It's a big question in enterprise tech right now: When will Databricks go public? The answer will have to wait.
The startup just raised another huge round of private capital ahead of its IPO, which could happen this year. The new $1.6 billion Series H round values Databricks at a jaw-dropping $38 billion. New investors include BNY Mellon, ClearBridge and the University of California's investment fund.
Money is no object in Silicon Valley these days, but to put that in perspective: There's only a handful of private startups, including TikTok owner ByteDance, that are valued higher. It's even more wild when considering that the products Databricks sells are deeply technical and targeted towards skilled developers. In other words: This isn't teenagers dancing to snippets of Olivia Rodrigo's album, it's heavy-duty artificial intelligence.
The latest fundraising round won't impact its IPO timeline, per CEO Ali Ghodsi. In fact, he believes Databricks is already well on the path to becoming a public company.
"We're going public six months at a time," Ghodsi told Protocol. "Usually when you IPO, you want to make sure you are getting the long-term investors … [and] we're basically allocating the big blocks of allocations to the big mutual funds and other investors right now," he added.
The investors in question are Franklin Templeton, which participated in February's $1 billion round, and Morgan Stanley, which was involved in the latest one. Another key audience that is likely watching the IPO timeline with great interest? Databricks employees.
When you look at the last few funding rounds, "it's not a lot of dilution, single digit percentages," Ghodsi said. "The company is getting more diluted by the people we hire every year … [and] that dilutes the company more than fundraising."
Databricks has offered "multiple liquidity events for our employees," per Ghodsi.
And a key reason Databricks needs to keep the continual flow of capital is to establish the data lakehouse — an architecture it created that blends together the data warehouse and the data lake — as a permanent category, as well as fend off competition from upstarts and the cloud giants. That means hiring pricey engineers and pouring money into research and development, among other costly undertakings.
"Building a whole data and AI stack, creating a new category, it's going to take a lot of investment," said Ghodsi. "We love the cloud vendors ... but there is also overlap with them. There is Snowflake. If you look at the market, all of those are massive companies with massive balance sheets."