MongoDB is the intelligent data platform the AI era.
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MongoDB just announced Q2 FY2027 earnings.
📈 Total revenue of $772M, up 30% YoY.
☁️ MongoDB Atlas revenue grew ~29% YoY
🚀 EA & Other revenue grew 36% YoY
🤝 2,900 additional customers for a total of more than 70,600 total customers
🔍 @VoyageAI customers nearly doubled QoQ
We are raising full-year 2027 guidance due to strength in MongoDB Atlas. Learn more: mongodb.social/6019B17bFL
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Big stage, big names. Here's who is taking the stage for #MongoDBlocal NYC 👇
→ @dittycheria, Interim President and CEO
→ Erica Volini, Chief Customer Officer
→ Ashish Kumar, Senior Vice President and Technical Fellow
→ @bencefalo, Chief Product Officer, Core Products
They'll be joined by customers like @okta, @ACI_Worldwide , @Wayfair, @coinbase, and more for a full day of talks, demos and technical sessions, all focused on real AI use cases.
🗽✨ Full agenda: mongodb.social/6015BGXEFL
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Welcome back, @dittycheria, as interim President and CEO!
No company has meant more to me professionally than MongoDB. I spent more than 11 years as CEO, and even after stepping down last year, I never really left. Serving on the board kept me close to our people, our customers and the business, and only strengthened my conviction in MongoDB’s future.
Today, I’m returning as interim CEO. I’m grateful for the trust of our board and our team, and I’m ready to get to work.
I also return with a different perspective. As a Partner and Senior Advisor at Sequoia Capital, I have a front-row seat to the founders, technologies and companies shaping the AI era. I see firsthand how AI is not only changing how software is built and businesses operate, but expanding technology’s role from helping people manage work to increasingly helping perform the work itself. I look forward to bringing those insights back to MongoDB as we help our customers capitalize on what may be the most consequential platform shift of our generation.
And we’re getting started immediately. Tomorrow at 11 AM ET, we’ll host Investor Day and share our long-term strategy, market opportunity and the product innovation driving MongoDB forward.
On Wednesday, our sold-out .local NYC event will bring together developers and business leaders focused on putting AI into production and moving from generating answers to taking action at scale. We’ll have exciting announcements at both events.
What has always made MongoDB special is the ambition of our people, the importance of what our customers build with our technology, and the size of the opportunity ahead.
I’m proud to lead this team again. There is a lot to do, and I couldn’t be more excited to get back to work.
mongodb.com/company/newsroom…
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Testing a dozen database drivers in a dozen languages against a dozen specs "by hand" means specs drift, APIs diverge, and bugs slip through silently.
We cut spec-nonconformance bugs by fixing this with a shared YAML test language on top of MongoDB's driver architecture: one Unified Test Format that every driver's test runner speaks, so a single suite of ~124,000 lines of tests enforces identical behavior across Python, Java, Rust, and the rest.
Learn how we did it: mongodb.social/6016BGiRLY
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Dear ______,
Please excuse ______ from work on Sept. 30, 2026.
Some days you have to trade the desk for a front row seat, and MongoDB.local NYC has one with ______'s name on it. A whole day with the people actually building the future of AI, not just talking about it.
We appreciate your understanding and hope you can respect a day spent in build mode.
Thank you.
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"'Best effort' uptime is simply not an option" — that's how @PaloAltoNtwks' Principal SRE describes the bar for their Prisma SASE Health Portal.
To hit it, they retired their relational database for a MongoDB Atlas cluster spread across three regions, with customer subscriptions modeled as flexible JSON documents instead of rigid tables.
✔️ Zero-downtime failover across region
✔️ Microsecond alert matching, no JOINs
✔️ New services onboarded without schema migrations
Learn how they built a zero-downtime, resilient status page: mongodb.social/6019BGdrVZ
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🗣️ Overheard at The Persistent Context Sprint Hackathon at MongoDB.local Build Fest, in partnership with @cerebral_valley, @cursor_ai, @OpenRouter, @ElevenLabs, @FireworksAI_HQ, and @LangChain:
"...anyone with a real problem and a willingness to start can build something that matters."
NYC, your chance is next on Sept. 26th with finalists taking the stage at #MongoDBlocal on Sept. 30. 🗽
Save your spot: mongodb.social/6016BGeDSI
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Retrieving 20 chunks "to be safe" burns ~10K tokens a call on mostly noise, and it often makes answers worse, not better.
We cut that by 80% with a simple pipeline on MongoDB Atlas and @VoyageAI: quantized embeddings to shrink storage and reranking to send only the top few chunks to the LLM.
Get the full breakdown: mongodb.social/6016BGV3xo
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AI starts with the data.
And if your data foundation is stuck in the past, your AI ambitions could be too.
New IDC research reveals that APAC organizations leading the way in modernization are generating nearly 3x the regional average in digital revenue.
Explore what it takes to modernize legacy infrastructure and build a foundation for AI.
Read the research: mongodb.social/6018BGpoHw
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MongoDB Atlas is turning 10! Gabriel Woo, Regional Vice President Greater China Region at MongoDB, shares his "Atlas moment:" watching enterprises like Cathay Pacific move mission-critical workloads to a fully managed, run-anywhere platform.
Learn more about what's next for Atlas: mongodb.social/6010BGph5Q
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Your archive is an untapped opportunity.
@brahma_ai helps media and entertainment companies orchestrate content end to end, from creation and management to distribution across social, broadcast, and theaters.
However, unlocking the value of massive content libraries requires flexibility.
That’s why Brahma AI moved from a traditional RDBMS to MongoDB, creating a foundation for complex media metadata and AI-enriched content.
The result? AI agents can help enterprises discover content that was once “sleeping” in their archives, and turn it into something they can actually use and monetize.
Read how Brahma AI is modernizing media content workflows here: mongodb.social/6015BGHnPF
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💭 What are you building with MongoDB right now?
Tell us in r/MongoDB_Official, our new home for the builder community to connect, ask questions, and share wins. 👉 mongodb.social/6010BGRaSm
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“Every system an agent reaches into is another credential and another place for that view to drift.” - Jess Yan, Product Lead for @claudeai Managed Agents at @AnthropicAI
Fraud review needs semantic precedent, exact-match lookups, and network traversal on the same case. This is usually handled by stitching together three or four separate systems, each with its own credential for the agent to manage.
In this new tutorial from Sai Teja Boddapati, Staff Solutions Architect at MongoDB, you’ll learn how to build a human-in-the-loop fraud-review agent on Anthropic’s Claude Managed Agents, using one MongoDB Atlas cluster as its retrieval engine, graph store, and system of record.
This stack ensures that your agent is always working from one source of truth, so there's nothing separate to fall out of sync and risk feeding it stale context.
Everything you need to get started: mongodb.social/6010BGMQLC
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Regulatory and standards content can be difficult to search when requirements are revised, referenced differently, or spread across multiple sources.
For automotive engineering and compliance teams, MongoDB Atlas combines lexical and vector search to help users find relevant CMVR and AIS content, while preserving the context needed to verify what they’ve found.
Key takeaways:
✅ Hybrid search supports both exact identifiers, like rule numbers, and natural-language queries.
✅ Provenance stays intact, including rule/section, revision, effective date, and source locator.
✅ The approach scales across standards, including CMVR/AIS, UNECE, FMVSS, and GB.
✅ Search helps users find and verify source material, it does not provide legal, regulatory, or compliance advice.
Explore the solution:
mongodb.social/6018BGMCz0
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The shopping agent in your favorite app understands what you want, checks what's actually in stock, and finishes checkout. 🛍️
Built with @crewAIInc + MongoDB Atlas Vector Search.
Full breakdown, code included 👉
mongodb.social/6014BGzY7u
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“What if we changed this?” For network operators, that question can have serious consequences.
AI agents could change the equation, letting teams explore scenarios, understand dependencies, and test their next move before putting it into production.
From intent to what-if, the future of network operations is becoming more predictive.
Read the blog: mongodb.social/6018BGzOS6
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MongoDB Atlas is now a connected data source in @OpenAI's Data agent for @ChatGPT Work, powered by the MongoDB Atlas Managed MCP Server.
Analysts and business teams can now point the Data agent at their live Atlas clusters and ask what changed, why it changed, and what to look at next, then turn the answer into a report or real-time dashboard without leaving the chat.
Get started: mongodb.com/company/blog/pro…
Now everyone can put data to work.
We’re introducing a new Data agent in ChatGPT Work so you can turn your company’s data into answers, interactive dashboards, and action—just by asking.
Just add the Data Plugin in ChatGPT Work, connect to the data sources and context you already use, and start the conversation. openai.com/index/put-data-to…
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One click. That's all it takes to now open MongoDB Atlas directly from @vercel.
If you're using the MongoDB Atlas Vercel Native Integration, you can now jump directly from Vercel to your Atlas organization or project, using the credentials you already have.
Learn more: mongodb.social/6018BGJsWO
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Set your OOO for MongoDB.local NYC on Sept. 30. ✨
One day, one room, one chance to hear from the experts putting effective AI agents into production.
🏙️ Register now: mongodb.social/6017BGB7a7
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MongoDB now powers the virtual file system behind @LangChain's Deep Agents. 🚀
This new integration lets you implement Deep Agents' BackendProtocol against MongoDB Atlas instead of building your own storage layer. And agent code calling read_file, write, glob, and grep don’t have to change.
Your retrieval and storage run through one backend instead of a maze of stitched-together tools. Vector, full-text, and hybrid search (via $rankFusion) route through Atlas; file reads and writes forward directly to S3.
For builders of long-running or multi-agent workflows: Plans, intermediate outputs, and knowledge artifacts persist across sessions, deployments, and sub-agent handoffs, so file-based context survives between runs instead of resetting each time.
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Learn more about this integration and how to get started: mongodb.social/6012B1C0e0
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