AI
Databricks reaches $188B valuation as it transforms from data platform into AI powerhouse
The enterprise data company has tripled its valuation in less than a year by pivoting to AI agents and open-source models, raising fresh capital at a pace that's exhausted the alphabet.
TechCrunch — AI
Databricks announced a new funding round Thursday that values the company at $188 billion, according to a press release from the company. Coatue led the investment, though the exact amount remains undisclosed—reports suggest roughly $3 billion—with the deal expected to close later in summer.
The announcement came before capital actually landed, an unusual move that signals investor enthusiasm. Insiders told TechCrunch the deal is secure enough that multiple firms were eager to participate, giving Databricks confidence to unveil the valuation early.
From data infrastructure to AI imperative#
Databricks has experienced a remarkable rebranding over the past eighteen months. Once primarily known for enterprise data storage and analytics in the cloud-computing era, the company has successfully repositioned itself as an essential AI platform as corporations race to deploy machine learning systems.
The company's valuation jump tells the story: from $62 billion in December 2024, to $100 billion by September 2025, then $134 billion in February 2026, and now $188 billion just five months later. The pace of rounds has spawned internet memes about running out of series letters—one post joked about announcing a "Series AA" next.
Building an AI agent ecosystem#
Databricks began rolling out AI-focused products to capitalize on existing relationships with enterprises sitting on vast troves of proprietary data. The company introduced Lakebase, a database purpose-built for AI agents; Unity, an AI gateway; and Omnigent, a system for orchestrating multiple agents across tasks.
Equally important, Databricks positioned itself as a champion of cost-conscious enterprise AI adoption. The company became known for advocating open-weight models—whose underlying code is publicly available—over expensive proprietary options. It particularly promoted GLM 5.2 from Chinese AI maker Z.ai as a competitive alternative for coding tasks.
Open models prove cost-competitive#
Last week, CEO Ali Ghodsi shared internal benchmarking results comparing how different AI models and agentic tools performed on real coding work across the company's 3,000 engineers. The findings revealed that open-source models, particularly GLM 5.2, could match the performance of proprietary systems from Anthropic and OpenAI while reducing total costs.
