I care profoundly about MongoDB and the people who have made the company what it is today. This news may come as a surprise, and for some, perhaps even a shock. That’s natural. Leadership transitions can evoke a range of reactions. I want to share why this is happening, and why it’s the right thing for MongoDB. Every personnel change, including the most senior leadership changes, involves two key decisions: first, recognizing that it is the right time for change, and second, selecting the best person to replace the person leaving. This email is intended to explain both decisions. Earlier this year, as part of our regular succession planning process, the Board and I discussed my long-term commitment. They asked if I would continue as CEO for another five years. After many conversations with my family and the Board, I realized I could not make that commitment. Some CEOs see their title as their identity. I do not. My core responsibility is to serve in the company's best interests. The company is primed for a new leader. One with a fresh perspective, grounded in experience and skills needed to guide MongoDB through its next evolution as a company, what we call MongoDB 3.0. Consequently, I informed the Board that I would commit to two more years to help find a successor. That began the search process for a suitable successor. To our surprise and delight, what we thought would easily take 12 to 24 months happened much faster than anyone expected. After engaging with multiple qualified candidates, we found the right successor in CJ. CJ is uniquely qualified for this role. CJ brings the rare growth-at-scale experience that will help continue to build MongoDB into an iconic technology company. At ServiceNow, he was the only executive to work directly with three of its highly regarded public company CEOs and played a pivotal role in organically scaling the company from just over $1 billion to more than $10 billion in revenue. Only a handful of independent software companies have ever reached that milestone. CJ helped transform ServiceNow from a product company to a platform company, scaled engineering, drove go-to-market excellence, and engaged deeply with investors. More recently, as President of Product and Engineering at Cloudflare, he helped fuel strong growth and stock performance. CJ also possesses the personal qualities needed to succeed as CEO. He is humble, eager to learn, and wants to draw on the perspectives of the people at MongoDB and other stakeholders to inform his thinking. This blend of experience, judgment, and character gives me full confidence that he is well-equipped to lead MongoDB through its next phase of growth. I often think of MongoDB’s journey as a long and extraordinary expedition. For the past eleven years, I have had the privilege of serving as its guide, helping chart the course, rally the team, and climb together through both calm and challenging terrain. Along the way, we have reached remarkable summits and proven what is possible through relentless innovation, persistence, and teamwork. Now it is time for a new guide to lead the next stage of the ascent and take MongoDB to even greater heights. CJ is the right leader to take MongoDB to the next summit. MongoDB is on a strong footing, with a clear strategy, an exceptional leadership team, a product platform that is more relevant than ever, and a business that is executing well. The rise of AI and the explosion of data-intensive applications play directly to MongoDB’s strengths. Our technology sits at the center of how modern applications are built and how organizations will harness data to power intelligent, adaptive systems. I am confident MongoDB is perfectly positioned to capture this next wave of innovation. As for me, I am not running away from MongoDB or leaving to join another company as CEO. I will remain on the Board and work closely with CJ to ensure a seamless transition. Over the years, this role has demanded an enormous amount of focus and energy; as a result, there are many things I’ve missed doing along the way. I’m looking forward to being more present for those moments — from simple time with my family to experiences and travel we’ve long put off. I plan to hold on to my MongoDB stock, as I firmly believe in the people and the opportunity, knowing that MongoDB’s best days are ahead of it. Yes, change can be unsettling. I’m sure you will have many questions about this change, such as why now, why CJ is the best person to lead the company, and what this means for you. We will hold an all-hands meeting tomorrow at 10:30AM ET to discuss this transition, introduce CJ and take your questions. That being said, I want to emphasize that the right change at the right time is how great companies get stronger. Just as a championship team refreshes its roster to stay competitive, MongoDB is bringing in new leadership, including other recent C-suite leaders who came before CJ, to drive our next phase of growth. This is not an ending; it’s the founding of a new moment. I am incredibly proud of what we have built together and genuinely excited about what lies ahead with CJ leading us forward. I also want to thank each of you for making this journey so meaningful. Words cannot fully capture my gratitude for your passion, creativity, and belief in building something truly special. I have often said that I want MongoDB to be an inflection point in people’s careers, a place where they can grow, take risks, and do the best work of their lives. I can say without hesitation that it has been exactly that for me. The skills I have developed, the experiences I have gained, and the relationships I have formed here have shaped me more than any other chapter in my professional life. I will carry them with me always, and will continue to cheer for and support MongoDB every step of the way. --Dev
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.