Ali Ghodsi
@alighodsi
Databricks CEO & Co-founder, UC Berkeley Faculty
277 Following 218.4K Followers
I don't think we should fight. The two companies are complementary. Since you're not doing this AI stuff, I really think Snowflake should consider getting its AI tokens through Databricks. You can get both cheap OSS models as well as frontier (OAI/Ant/Gemini/Grok) models from our platform. It all goes through Unity Gateway which has Smart Routing, Budgets, and Access Control. You can even use Omnigent with any harness, including CoCo. Most of our customers used this to reduce token spend ~30% last few months while token count went up. Genie lets you then track, control, manage that spend, all your financials, marketing, etc. Quick POC?
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We are super exciting about the automation of campaigns in marketing! Check out @tasso's talk at Braze!
Not every marketer is familiar with Databricks today, but that's about to change!
Marketing agents simply do not work without real-time customer+business context. Databricks' CustomerLake is quickly becoming the cornerstone of the marketing stack of the future, providing just that.
It was great discussing all of that on stage at Braze's Forge conference. Thank you for having me, @jon_hyman!
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Everyone has been obsessed by Jev and what it can do, and they should be! Fast typed decisions >> slow text generation. It's a complete game changer. But how do you do that with all the data you have?
We just dropped 𝚊𝚒_𝚍𝚎𝚌𝚒𝚍𝚎(), which runs decision models natively on all your data in @databricks . Now you can run system one decisions on all your big data, see screenshot.
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We just tested the latest AI models on 2,400 engineers. The cost/quality frontier just shifted massively.
The TL;DR:
• Opus 5.5: Our new daily default - higher quality and 20% cheaper than prior baselines.
• GPT-6 Luna: Shockingly capable and 20x cheaper than Opus 5.5 for high-volume routing.
Must-read for anyone managing production AI workloads 👇
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All seven Databricks co-founders began our journey together at UC Berkeley. We started the company in a room in Soda Hall, moved to a small office on Addison Street, and spent our first few years in Berkeley before eventually heading to SF. Two of our co-founders are still on the faculty.
Today we announced Databricks Field at California Memorial Stadium, our first collegiate athletics sponsorship.
Berkeley shaped everything about how this company thinks, and this is our way of investing in the next generation of students and builders who'll do their best work on that campus.
Go Bears!
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At the beginning of this year, big portions of @databricks started running on Genie. When finance started doing all their work with Genie, we noticed that they were combining Genie with a Live Cloud Spreadsheet called Row Zero. This combination is really powerful. We met the Row Zero team and were blown away. Today, we're excited to announce that we've agreed to acquire Row Zero. Stay tuned for an awesome experience of Genie + Row Zero:
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This was a fun and very different interview with @bhalligan!
This will be great for the industry and open source. Congrats @ClementDelangue and @JensenHuang!
Exciting day for NVIDIA and @huggingface.
Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university, industry and country to build with, customize and benefit from AI.
Thank you @ClementDelangue for coming to me.
NVIDIA is going to be a great home for Hugging Face, its community and the future of open models. 🤗
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To avoid this scenario where agents wipe everything out permanently, just branch your database, it's super easy to do on Neon Lakebase:
𝚗𝚎𝚘𝚗𝚌𝚝𝚕 𝚋𝚛𝚊𝚗𝚌𝚑𝚎𝚜 𝚌𝚛𝚎𝚊𝚝𝚎 --𝚗𝚊𝚖𝚎 𝚗𝚎𝚠𝚋𝚛𝚊𝚗𝚌𝚑
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Bad news: Fable nuked my entire dev machine
Claude decided to test a sandbox it was building by running `rm -rf` on my home directory
The sandbox didn't work.
It's all gone
This is true. It wasn't actually possible before 2010 because datacenter networks would bottleneck. We used to design coupled storage/compute where you brought compute close to "big data". But research on "full bisection bandwidth" networks made it possible to essentially just talk from any machine to the storage system at full speed. The disaggregation started then! Databricks and Snowflake started soon after many others followed. Now "Put it on the object store" is the way to go.
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"Put it on the object store" remains undefeated.
- OLAP: Lakehouse
- OLTP: Lakebase
- Kafka: Warpstream
- Vector Search: Turbopuffer
- Git: Cursor Origin
@willcb Never felt so bad about our growth as right now!!
An extremely important functionality for agents is to simply extract fields out of PDFs. This turns out to be harder than people think because LLMs are primarily trained on predicting the next tokens. This leads them to "autocorrect" things that they shouldn't autocorrect. We launched an AI Extract capability that just excels at doing just this task with very high accuracy (95% vs 87% for others) and extremely low cost. Check out this blog on how we did it. The function can of course be called directly from SQL and be used throughout the platform.
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This is how Smart Routing working on our AI Gateway, super simple idea, lowers cost about 30% without giving up on quality!
Introducing Smart Routing in Unity AI Gateway.
Stop overpaying for AI. Smart Routing matches each coding task to the right model and harness based on what the task needs, so higher-cost models can differentiate on intelligence while lower-cost models differentiate on cost and performance.
Match frontier quality and cut task costs by 30%+.
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I got this question so many times today. "How can you grow 80% at $7B?"
The true answer is that we're finally seeing a breakthrough with AI agents starting to work in the enterprise.
The AIs have been super smart for a while, but have lacked basic context that's in people's heads, or in some SaaS system-or-record. A lot of organizations are deploying FDEs to capture this context, or Ontology, and feed it to the AI. This is labor intensive and expensive. We just automated that with Genie Ontology.
Once you have that enterprise context graph, an AI agent like Genie becomes magical. I find myself no longer waiting for answers from my CRO, CFO, CMO, CHRO etc, I just keep queuing up questions on the phone while sitting in meetings. It'd frankly addictive.
Our customers are starting to do the same, over 70% of all queries on the platform are now generated by Genie agents. This fuels more questions to the platform, which drives consumption, which drives revenue. That's the simple answer.
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Today, we announced that we crossed $7B in revenue run-rate, growing over 80% year over year in Q2.
We also shared:
🚀 $100M+ revenue run-rate for Lakebase
🚀 $1.5B+ revenue run-rate for Lakehouse, growing over 100% year over year
🚀 Continued positive adjusted free cash flow
And we raised $5B in our latest fundraise.
We’ll use this capital to invest in:
1️⃣ Lakebase, our serverless Postgres database built for AI agents
2️⃣ Genie, our AI coworkers that actually understand your business data
3️⃣ Unity AI Gateway, our multi-AI governance solution that helps control costs
@iamVictorDey shares more in @Forbes:
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Excited to share that we've acquired ElectricSQL, the team behind PGlite. Agents need super fast Postgres and this team built an amazing WASM (WebAssembly) implementation of postgres that runs in your browser, but can sync back with Postgres instances asynchronously. Exactly what blazing fast AI agents today need. Excited to supercharge our 𝐋𝐚𝐤𝐞𝐛𝐚𝐬𝐞 𝐏𝐨𝐬𝐭𝐠𝐫𝐞𝐬 offering with these capabilities.
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My cofounder Patrick talked to top tech companies about how they control their AI spend and summarized his findings. Highly recommended reading.
Today @databricks we're publishing a detailed analysis of techniques we used to drastically reduce our internal AI spend while aggressively growing adoption. Savings come from layering in several techniques, which combine to drive unit costs down as much as 90% in some scenarios. Tl;dr, the wins come from:
1. Shifting defaults to more efficient models, including OSS models such as GLM. Maximum intelligence models simply aren't needed for many coding tasks, and "good enough" models are quickly becoming very cheap. We shift traffic between models using Unity AI Gateway. Approximate savings: 50% or more.
2. Using smart routing to automate model selection. Routing can further squeeze efficiency by dynamically selecting the model or harness that can most efficiently execute a particular task. Our task-level routing leverages @omnigent_ai. Approximate savings: 30%.
3. Providing user visibility and adaptive budgeting. Every user can see how much they spend, and users receive hints on how to contain spend. Heavy spenders encounter progressive friction as they ratchet spend above certain levels. Approximate savings: 10%.
4. Managing context bloat by pruning tool call results and tuning harness settings. Extraneous context costs $$ and delivers no value. Tuning cache settings also help lower average token costs. Approximate savings: 10%.
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This makes a lot of sense. Democratize AI!
I wrote about why we believe the future is for everyone. More coming about a positive vision for a world with superintelligence soon.
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community.
During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion.
That’s why we created the Open Secure AI Alliance.
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This is one of those unintuitive things. Agents that cook longer are often worse. Genie just gets to the results faster. Ontology will be key to getting these agents the context they need to get the answers right quickly.
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We found that improving data agent quality also improves efficiency. Sometimes more is less: agents that take long, exploratory random walks are often less likely to arrive at the right answer.
We put Genie Code head-to-head against three leading general-purpose coding agents on 400+ real user data tasks.
Result below 👇
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