Databricks announced on Thursday that a new funding round values the company at $188 billion. The round was led by Coatue. The company did not disclose the exact amount raised, and said the funds are not yet in hand; the round is expected to close later this summer. Other outlets have since reported the raise at roughly $3 billion.
Why the announcement matters
It is unusual for a company to publicize a valuation before the money is received, but a venture capital source told TechCrunch the deal is solid and that many firms wanted to participate, giving Databricks little reason to keep the valuation private.
A rapid fundraising streak alongside an AI repositioning
This financing is the latest in a series of large rounds over the past year and a half as Databricks repositioned itself from a big-data SaaS success into an AI provider. In February 2026 Databricks closed a $5 billion Series L at a $134 billion valuation. Five months earlier, in September 2025, it raised $1 billion at a $100 billion valuation. Roughly nine months before that, in December 2024, it closed a then-record $10 billion round at a $62 billion valuation.
The frequency of rounds has even spawned online jokes about running out of series letters — for example, people quipping about getting alerts for a hypothetical "Series AA."
How Databricks became an AI company
Founded in 2013, Databricks originally made its name in the big data era with software that allowed enterprises to store vast amounts of data in the cloud while delivering fast analytics. Because it already held large troves of enterprise data, Databricks was well positioned when companies began demanding AI with the same security and governance expected from traditional enterprise software.
The company has rolled out multiple AI products, including Lakebase, a database built for AI agents; Unity, an AI gateway; and Omnigent, a so-called "meta-harness" that manages multiple agents.
Model choices, harnesses and cost benchmarking
Last week Databricks CEO Ali Ghodsi published internal benchmarking results conducted to manage AI costs for the company’s roughly 3,000 software engineers. The company compared models on the actual coding tasks its engineers perform.
In the blog post, Databricks reported that "open models, and GLM 5.2 in particular, are now able to handle even the highest level of task difficulty" for coding, and that those open models delivered lower total costs compared with proprietary models from Anthropic and OpenAI. The company also found that the choice of harness — the agentic coding tool such as Codex or Claude Code that wraps a model and manages context and instructions — materially affects costs.
Surprisingly to some, the open-source harness Pi performed among the best at managing prompt context, producing one of the lowest-cost options without sacrificing quality. Databricks emphasized that the lesson is not that one harness is always cheaper or that native harnesses are inherently worse; model choice is only one piece of the puzzle.
Why this is significant
The product rollout and cost-focused benchmarking have reinforced Databricks’ reputation as an AI company, even though it was not founded as an AI lab. That AI reputation has helped the company raise capital and surge in valuation. The article notes how powerful the AI effect can be in markets today — a colorful example being that Jersey Mike’s mentioned AI 22 times in its S-1 filings.
Next steps
Databricks says the round will close later this summer and has not officially disclosed the final amount raised. How the company uses any new capital for product development and market expansion will be a key story to watch in the coming months.
