MongoDB Blog
Announcements, updates, news, and more
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.
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.
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.
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.
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.
Design and Develop Apps on MongoDB Faster with Google ADC
The journey from an initial vision to a production-grade application is often stalled by the infrastructure tax, those grueling hours spent navigating complex configurations and rigid data schemas. In the modern cloud landscape, the integration of MongoDB Atlas with Google Cloud’s Application Design Center (ADC) is helping to eliminate these hurdles by offering a unified path for visualizing, designing, and deploying cloud-native applications at scale.