Database architecture thread. Technical. There has been several startups building an operational relational databases focused on OLTP with a shared nothing architecture. @neondatabase is using a different approach - shared storage. What's the difference?
@TejasKumar_ Sorry this happened. We're working hard to make Neon more stable under the new load coming from AI. No excuses we need to do better. DM me your org id or email and I'll refund this month.
Big news: Neon is expanding to offer a more complete set of backend primitives for running apps and agents:
✅ Database ✅ Authentication 🔜 Storage 🔜 Compute 🔜 AI Gateway
@trevorlasn@neondatabase Both. For any of the utilization graphs, agent platforms drive the big jumps in usage and devs using cc and codex drive the steadily increasing slope
The future of databases is being built directly on top of object stores. We call this the Lakebase architecture.
For a long time, the industry treated data lakes strictly as analytical or offline storage. But the Lakebase architecture is changing that by enabling true operational databases directly on top of the lake.
I believe this is the future of data infrastructure. It is how every database, whether it's an OLTP system or a vector database, should be built moving forward.
Of course, delivering the stringent performance requirements for operational databases on top of object stores require some creative engineering. Really excited to see more real-world examples of this architecture emerging. The team at Zilliz just shared a piece on why they rebuilt their vector database using this exact approach, and it perfectly captures where the industry is heading.
Check it out here: https://t.co/xZrXtFiAzi
How we build in a world where cloud limits are hit daily:
1. HA on both Compute and Storage
2. Hold control plane to same standards as data plane
3. Dependency minimalism
4. Control blast radius
5. Failure simulation
6. Measure everything
7. Build a world-class team with deep operating/scaling experience
Today, @MichaelElabd, @QuantumArjun, and I are excited to announce Trajectory.
We are a research lab and product company building the platform for Continual Learning.
Our platform unlocks the signal already sitting in product usage, so companies can continuously post-train large-scale agentic models that outperform the frontier. @trajectorylabs
We’ve raised $15M from @Conviction, @BessemerVP, @radicalvcfund, @jeffdean, @drfeifei and more.
We’re partnering with some of the best AI-native companies: @ClayRunHQ@Harvey, @DecagonAI, @mercor_ai, @RogoAI to power their agentic systems, some of which we are already in production with.
We’ve brought together a world class research team from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, Scale AI, and an elite product team from Stripe and Figma.
AI will never again start on day one. Every correction, every retry, every edit will make products smarter. This is Continual Learning.
I might be crying…
Thank you @neondatabase ❤️ it was a pleasure working with @andrelandgraf, @TaranehDohmer, and the entire team at Neon.
You guys are the best 🥹 can’t wait to do it again next year!
Today we're announcing our Series C funding: $355M at a $4.65B valuation, led by some great investors @generalcatalyst and @Redpoint.
We've had insane growth in the last year, but we're still very early. So proud of the team and what we have built so far!
Oracle has spent the last two weeks writing articles comparing Oracle (and PDB) to Lakebase, and it highlights a massive philosophical divide in how we view databases in the agentic era.
They are trying to retrofit heavy, traditional architectures for AI. We believe Lakebase are the future because agents need something entirely different:
⚡️ Super simple APIs: so agents don't have to read a giant manual and hallucinate a query.
⚡️ Sub-second provisioning & auto-scaling: so you aren't paying legacy-level prices for idle time.
⚡️ Branching: Git-style branching to create isolated, safe environments for agents on the fly.
⚡️ Automatic backup & restore: so you don't sweat it when an autonomous agent inevitably drops a table.
The numbers speak for themselves. Lakebase is our fastest growing product. In the last few months alone, we've seen database start rate 30X, and now we are starting tens of millions of databases EVERY DAY. Some of these databases have 500 level deep branches and lifetime of just seconds due to how fast agents move.
Go try it yourself in a few seconds on https://t.co/ne9Tv18JhV!
The team has been cooking hard to push this gap even further. Come to Data and AI Summit next month to hear about some major new breakthrough capabilities. 🚀
(Links next so you can read their take)
Oracle has spent the last two weeks writing articles comparing Oracle (and PDB) to Lakebase, and it highlights a massive philosophical divide in how we view databases in the agentic era.
They are trying to retrofit heavy, traditional architectures for AI. We believe Lakebase are the future because agents need something entirely different:
⚡️ Super simple APIs: so agents don't have to read a giant manual and hallucinate a query.
⚡️ Sub-second provisioning & auto-scaling: so you aren't paying legacy-level prices for idle time.
⚡️ Branching: Git-style branching to create isolated, safe environments for agents on the fly.
⚡️ Automatic backup & restore: so you don't sweat it when an autonomous agent inevitably drops a table.
The numbers speak for themselves. Lakebase is our fastest growing product. In the last few months alone, we've seen database start rate 30X, and now we are starting tens of millions of databases EVERY DAY. Some of these databases have 500 level deep branches and lifetime of just seconds due to how fast agents move.
Go try it yourself in a few seconds on https://t.co/ne9Tv18JhV!
The team has been cooking hard to push this gap even further. Come to Data and AI Summit next month to hear about some major new breakthrough capabilities. 🚀
(Links next so you can read their take)
Congrats to @SocketSecurity on the Series C. AI writes most of our code now, which means more open source dependencies flowing into production than any human can review. Socket is how we keep that from becoming a liability. The right tool at the right time.
I saddened deeply by the passing of @SSomasegar. He was an incredible human and a bright technologist.
Each of my interaction with him was stellar and I was touched by the kindness of his heart.
https://t.co/6kRU2yDcpC