Databricks jumps to $188B valuation in fresh funding round

Databricks has reached a $188 billion valuation in a new funding round led by Coatue, extending a rapid fundraising run that has recast the company as an AI provider. The company did not disclose the size of the round, and said the deal has not closed yet; it is expected to close later this summer. Other outlets have reported that the raise is roughly $3 billion.

Announcing a financing before receiving the money is unusual. But a venture capital source told TechCrunch the deal is solid, with enough firms seeking a stake that Databricks had little reason to keep its new valuation private.

Databricks' rapid valuation climb

The latest round comes just five months after Databricks closed a $5 billion Series L at a $134 billion valuation in February. The company’s recent financing history includes:

• September 2025: $1 billion raised at a $100 billion valuation
• December 2024: $10 billion raised at a $62 billion valuation
• February 2026: $5 billion raised at a $134 billion valuation

Databricks has raised enough rounds over the years to inspire jokes about exhausting the alphabet. “Turning on alerts for when we get a Series AA,” one person posted.

The company was founded in 2013 and initially built its business during the big data era. Its software helped enterprises store vast amounts of information in the cloud while running fast analytics on that data.

Recommended reading

Australian founders get 48 hours to enter Startup Battlefield

That installed base of enterprise data positioned Databricks for the shift toward corporate AI systems that offer the security and governance companies expect from conventional enterprise software. The company has since introduced products including Lakebase, a database built for AI agents; Unity, an AI gateway; and Omnigent, a “meta-harness” designed to manage multiple agents.

Databricks pushes lower-cost AI models

Databricks has also become a prominent example of an enterprise adopting more affordable Chinese-based open-weight models, whose underlying code is published for anyone to use and modify. The trend has grown in 2026, as companies look to control AI costs.

The company has particularly championed Z.ai’s GLM 5.2 for coding. Last week, CEO Ali Ghodsi shared internal benchmarking covering the real-world tasks performed by Databricks' 3,000 software engineers.

The tests found that “open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty” in coding, while costing less overall than proprietary models from Anthropic and OpenAI.

The benchmarking also found that the choice of harness—the agentic coding tool that wraps around a model and manages its context and instructions—had an equally significant effect on cost. Databricks identified the open-source Pi harness as one of the strongest at managing the context around each prompt, making it one of the lowest-cost options without sacrificing quality.

“The lesson here isn’t that one harness is always cheaper or that native harnesses are worse. Instead, model choice is only one piece of the puzzle,” the company wrote.

That product strategy has strengthened Databricks' image as an AI company, despite its origins outside AI research. It has also helped the company benefit from the valuation premium currently attached to AI businesses—a trend strong enough that sandwich chain Jersey Mike’s mentioned AI 22 times in its S-1 documents.