On Thursday, Databricks revealed a new funding round that values the business at $188 billion, led by Coatue.

While Databricks did not specify the total amount raised, it mentioned that the funds are not yet in its possession and that the round is set to close later this summer. (Other sources have reported that the amount is approximately $3 billion.) Typically, it’s not common for a company to announce funding prior to receiving the funds, but a VC told TechCrunch that the deal is robust, with numerous firms eager to participate, leading the company to have no reason to conceal its impressive new valuation.

Indeed, Databricks has been in the midst of an intensive fundraising campaign over the past year and a half, successfully rebranding itself as an AI provider rather than merely a past SaaS phenomenon. The past being the days before the BC era (Before ChatGPT).

Just five months ago, in February, Databricks finalized a $5 billion Series L round at a valuation of $134 billion. Five months prior to that, in September 2025, it secured $1 billion at a valuation of $100 billion. Approximately nine months before that, in December 2024, it achieved what was then a record-breaking round of $10 billion at a $62 billion valuation.

Databricks has raised an impressive number of rounds over the years, making this latest one the subject of memes about exhausting the alphabet. “Setting up notifications for when we receive a Series AA,” one user quipped.

However, its rebranding has been authentic. Established in 2013, the company initially prospered during the big data boom, providing software that allowed businesses to store vast quantities of data in the cloud while generating rapid analytics.

With access to significant amounts of enterprise data, Databricks was well-placed to respond as organizations began seeking AI solutions with the same security and governance they expect from conventional enterprise software.

The company started launching a series of AI products, including Lakebase, its database designed for AI agents, and Unity, its AI gateway, along with a multi-agent management tool named Omnigent.

Databricks also increasingly became recognized as a prime example of companies adopting more cost-effective Chinese-based open-weight models (models whose inner workings are publicly available for use and modification) as a strategy for cost control, a significant trend of 2026. It notably supports Z.ai’s GLM 5.2 as a coding model.

Last week, CEO Ali Ghodsi shared the findings from some internal benchmarking aimed at managing AI expenses for his team of 3,000 software engineers.

The company evaluated AI models based on the actual responsibilities performed by its programmers. Not unexpectedly, Databricks disclosed in the blog post detailing the findings that “open models, particularly GLM 5.2, can now tackle even the most challenging tasks” in coding, all at a significantly lower cost compared to proprietary models from Anthropic and OpenAI.

However, it surprised many by revealing that the selected harness—the coding tool that wraps around a model and governs its context and instructions—also significantly influenced costs. It identified the open-source harness Pi as one of the most effective tools for managing context for each prompt, making it one of the most economical options without compromising quality.

“The takeaway here isn’t that any one harness is universally cheaper or that native harnesses are inferior,” the post stated. “Rather, model selection is just one component of the overall equation.”

All of this has contributed to Databricks’ reputation as an AI company, even though it didn’t initially launch as an AI-focused lab. This has, in turn, provided it with the AI-glow necessary for attracting investments and boosting its valuation. As previously noted, the influence of AI is so pronounced nowadays that even sandwich chain Jersey Mike’s referenced AI 22 times in its S-1 filings.