Databricks Hits $188B Valuation, Reinforcing Its Position as AI’s Next Frontier

On Thursday, Databricks revealed a new funding round that positions the company at a valuation of $188 billion, with Coatue taking the lead in the investment.

Although Databricks has not disclosed the precise amount raised, it noted that the funds have yet to be completed and will finalize later this summer. (Other sources indicate the total is close to $3 billion.) It’s unusual for companies to announce funding before securing it, but a VC informed TechCrunch that the deal is strong, with many firms showing interest, allowing Databricks to flaunt its impressive valuation.

In the past 18 months, Databricks has been actively seeking funds as it has successfully transformed into an AI provider, moving beyond its origins as a SaaS success story from before the ChatGPT era.

Just five months ago, in February, Databricks wrapped up a $5 billion Series L round at a $134 billion valuation. Prior to that, in September 2025, it raised $1 billion at a $100 billion valuation. Around nine months earlier, in December 2024, it set a record with a $10 billion round at a valuation of $62 billion.

Databricks has pursued various funding rounds throughout its history, prompting jokes about running out of letters in the alphabet. “Waiting for our Series AA,” quipped one user.

Nonetheless, its transformation is authentic. Established in 2013, Databricks initially flourished during the big data era, offering software that enabled enterprises to store extensive data in the cloud while facilitating rapid analytics.

With a wealth of enterprise data already at its disposal, Databricks was ideally positioned to meet the increasing demand for AI solutions that provide the same level of security and governance as traditional software.

The company has since introduced several AI products, such as Lakebase, a database tailored for AI agents, and Unity, its AI gateway, along with a “meta-harness” known as Omnigent for managing multiple agents.

Databricks has also earned recognition for its advocacy of affordable, Chinese-based open-weight models (which allow users to access and modify the underlying code) to help manage costs, reflecting a significant trend in 2026. It particularly backs Z.ai’s GLM 5.2 model for coding tasks.

Last week, CEO Ali Ghodsi shared insights from internal benchmarking designed to control AI expenses for his team of 3,000 software engineers.

The company assessed AI models based on the actual tasks executed by its programmers. Unsurprisingly, the blog post detailing these findings pointed out that “open models, especially GLM 5.2, can handle even the most complex coding tasks at a lower total cost than proprietary models from Anthropic and OpenAI.”

However, it was unexpected to discover that the choice of harness—the coding tool that interacts with a model to manage its context and instructions—also significantly impacted costs. They identified the open-source harness Pi as one of the most effective options for managing context around each prompt, resulting in one of the most economical choices without sacrificing quality.

“The takeaway is not that one harness is always cheaper or that native harnesses are subpar,” the post noted. “Rather, model selection is just one aspect of the overall equation.”

All these advancements have enhanced Databricks’ reputation as an AI company, even though it was not originally established as an AI lab. This transformation has created an “AI halo” effect, aiding in its fundraising endeavors and valuation growth. As mentioned before, the impact of AI has become so notable that even sandwich shop Jersey Mike’s referenced AI 22 times in its S-1 documents.