MongoDB

Today at MongoDB.local Bengaluru, we announced new capabilities designed to help teams move AI into production with increased accuracy and deployment flexibility. Here’s what shipped: ✅ voyage-context-4 ✅ Public Preview: Native Reranking ✅ Search and Vector Search in MongoDB Enterprise Advanced and Community Edition ✅ Hybrid Search ✅ Apache Iceberg Support in Atlas Stream Processing ✅ Gen2 Atlas M30+ Dedicated Clusters on AWS See these and more: mongodb.social/6012BDakGp #MongoDBlocal

Voice AI is scaling fast. @ElevenLabs is showing what that looks like. ElevenLabs has evolved from breakthrough voice models into a platform for interactive, enterprise-ready AI agents. With MongoDB, the company replaced a more complex architecture with a unified data layer, making it easier to support high-accuracy speech workflows and chat experiences with less operational friction. See how they unlocked scale with MongoDB: mongodb.social/6018BELOzG

10 years. Thousands of customer outcomes. One platform built for what’s next. As MongoDB Atlas reaches its 10-year milestone, we’re reflecting on the customers behind that journey—the organizations using Atlas to innovate faster, work with complex data, and build resilient, AI-ready applications. MongoDB Chief Customer Officer Erica Volini reflects on how customers are building what’s next with MongoDB Atlas: mongodb.social/6017BELofx

Great interview with @MollySOShea and @ScottWu46.

How did Kaax go from prototype to production in under four months? Kaax, built on MongoDB Atlas, is helping agricultural teams across Latin America turn drone and satellite imagery into same-day action. Kaax scaled from early validation to enterprise production without rewrites, migrations, or downtime. The results: -15,000+ hectares processed -Up to 320x faster analysis -Up to 84% cost savings in replanting operations See how they did it 👇

A legacy stack wasn’t going to power an AI-native future. mongodb.social/6011BEai8U founder Samantha McBride shared how the team moved to MongoDB to build a more flexible, secure platform for financial services, making it easier to deploy AI agents without restructuring client data. Learn how mongodb.social/6011BEai8U is building on MongoDB: mongodb.social/6012BEai8q

@denizrudin You’re welcome @denizrudin.

WHY are there NO mongodb billboards in SF??? @MongoDB

The Build Fest agenda just dropped. ⚡️ Six hands-on workshops. Deep dives on agent memory architecture. A chance to learn from AI leaders at companies like @rivian, @adobe, @mentraglass, and more. And we're not done announcing. San Francisco, Pier 48, August 13. 👉 mongodb.social/6014BEsBSw

ICYMI: MongoDB Atlas is now a native part of the @Azure experience! As an Azure Native Integration, Atlas is discoverable in the Azure Portal alongside first-party database services. Provision it, manage it, and bill it directly through your existing Azure invoice. MACC eligible, no separate account needed. New as of July 2026: Org, Project, and Cluster creation is now fully self-service from the portal, with Org creation available in 10 regions. Learn what this means in practice, plus how to use Fabric for analytics on your Atlas data and Foundry to build AI agents on top. Watch the replay: mongodb.social/6014BEo8qu

What if Raft could deliver fast, consistent reads without the usual lease complexity? The new MongoDB Research Blog launches with its first post from @jessejiryudavis on LeaseGuard, a simpler approach to Raft leases designed to reduce read overhead, improve read availability, and restore write throughput quickly after leader failover. Discover here how to recover reads faster and move past the downtime: mongodb.social/6010BElHtC

@deerchaudhary Hi Deepak, I understand that you might need some help. On the MongoDB website, please get in touch with the Atlas Support team immediately. The team is there to assist you and will be more than happy to help.

Calling on the @MongoDB community. Has anyone successfully recovered data from a MongoDB Atlas Free Tier after an accidental deletion? Our production data was deleted on 21 July 2026, and we’re looking for any possible recovery options or advice. Looking for help @MongoDB

Build smarter with Codex + MongoDB 🚀 Stop context-switching and start interacting with your data directly. With the official MongoDB Codex Plugin, your agents connect to your MongoDB database via the MCP Server and use pre-built skills for data modeling, query optimization, MongoDB Search, Vector Search, and more. Check out the video and start working smarter today: mongodb.social/6018BEdVnq

Fresh off MongoDB.local Bengaluru, the latest episode of Between Two Leaves takes you through 7 new launches: -Hybrid Search GA -Search & Vector Search GA everywhere -Gen2 Atlas Clusters -Iceberg support -Native Reranking preview -voyage-context-4 🎥 Watch now! mongodb.social/6014BEciSG

NEW: Inside AI's Biggest Downstream Winner.. the Surge in AI Database Demand "Data is the unsung hero, & data is back." @MongoDB CEO CJ Desai (@cj_mongodb) The unexpected result? Hyperscalers are turning away even top-50 accounts. "Sorry, we don't have a capacity." "And they are one of the top 50 customers for that hyperscaler." ElevenLabs alone runs "north of 50 million agents, depending on when you look at it, all running on MongoDB.. that gives us a lot of confidence that we have the right architecture for agentic workloads." NASDAQ: $MDB We cover: › The 3 classes of AI customers MongoDB serves › Why hyperscalers are telling top-50 accounts "no capacity" › On-prem, sovereign AI, and the data-center comeback › ElevenLabs running 50M+ agents on MongoDB › MongoDB (OLTP) vs Snowflake & Databricks (OLAP) › Why there is no standardization in enterprise AI models › Auto-scaling & the fall of expensive DBAs 𝐓𝐈𝐌𝐄𝐒𝐓𝐀𝐌𝐏𝐒 (00:00) CJ Desai, CEO at MongoDB (01:12) Top CEOs at the Raise AI Summit (03:24) The 3 types of companies powering the AI boom (07:25) Why on-prem is making a shocking comeback (09:01) Hyperscalers are quietly running out of capacity (11:06) What actually separates MongoDB from Snowflake x Databricks (13:18) Why Frontier Labs treat MongoDB as their memory layer (16:22) From Oracle intern to first-time CEO (19:19) Becoming CEO during the AI chaos (23:53) The World Cup crisis that tested MongoDB's scale (28:19) The real complexity behind simple AI agents (31:22) Open source vs. closed models: what customers actually pick (34:36) CJ's honest take on data centers in space (36:50) The mentors who shaped a first-time CEO (40:32) MongoDB's next big bets (41:36) Data is back?

Most retrieval pipelines send too much to the LLM. Not because builders want them to, but because filtering low-relevance results used to mean adding a separate vendor, a new API, and more pipeline complexity. Most teams decide it isn't worth it. Native Reranking in MongoDB Atlas changes that. ⭐️ Now in public preview: a $rerank stage that runs inside the aggregation pipeline, powered by @VoyageAI by MongoDB, with no external round-trips. Up to 30% better retrieval quality on the MAIR benchmark, nothing added to your stack. See how native reranking fits your pipeline → mongodb.social/6010BEj6op

San Francisco, it’s time to build. Bring your next big idea to Build Fest. A full day of hands-on workshops, bootcamps, live demos, and 1:1 office hours — building agents and AI apps directly on MongoDB, with the people who build the platform. See you August 13 👉 mongodb.social/6019BEdifU

🎉 We're celebrating 10 years of MongoDB Atlas! Hear from Benjamin Flast, Senior Director, Product Management at MongoDB, on how Atlas grew from a managed cloud database to a unified data platform used by nearly 70,000 organizations around the world. See what's next for Atlas: mongodb.social/6014BEkfn2

Did you hear? Search and Vector Search for MongoDB Enterprise Advanced (EA) is now GA. 🛡️ Deploy fully integrated advanced search and semantic retrieval for mission-critical workloads on-premises and in private or hybrid clouds. Experience it today: mongodb.social/6017BEbhRR

When teams call their AI agent "unreliable," they usually mean one of two things: it returned a wrong answer (a memory or model problem), or it can't resume work it already finished (a state problem). Most teams govern state and memory under one lifecycle and one store, and that collapse is behind a lot of the "unreliability" they're seeing. Research on real agent failures keeps landing on the same conclusion: replaying an agent's steps after a crash isn't safe, because the agent can "redo" a task slightly differently the second time — even double-charging a customer in one case. The fix isn't a better tool, it's replaying the recorded decision instead of asking the agent to decide again. Learn the five state architectures teams ship in production, with the trade-offs of each 👉 mongodb.social/6010BEb37j

Chatbots are old news. Agentic AI is the real shift — autonomous systems that execute multi-step workflows, not just chat. We break down the 3 waves of AI evolution: 🧠 LLMs — built-in knowledge 🔍 RAG — context-rich retrieval 🤖 Agentic AI — fully autonomous, multi-step execution Plus, a real telecom use case showing how MongoDB powers it all as the stateful backbone behind high-volume, unstructured data and vector search. Watch the full video: mongodb.social/6018BEkA26