Reynold Xin

Collaboration should be the default, not a paid feature. GitHub did it for coding, Neon does it for databases.

gumloop raised a $50m series b led by benchmark here's a video we had fun making about the journey back to work.

Awesome job by the @databricks team My summary: They trained a model called KARL that beats Claude 4.6 and GPT 5.2 on enterprise knowledge tasks (searching docs, cross-referencing info, answering questions over internal data), at ~33% lower cost and ~47% lower latency. The key insight: instead of throwing expensive frontier models at enterprise search, you can use reinforcement learning on synthetic data to train a smaller model that's faster, cheaper, AND better at the specific task. RL went beyond making the model more accurate. I t learned to search more efficiently (fewer wasted queries, better knowing when to stop searching and commit to an answer). They're opening this RL pipeline to Databricks customers so they can build their own custom RL-optimized agents for high-volume workloads. I think we'll continue to see data platforms become agent platforms. Databricks' KARL paper is really an agent platform play. The pitch: you already store your enterprise data in the Lakehouse, now Databricks will train a custom RL agent that searches and reasons over it, tuned specifically for your highest-volume workloads (workloads = apps = agents). The business move is closing the loop: data storage → retrieval → custom agent training → serving, all on Databricks. They're turning "your data lives here" into "your agents live here too." Kudos @alighodsi @matei_zaharia @rxin

Exactly 10 years ago (Jan 2016), I stepped in as CEO. We had just closed out Q4 with a total of $600k in revenue (screenshot from board deck). Fast forward a decade. Q4 audited GAAP Revenue: $1,290M and over $5.4B revenue run-rate ending January. I’ve never tweeted our exact quarterly financials before, but seeing that $1.29B number cross my desk on my 10-year anniversary hit differently. To the team that built this: thank you. If you're following the data space, you know exactly what this milestone means. 🏗️

Instead of configuring the Cursor agent from scratch, you can now install the Databricks plugin once and use it to build custom applications that query production data.

I now constantly get questions about the SAAS meltdown, role of AI, system of records etc. I don't have an answer to all these. But I do know that we saw an acceleration in our business in Q2, Q3, and now finished the year with accelerating Q4. The question is, why? Short answer: AI. But the underlying reason is subtle. We are growing fast because we are finally removing the biggest bottleneck in data: the technical barrier to entry. For years, if you didn’t know SQL, Python, you were locked out of the value chain. That has changed fundamentally with the 𝐆𝐞𝐧𝐢𝐞 𝐟𝐚𝐦𝐢𝐥𝐲, and it is the "secret sauce" behind our recent momentum: • 𝐆𝐞𝐧𝐢𝐞: Analysts can query data without any SQL. I use this every day myself. • 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐆𝐞𝐧𝐢𝐞: Builds end-to-end AI models for you, similar to Cursor for ML on your data. • 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐆𝐞𝐧𝐢𝐞: Write Spark pipelines, does plumbing, troubleshooting. We've been talking about DATA + AI democratization, but generative AI finally enabled it in a way that wasn't possible before. That's why we're seeing a market response. Take 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬. We launched this serverless engine for agents and apps recently. At 8 months into its journey, its revenue is already 2x what our Data Warehouse product was at the same stage. All this taken together, we ended up with the following stats for Q4: 🚀 $5.4B Revenue Run-Rate, growing >65% YoY 🚀 $1.4B AI Revenue Run-Rate 🚀 FCF Positive for the year 🚀 NRR >>140% databricks.com/company/newsro…

Today we announced Databricks Q4 results: * Surpassing $5.4B revenue run-rate, growing >65% year-over-year * Delivering positive free cash flow over the last 12 months * Crossing $1.4 billion revenue run-rate for our AI products Databricks is also completing investments in the company in excess of $7 billion, including ~$5B of equity financing at a $134 billion valuation and ~$2B of additional debt capacity. “We’re seeing overwhelming investor interest in our next chapter as we go after two new markets,” said @alighodsi, co-founder and CEO of Databricks. “With this new capital, we’ll double down on Lakebase so developers can create operational databases built for AI agents. At the same time, we’re investing in Genie to let every employee chat with their data, driving accurate and actionable insights.” databricks.com/company/newsro…

AI agents are no longer just writing code. They’re building the data layer itself. New data shows agents now create 80% of enterprise databases and 97% of dev and test environments. This is evidence of a broader shift in data infrastructure - it needs to evolve to support building intelligent apps at machine-speed iteration. @nikitabase, VP at Databricks and co-founder of @neondatabase, explains what is driving the move to a new kind of database: “The database is the system of record for AI applications. It’s no longer just a place to store rows; it’s the persistent memory and coordination layer for multi-agent systems.” More from @Forbes writer @iamVictorDey: forbes.com/sites/victorde…

Lakebase is GA! We think this is going to make it radically simpler and more reliable to work with online databases. You can instantly branch your DB, take snapshots, roll back to a point in time, or create a copy for offline analysis, whether it's humans or AI doing the work.

Operational databases have long relied on tightly coupled compute and storage. This architecture creates resource contention and pushes teams to manage infrastructure rather than build. As applications become more real time and automated, the transactional layer needs to adapt. Databricks Lakebase is built for that evolution: • Familiar Postgres semantics for app developers • Compute separated from durable state • Operational data running directly on the lakehouse • Serverless autoscaling (including scale to zero), branching, and recovery to match agent-driven workload Now generally available: databricks.com/blog/databrick…

From data lake to lakehouse to lakebase @databricks ' Lakebase operational database cuts app delivery 92% for enterprises building AI agent infrastructure "Really for the vibe coding trend to take off, you need the developers to believe they can actually create new apps very quickly, but you also need the central IT team, or DBAs to be comfortable with the tsunami of apps and database," @rxin told @VentureBeat venturebeat.com/data/databrick… via @VentureBeat

Yes!

If you’re a recently active Neon user I just sent you a note asking for feedback on what we should prioritize in 2026. So many great replies already. We are going through all of them. Your feedback is a gift. We are going to ship some epic work this year!

Yesterday we talked about how we got to $1B revenue run rate for our data warehousing business with Lakehouse. Today, we are excited to announce a whole suite of improvements for Lakebase: ⚡ Autoscaling that dynamically adjusts compute based on load 🕛 Scale to zero, allowing compute to shut down when idle and resume automatically in hundreds of milliseconds ⏫ Instant provisioning to create new database instances in seconds 🎋 Instant database branching, enabling git-like workflows with isolated, copy-on-write environments for development, testing, and staging 💾 Automated backups and point-in-time recovery for fast restore and safer operations 🆕 Postgres 17, alongside continued Postgres 16 support 📦 Increased storage capacity up to 8TB for larger production workloads 🎉 A new Lakebase UI that simplifies common workflows Many of these are made possible by the unique Lakebase architecture that separates storage (in object stores) from compute. Read more at databricks.com/blog/lakebase-… Happy holidays!

@fahdananta Yes, owning your own lakehouse is #12 on the rejuvenation program. It's right after collagen peptides and right before gene therapy. Reverses portfolio aging instantly.

We disclosed today as part of our Series L that our 4-yr old data warehousing business is now >$1B revenue run rate. This is to the best of my knowledge the fastest to $1B DW product in the industry. How did we do it, and what’s next? The conventional wisdom is that it would take 5+ years to build a new database (just to release one). Four years ago, the linked blog announced that Databricks had won the official TPC-DS 100TB benchmark with DBSQL, which was in preview back then. It had the best perf and the best price/perf, and notably beating Snowflake by 12x in price/perf in that benchmark. (Note: we are still the top place on the official TPC-DS benchmark today.) That blog post launched a contentious "benchmarking war" with a lot of back and forth between vendors, but more importantly it marked the very beginning of our data warehousing business. To build this business, we assembled the best engineering team and established a new infrastructure product category called Lakehouse that inherits the flexibility and openness of data lakes and performance of data warehouses. Lakehouse is now the standard for data infrastructure, and organizations are migrating from legacy data warehouses to the Lakehouse. The result so far is a testament to the team and their execution. We have a lot of ideas on how to take performance and usability to the next level, and the team is working hard to make that happen. Expect some big announcements next year. We want to lay the foundation for growing the data warehousing product to a $10B business. Databricks had operated largely in the “analytics” side of data in the past, and we believe the “operational” side of data (aka “OLTP”) is also ready for a “Lakehouse” style disruption. A huge chunk of the founding team’s time is now focusing on “Lakebase”, a new category of OLTP databases that separates storage (in the lake) from compute. That architecture enables features that have been virtually impossible for databases in the past: instant provisioning, elastic scaling (down to zero), branching, high throughput scan directly from Spark, … I won’t go into too much detail about Lakebase here, but we expect a similar trend to happen in the next few years: Lakebase will transform the industry and other OLTP systems will re-architect or position towards it. The best data warehouse is a lakehouse, and the best database is a lakebase! databricks.com/blog/2021/11/0…

We are excited to share our strong Q3 results: - Growing >55% year over year, surpassing $4.8 billion revenue run-rate - Exceeding $1 billion revenue run-rate for our Data Warehousing business - Exceeding $1 billion revenue run-rate for our AI products - Positive free cash flow over the last 12 months We are also raising an >$4B Series L investment, valuing the company at $134 billion. With this investment, we’ll help customers accelerate the development of Data Intelligent apps. Congratulations to our employees, customers, partners and investors! databricks.com/company/newsro…

I tried several databases before coming back to the light (Postgres), and because of that, I ended up with currently having two different databases in parallel: Postgres hosted on @neondatabase and @MongoDB with Mongo Atlas. Because of a over-eager clean-up routine that cleaned up too much, I need to restore a backup. Neon took a second to restore, and I didn't have to worry about restoring the wrong thing because it clearly said that the pre-restore latest version is also saved, and I could switch back if I need to. With @MongoDB , I'm writing this as I'm waiting for it to complete the restoration process. To be clear, my database is like kilobytes of data. The Mongo part of it is miniscule, and yet I'm still waiting. And yeah, they needed me to sign in blood that I agree that the cluster thing will be down during this whole process. @MongoDB is a liability, I need to finish my move back to good old @postgres.

Zerobus will make it super easy to push data into Databricks. This is just the beginning. We will release far more capabilities in the coming months!