MongoDB

/*Think outside rows and columns.*/

[MongoDB @MongoDB]

Arek Borucki started with a MongoDB University course and ended up as an ML Engineer at @huggingface. 🧑‍💻

What he shared about scaling MongoDB in production applies to your stack right now.👇

[MongoDB @MongoDB]

Most agent failures are actually data failures. Teams keep tuning prompts, swapping models, and adding guardrails when the real issue is data that isn't retrieval ready, stale context, or workflows that can't maintain state.

MongoDB Field CTO Pete Johnson breaks down the three layers where these systems actually fail: https://t.co/HKEJG4yYHr

[MongoDB @MongoDB]

"No matter what AI workload you run, you always need LLMs, a harness, and a data layer."

That's MongoDB President and CEO @cj_mongodb on @Bloomberg Open Interest, making the case that the data layer is where agentic AI lives or dies, and that it has to scale as those workloads scale.

MongoDB added 2,500 new customers last quarter, with frontier labs and AI-native companies like @ElevenLabs already building on MongoDB as their data layer.

Watch the full conversation with Dani Burger → https://t.co/bfGltCOJwy

[MongoDB @MongoDB]

How long does it REALLY take to earn our new Gen AI Skill Badges? We put it to the test at MongoDB.local London.

Can you beat her time? 👀

Start earning now: https://t.co/OTWIEtQOAC

[MongoDB @MongoDB]

“Lift and shift” isn’t modernization.

Today’s applications need to be modular, AI-ready, and built for real-time decision-making. Legacy systems can’t keep up with the demands of always-on consumer experiences.

Hear how organizations in APAC are modernizing faster, scaling in real time, and reducing the cost of maintaining legacy infrastructure in our newest report with @IDC: https://t.co/s0anfdrf7Y

[MongoDB @MongoDB]

@Dev_TheAnalyst Let's go! Welcome, Devin!

[MongoDB @MongoDB]

May was a busy month for our MongoDB Community members and startups! 💚

A BIG congratulations to:

MongoDB Champion of the Month - Abirami Sukumaran
MUG Leaders of the Month - Brice Fotzo and Abdul Rahman Masri Attal
Creator of the month - Matteo Rossi
Startup of the month - Pawbud

Read more about our winners: https://t.co/3iRhN3Xapg

[CJ Desai @cj_mongodb]

.@MongoDB just reported a strong first quarter.

Total revenue reached $688 million — up 25% year-over-year — driven by four consecutive quarters of more than 29%+ growth from Atlas.

We’re seeing momentum across many of our AI products: MCP server usage is growing significantly, @VoyageAI customers have more than doubled quarter-over-quarter, and Vector Search adoption is far outpacing overall company growth.

[MongoDB @MongoDB]

MongoDB just announced Q1 FY2027 earnings.

Highlights include:

📈 Total revenue of $687.6M, a 25% increase YoY
☁️ A 29.4% YoY increase in Atlas revenue, accounting for 75% of total Q1 revenue
🤝 2,500+ additional customers for a total of more than 67,700 customers

Read more here: https://t.co/aYNptvXMPT

[MongoDB @MongoDB]

Meet PlanPass AI - the winner of the Agentic Evolution Hackathon at MongoDB.local London!

In the UK, navigating building compliance for house design can be incredibly complex for builders and developers.

PlanPass AI uses an agentic workflow to:
-Scrape local council documents based on your post code to build a real-time "compliance intelligence" engine
-Create a summary of the probability of approval by the local council before you even submit
Create a summary of the probability of approval by the local council before you even submit
-Download your designs as PDFs or DXF files for AutoCAD

Powered by MongoDB and LangChain, PlanPass AI ensures your designs are fully compatible with UK building standards from day one.

Congratulations to our winners @RrekhaA, @david9887_, and Rostam Sodagari on the win, and to everyone who came out for the hackathon! 🏆

Watch the full pitch from MongoDB.local London 👉 https://t.co/71On1tbpDC

[MongoDB @MongoDB]

After talking to hundreds of customers and startups, @cj_mongodb sees three things that will hold in the AI wave: the model layer, the data layer, and the agent layer.

🎥 Watch the full interview with @HarryStebbings to learn why MongoDB is built to serve as the long-term memory and reasoning layer for the agentic era: https://t.co/AvoaGgOSEA

[MongoDB @MongoDB]

In every tech transformation, something changes.

But one thing has stayed constant.

Our President & CEO Chirantan @cj_mongodb joined @HarryStebbings of @20vcFund to discuss why data remains the constant across every major technology shift — from mainframes, to cloud, to AI.

Watch the full conversation: https://t.co/zIsFtdOItM

[MongoDB @MongoDB]

The future of AI in financial services won't be built on legacy infrastructure.
While "lift and shift" methods provided a quick fix in the past, true innovation requires a re-engineered approach. Learn how we're partnering with @Accenture to help financial institutions make the leap from mainframe dependency to AI-ready architecture.
Watch the video 👇
https://t.co/ZM30U98tp8

[MongoDB @MongoDB]

Everyone wants to “build an agent platform” until they realize they’re actually signing up to build:

→ Memory systems
→ Governance frameworks
→ Eval infrastructure
→ Orchestration layers

…while the entire AI stack changes underneath them in real time.

The best platform engineering decision this quarter might be deciding what not to build. MongoDB Field CTO Pete Johnson weighs in: https://t.co/2Iz5KDWLKW

[MongoDB @MongoDB]

🚗 Rivian Volkswagen Tech Group manages a growing fleet of connected vehicles — each capable of generating tens of GBs of data per hour.

Instead of collecting everything, they built a system on MongoDB Atlas that collects exactly the data they need, from exactly the right vehicles, without permanently increasing baseline telemetry or compromising regional data governance.

See how it’s built: https://t.co/svM2VsvkD5

[MongoDB @MongoDB]

LangGraph.js Long-Term Memory Store is now generally available.

This integration brings long-term memory across sessions, across users, and across time, with Atlas as the unified backend for checkpointing and semantic recall, powered by Voyage AI embeddings. 👇

https://t.co/uXEzA1Al34

[MongoDB @MongoDB]

ICYMI — We announced new capabilities at #MongoDBlocal London to deliver a unified AI data platform that gives enterprises everything they need to run agents in production. 🔥

From persistent agent memory with LangGraph.js Long-Term Memory Store, to Automated Voyage Embeddings in MongoDB Vector Search, to MongoDB 8.3 performance gains, these launches are designed to help teams retrieve the right context; persist memory across sessions, channels, and frameworks; and scale AI applications in production.

🎥: https://t.co/OdUIZ6UnGs