Stephen Orban

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.

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: forbes.com/sites/victordey/2…

@databricks has the fastest and lowest latency on Kimi K3 (max)! Great job by Databricks AI team! artificialanalysis.ai/models…

Highly recommended if you want to understand how why Omnigent and LTAP are important!

Oh you're right! Key point being that open source GLM is over 300 tokens per second. This matters for agents (we all hate waiting 10 minutes for responses). Proprietary frontier models are at best at 100 tokens per second. So 3x speedup really matters for agentic workloads.