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10 Years of MongoDB Atlas: Built for What’s Next
June 25, 2026
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How Indian Organizations in Regulated Industries Are Scaling with MongoDB
Scaling while ensuring regulatory compliance is a major challenge that many Indian organizations today face, particularly when operating in industries like financial services, retail, education, and aviation.
Leading Indian Organizations Are Driving India’s AI Future With MongoDB
Millions of Indians now use AI on a daily basis. It’s been said before, but the technology’s possibilities are incredible, whether you’re using AI to build an app with just a few sentences, or whether a virtual assistant is not only answering your questions but actually anticipating your needs.
Close the Database Blind Spot with MongoDB Atlas Observability
Modern applications generate more telemetry than ever before, yet one of the most critical systems in production often remains disconnected from the rest of the observability stack: the database. When incidents occur, operators shouldn’t have to jump between dashboards, stitch together signals from disconnected systems, or rely on brittle integrations just to understand what’s happening. Database telemetry should be part of the same operational picture as application and infrastructure telemetry.
Closing the Gap Between Agentic Promise and Impact with Industry Leading Retrieval
A recent McKinsey report* noted that AI agents and robots “could generate about $2.9 trillion in US economic value per year,” while another study from WRITER** quantified the speed at which organizations have moved toward agents: 97% of C-suite executives surveyed indicate their companies have deployed AI agents, and 75% expect agents to become members of the C-suite in the next five years.
MongoDB for the Agentic Era: Built for Developers and AI Agents
How developers work has dramatically changed. Anthropic's 2026 Agentic Coding Trends Report* found that developers now use AI in roughly 60% of their engineering work. Coding agents are no longer just executing decisions; they're helping make them. If you’re building software today, it has to work for both humans and the agents working alongside them.
How Greater China Organizations Power AI And Compliance At Scale
Organizations across Greater China are heavily investing in data-driven technologies to deliver innovative digital services and experiences. According to Morgan Stanley Research, AI investments in China alone could deliver a 52% return on invested capital by 2030*.
MongoDB Atlas Delivers Greater Capacity, Reliability, and Operational Confidence
Production reliability is rarely lost in a single dramatic moment. More often, it is challenged by the realities operators face every day: cloud capacity constraints that delay critical scaling operations or the need to recover a single collection without disrupting an entire environment.
Data Sovereignty Is a Spectrum. Your Strategy Should Be Too.
When organizations talk about “data sovereignty,” they are usually expressing concerns about how concrete legal and regulatory regimes apply to their workloads—from data protection laws and sectoral localization rules to requirements around exit strategies and third‑party access.
Atlas Stream Processing Brings Operational Data to Apache Iceberg
Every order placed, message sent, or sensor reading captured lands in an operational database. Analysts, data scientists, and AI teams all want that data, because the freshest signal about a business lives in the systems running it. But getting this data from the application layer to the lakehouse, continuously and at scale, has been one of the most stubborn problems in the modern data stack.
Improving Agent Retrieval with Native Reranking and Hybrid Search
In production AI, what the system retrieves shapes everything that follows. It determines whether an application surfaces the right context in the first place and how much irrelevant information gets passed to an LLM. That affects two things you care about most: answer quality and cost.
MongoDB Search and Vector Search Now Run Anywhere
MongoDB is excited to announce the general availability of MongoDB Search (full-text) and Vector Search for MongoDB Enterprise Advanced and MongoDB Community Edition. Since announcing the public preview at MongoDB.local New York last year, these milestones represent MongoDB’s commitment to ensuring developers can build intelligent, search-driven applications wherever their data lives without compromising on capability, data control, or infrastructure flexibility.
Retrieval Accuracy Is Now a Competitive Advantage
The token economy is here. And as soon as you have an economy, people start budgeting. So it’s no surprise that most conversations about AI now come back to the token budget. Teams are being asked to show the value of inference—the action phase of AI—spend and justify the cost. The answer traces back to the data that was retrieved and fed to the LLM. This step is intuitively referred to as “retrieval.”
Run AI Wherever Your Compliance Framework Demands
The CTOs I talk to in regulated industries aren’t debating whether to build with AI. That decision has been made. What they're navigating is a harder question: how do you build AI at the standard your compliance framework requires, when most of the tooling was designed for environments you're not allowed to use?
MongoDB to Upskill Two Million Builders in India by 2030
Organizations across India are racing to take AI applications from experimentation to full-scale production, but the bottleneck is almost never the AI model—it's the data layer. That's why data infrastructure skills are so important to the AI economy, and it's why we're so excited to share that today at MongoDB .local Bengaluru, we announced plans to upskill two million Indian builders by 2030 through new local language initiatives and regional partnerships. The expanded MongoDB for Academia program will help equip the next generation of builders with the cloud, data, and AI skills required to scale India’s digital economy.
10 Years of MongoDB Atlas: Built for What’s Next
Nearly a decade ago, I joined MongoDB as a Senior Product Manager to help build the company’s new cloud product, MongoDB Atlas. Our customers had been telling us they wanted to bring MongoDB’s familiar developer experience to the cloud, with the reliability and confidence teams needed to run in production. Atlas was our answer.
BAAC Transforms Rural Banking With MongoDB-Powered Mobile App
One of Thailand’s largest state-owned banks, the Bank for Agriculture and Agricultural Co-operatives (BAAC), serves over 32 million customers and 7 million farmer households across Thailand. BAAC is focused on rural development, and the bank provides essential funding, knowledge, technology, and value-added services to Thai farmers.
Celebrating Graduation Season with MongoDB Student Projects
Welcome to a showcase of student innovation from KL University Hyderabad! In this blog post, we spotlight outstanding projects developed by KL University Hyderabad students using MongoDB in their Python Full Stack Development curriculum. These projects exemplify the creativity, technical expertise, and social consciousness of the students. Under the mentorship of Professor Chanda Raj Kumar, the integration of MongoDB into the students’ Python Full Stack Development journey has not only expanded their technical skillset but also allowed them to address pressing real-world challenges.
Production-Ready Agents Need A Production-Ready Data Platform
There’s a common theme to the conversations I’ve been having with AI teams lately: change. Constant, head-spinning change. Teams across industries are evaluating and re-evaluating model providers, agent frameworks, and harnesses on a continuous basis. At MongoDB, we believe that your choice of technology partner—specifically, your data platform—should simplify how you build with AI. It should deliver performance at scale, enable you to build and run anywhere, and it should allow you to choose your own providers and frameworks. This is exactly what MongoDB offers, and it’s why more than 67,000 customers rely on us for their most important applications. The organizations seeing the most AI success are the ones whose technology stacks are set up for the current pace of change. For example, DevRev’s AgentOS platform is powered by MongoDB Atlas. AgentOS handles billions of requests each month, for everything from AI-assisted insights and analytics to internal communications and development. Relying on MongoDB Atlas has helped DevRev get innovations to market faster, and enables the company to scale seamlessly as it grows. MongoDB is ideal for agentic AI in two key ways. First, an agent is only as smart as its context—which requires blending short-term memory, long-term knowledge, and enterprise data. Because this information is highly dynamic and unstructured, JSON is the ideal format. It provides the schema flexibility inherently needed by the data and allows attaching metadata like IDs and confidence scores. MongoDB stores JSON natively and provides the scale and consistency required to run thousands of concurrent agents. Second, it’s designed for how agents work. As memory accumulates, agents must pinpoint the precise context needed for a request. MongoDB solves this by providing state-of-the-art information retrieval capabilities (search, vector search, hybrid search, embeddings) directly where the operational data already lives, eliminating the need to constantly sync data across separate systems. Customers get high-precision semantic retrieval without the operational headache of managing multiple fragmented products. A good example of how MongoDB powers agents is ElevenLabs. The company relies on MongoDB Atlas to power the long-term memory and knowledge base for its autonomous agents. By leveraging Atlas Search and Vector Search, ElevenLabs enables their agents to retain complex context and deliver highly personalized interactions in real-time. Adobe, meanwhile, chose MongoDB as the long-term memory and reasoning layer for Journey Agent, its composite multimodal AI agent that unifies Adobe's marketing suite and orchestrates end-to-end customer journeys. Adobe leverages MongoDB Atlas Search and Atlas Vector Search together to power the sub-100 millisecond hybrid search the agent needs to act in real time. Defining an open standard for agent memory Last month, MongoDB partner LangChain announced the launch of Context Hub in LangSmith, a place to store, version, and collaborate on the files that define how agents behave, like AGENT.md and agent skills. But context engineering goes beyond that. Agents also rely on memory: short-term context captured in states, sessions, and interaction history, and long-term memory that persists across sessions. Figure 1. Agent memory with MongoDB. Production-Ready Agents blog image 1 media Today, there is no broadly adopted open standard for defining and managing portable agent memory across agent frameworks. Now, MongoDB—alongside LangChain and ecosystem partners—is working on an open reference architecture and contributing toward greater interoperability in this space. This work will help define what has been missing from the AI ecosystem: shared interfaces, metadata conventions, versioning patterns, and retrieval semantics for the data that differentiate agentic experiences and shape agent behavior. The aim is to enable organizations to switch model providers or try a new framework on a Tuesday—and not lose Wednesday rewriting memory plumbing. Ultimately, we want to make agent memory and context easier (and faster) to manage. For customer-facing agents to make real-time decisions, such as responding to a support request or making a policy change, they need contextual information instantly. Not info from a data warehouse that might be 30 minutes old. The context layer needs to be real-time, a required capability we’ve been delivering for tens of thousands of customers going on almost two decades. MongoDB’s performant, flexible platform = agentic success The next generation of agents will increasingly be long-horizon systems, running for hours or more. As they take on more complex tasks, context will become even more critical, and agent memory will be central to making them effective. This will create a demand for diverse, high-performance memory systems, and MongoDB is positioned to provide the flexibility and scalability agents require. With the recent release MongoDB 8.3, our core database has evolved to better support the speed and demands of AI workloads. MongoDB also delivers the retrieval accuracy necessary for agent outputs to be trusted (a non-negotiable for customer-facing applications) while optimizing tokens and cost in production. Every AI team is currently making a bet about what the future of the agentic stack will look like. The ones betting on a flexible, production-ready data platform like MongoDB—that enables teams to innovate now while ensuring structure and resilience for the future—will be able to pivot quickly. The ones betting on rigid schema designs, or on a smattering of specific models and frameworks, might end up redoing their plumbing instead of shipping products. Figure 2. Advantages of MongoDB's flexible schema for AI workloads. Production Ready Agents Blog - Image 2 media MongoDB is built for AI: JSON is the lingua franca of AI. The information agents need is highly dynamic and can be structured, semi-structured, and unstructured. MongoDB provides the schema flexibility inherently needed by the data and allows attaching metadata for richer, more precise context. Dynamic, adaptive schemas that evolve in place as fast as thought without breaking what runs on top. The MongoDB document model isn’t adapted for AI; it’s the natural shape of AI data. MongoDB offers one data platform: Every data requirement for production AI is natively integrated. Search, vector search, embeddings, hybrid retrieval, time series, and streaming run on the same OLTP foundation 67,000+ customers trust with mission-critical applications—with one API, one security model, one operational footprint.
TNL Mediagene Builds a Data-Driven Content Platform with MongoDB Atlas
TNL Mediagene is a technology and digital media company providing AI-driven advertising, marketing technology, content commerce, and data analytics solutions, and operating multi-language digital media brands across Asia, with operations in Taiwan and Japan. Its portfolio features leading Chinese, Japanese, and English-language media brands, including The News Lens, Cool3C, Roomie, and Business Insider Taiwan. Widely recognized for its diverse content and in-depth reporting, the group is redefining media and news services in the digital age.
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.
Innovating with MongoDB | Customer Successes, April 2026
The energy across the tech industry is high, but it’s shifting. While the initial wave of excitement around generative AI was defined by what was possible in a sandbox, we are squarely in the era of execution. I spend more and more of my time talking to founders who are working to move past experimentation, and to build autonomous systems that handle high-stakes tasks without human intervention.
Security in the Age of AI
Everything we do at MongoDB starts with customers and we work backward from their needs. As a result, I frequently engage with customers, partners, and industry peers to proactively find new ways to collectively strengthen our defenses—ideally, long before any issues arise.
LG Uplus Works With MongoDB to Expand AI Services and Modernize Architecture
LG Uplus, a key subsidiary of LG Corporation and a leader in mobile, internet, and AI transformation, today announced that it will work with MongoDB to expand the use of generative AI and accelerate its modernization strategy across the company.
A Year of Momentum: Why MongoDB Is Winning by Collaborating with Google Cloud
Today, the race to deploy AI is at an inflection point—2026 is the year of the mandate, and making good on AI’s promise. Enterprises are no longer satisfied with chatbots; they are focused on building the next generation of AI applications: autonomous, agentic systems that can reason, act, and execute complex business logic.
Google Cloud Pub/Sub is Now Supported in Atlas Stream Processing
Atlas Stream Processing lets developers build real-time data pipelines directly within MongoDB, without stitching together separate streaming infrastructure. Today, we're extending that reach with native Google Cloud Pub/Sub sink support.
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.
Improved Multitenancy Support in Vector Search: Introducing Flat Indexes
The future of AI is personal. The more accustomed to AI tools users are, the more they want their experience of working with them to be personalized and agentic. Whether it is an AI assistant recalling your past conversations, a legal tool reviewing a specific company's contracts, or a personal knowledge base searching through your private documents, these applications all rely on one core capability: providing "memory" specific to a single user or business.