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

Databricks did not reveal exactly how much it raised; He said the money is not yet in his hands and the round will close later this summer. (Other outlets have since reported the increase to be approximately $3 billion.) While it’s unusual for a company to announce before receiving the money, one VC tells TechCrunch that the deal is solid, with so many companies wanting it that the company had no reason to keep its shiny new valuation a secret.

In fact, Databricks has been in a year and a half of fundraising as it successfully transitioned its image into an AI provider and not just a SaaS sensation of yesteryear. Back in the days of BC (before ChatGPT).

Just five months ago, in February, Databricks closed a $5 billion Series L raise at a $134 billion valuation. Five months before that, in September 2025, it raised $1 billion at a valuation of $100 billion. And about nine months before that, in December 2024, it raised what was at the time a record-breaking $10 billion round at a $62 billion valuation.

Databricks has generated so many rounds over the years that this latest one became the subject of memes about running out of letters of the alphabet. “Activating alerts for when we have a Series AA,” one person posted.

But his image reconstruction has been legitimate. Founded in 2013, it initially found success in the era of big data, with software that allowed companies to store huge amounts of data in the cloud while producing rapid analysis.

Because it already had a wealth of enterprise data, Databricks was then well positioned to respond when enterprises began to want AI with the same security and governance they expect from traditional enterprise software.

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

Databricks has also become increasingly known as one of the great examples of companies adopting more affordable China-based open weight models (models whose underlying code is published so anyone can use and modify it) for cost control, one of the big trends of 2026. He is a particular proponent of Z.ai’s GLM 5.2 as a model for coding.

Last week, Databricks CEO Ali Ghodsi shared the results of some internal benchmarking conducted to manage its own AI costs for its 3,000 software engineers.

The company compared AI models with the real tasks its programmers perform. Not surprisingly, in the blog post revealing the results, Databricks shared that “open models, and GLM 5.2 in particular, are now capable of handling even the highest level of task difficulty” in coding, and at a lower overall cost than Anthropic and OpenAI’s proprietary models.

But it did surprise people to find that the choice of harness (the agent coding tool, like Codex or Claude Code, that wraps a model and manages its context and instructions) equally impacted costs. It found that using open source Pi is one of the best at managing the context surrounding each message and therefore one of the lowest cost options without sacrificing quality.

“The lesson here is not that a harness is always cheaper or that native harnesses are worse,” the post states. “Instead, model choice is just one piece of the puzzle.”

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