Databricks eyes $175B valuation, four months after raising at $134B
What's the deal? Databricks is in talks to raise a new funding round at a valuation of $165B to $175B, according to The Information. The round could begin as soon as July 2026 — just four months after the data platform closed a roughly $5B raise (including $2B in debt) at a $134B valuation in February 2026.
CEO Ali GhodsiDealroom has a profile for this one. Try Dealroom → has privately signalled to investors that Databricks could pursue an IPO as soon as 2027. A pre-IPO round at the higher valuation would set the floor for a public listing.
Why now? Databricks said in February 2026 it had surpassed $5.4B in annualised revenue run rate, up 65% year on year. At $175B, the company would trade at roughly 32 times that run rate — expensive by traditional software standards, but in line with multiples for private AI infrastructure companies.
Raising at a 30% markup so soon after a $5B round is not about needing cash. It is a valuation exercise designed to establish the highest possible private-market benchmark before going public.
The timing also places Databricks in a crowded IPO queue. OpenAI filed confidentially for an IPO in early June 2026, AnthropicDealroom has a profile for this one. Try Dealroom → filed days earlier, and SpaceX listed the same week. If Databricks goes public in 2027, it would enter a market already absorbing the largest AI listings in history.
What could go wrong? Discussions are early-stage and the round has not closed, meaning the valuation range could shift. The core risk is that private-market enthusiasm does not translate to public-market pricing. Databricks has not commented on the report.
The signal: The pattern of rapid-fire raises at escalating valuations has become standard among AI infrastructure companies racing toward public listings. Databricks sells a data lakehouse platform that combines data warehousing and data lake capabilities — the plumbing enterprises need to store, process, and analyse the massive datasets AI models require.
That positioning, at the infrastructure layer rather than the model layer, may prove more durable. But a 32x revenue multiple still demands that growth holds. The IPO market will be the real test.
Read more: The Next Web
