Databricks hits $188B valuation, extends its run as second-favorite AI venture

Databricks on Thursday announced a new funding round that values ​​the company at $188 billion. The round was led by Coatue.

Databricks did not disclose how much it raised; said that the money is not yet in its hands and the round will close later this summer. (Some outlets have since reported the raise to be as much as $3 billion.) While it’s rare for a company to announce before it gets the money, the VC tells TechCrunch that the deal is so tight, with so many companies vying that the company had no reason to keep its shiny new valuation a secret.

In fact, Databricks has been struggling for a year and a half as it has successfully transformed its image into an AI provider and not just a classic SaaS experience. Yesterday you returned to BC times (Before ChatGPT).

Only five months ago, in February, Databricks closed a $5 billion Series L raise at a value of $134 billion. Five months before that, in September 2025, it raised $1B for an estimated $100 billion. And about nine months before that, in December 2024, it raised a then-record-breaking $10 billion at a valuation of $62 billion.

Databricks has raised so many rounds over the years that the latest one became the subject of memes about running out of characters. “Turns on alerts when we get series AA,” one person posted.

But the reconstruction of its image was legitimate. Founded in 2013, it first grew to success back in the big data era, with software that enabled businesses to store large amounts of data in the cloud, yet generate instant analytics.

Because it was already sitting on top of enterprise data, Databricks was well-positioned to respond as companies began to demand AI with the same security and governance they expect from traditional enterprise software.

The company began releasing one AI product after another, such as Lakebase, its database built for AI agents, and Unity, its AI gateway, and a “meta-harness” called Omnigent that manages multiple agents.

Databricks is also increasingly recognized as one of the biggest examples of enterprises adopting low-cost Chinese-based open weight models (their underlying code models are published for anyone to use and modify) to control costs, which is one of the biggest trends of 2026. It is a particular champion of Z.ai’s GLM 5.2 as a coding model.

Last week Databricks CEO Ali Ghodsi shared the results of an internal assessment conducted to manage his AI costs to his 3,000 software engineers.

The company compared the AI ​​models to actual tasks performed by its programmers. Not surprisingly, in a blog post announcing the results, Databricks shared that “open models, and GLM 5.2 in particular, are now able to handle even higher levels of workload” in coding, and at a lower overall cost than proprietary models from Anthropic and OpenAI.

But it surprised people to find that choosing a harness – an agent code tool, like Codex or Claude Code, that integrates the model and controls its context and commands – had the same effect as the cost. It found the open source harness Pi to be one of the best in managing the context surrounding each information, and therefore one of the lowest cost options without sacrificing quality.

“The lesson here is not that a single harness is always cheap or that traditional harnesses are worse,” the document said. “Instead, choosing a model is only one part of the puzzle.”

All this added to the image of Databricks as an AI company, even if it was not founded as an AI lab. This, in turn, gave it an AI-halo to raise capital and jump in its valuation. As we’ve previously reported, the effect of AI is so strong these days, that even the sandwich shop Jersey Mike’s mentioned AI 22 times in its S-1 filings.