Databricks is operating at a scale most companies reach only after going public. With a $4.8 billion annual revenue run rate, positive cash flow, and a valuation of $134 billion, the data platform is challenging long-held assumptions about when—or whether—high-growth tech companies need an IPO.
The data platform recently announced raising more than $4 billion in a Series L round, a scale of late-stage funding that remains rare, as demand for AI and data platforms continues to grow.
Commenting on the company’s prolonged stay in the private markets, Vasuman M, founder of AI engineering firm Varick Agents, joked on X, “Imagine joining Databricks in 2017 at its Series D, thinking the IPO was just around the corner, and then nine years later the company is raising a Series L.”
Only SpaceX, OpenAI, Anthropic, and xAI are higher-valued private US companies, and three of them are looking to go public.
Databricks’ funding comes after the company reported more than 55% year-over-year growth in the revenue run-rate in Q3, projecting over $1 billion in revenue from AI products and a similar amount from its data warehousing business.
The company said it has remained cash-flow positive over the past 12 months, a rarity among high-growth tech firms in the AI space, investing heavily in data centres.
Interestingly, since its founding, the company has raised about $19 billion in private funding to date, according to Tracxn, far exceeding its rival Snowflake’s $1.4 billion in capital raised before its 2020 IPO. That puts Databricks’ private fundraising at roughly 13 times higher than Snowflake’s, even as the two companies have a similar revenue run rate.
Snowflake is currently valued at about $76 billion, significantly below Databricks.
Why Not IPO?
In a recent interview with CNBC, Databricks CEO Ali Ghodsi said that remaining private has given the data company the flexibility to keep investing through market cycles without the pressure of short-term expectations.
“If we had gone public earlier…I don’t think we would have been at this growth rate right now,” he said, adding that staying private allowed it to invest in areas such as AI agents and its database business, including Lakebase.
Over the past few years, Databricks has made several large acquisitions—spending about $1.3 billion on GenAI startup MosaicML, more than $1 billion on data management platform Tabular, and roughly $1 billion on Neon—as it builds out its AI and data platform capabilities.
Ghodsi said Databricks has been free-cash-flow positive but is intentionally reinvesting rather than optimising for margins. “We’re an efficient business, but we’re reinvesting it. We just want to stay at break-even and invest it back in agents and databases,” Ghodsi said. He further added that the company is betting on what it calls a new category of “data intelligent applications.”
On a potential IPO, Ghodsi said going public remains an option, possibly as early as next year, but cautioned against listing in a market that could limit long-term innovation. “I don’t want to take the company public and then have the market demand 30% EBITDA,” he said.
Addressing concerns about an AI bubble, Ghodsi said AI adoption is structural rather than cyclical. “People are not going to stop using AI,” he assertedsaid, pointing to growing automation in software development and enterprise workflows that will continue to drive demand for data platforms built to work with AI agents.
What Makes Databricks Special?
Databricks’ co-founder and chief architect, Reynold Xin, in a post on X, said that the new funding challenges the conventional belief that “it would take 5+ years to build a new database, just to release one.”
He traced the origins of the data warehousing business to four years ago, when Databricks’ DBSQL, then still in preview, topped the official TPC-DS 100TB benchmark, outperforming a previous best by 2.2x, including a 12x advantage over Snowflake at the time.
Databricks, Xin added, continues to hold the top spot on the benchmark.
According to Xin, Databricks built the business by assembling a dedicated engineering team and introducing the Lakehouse architecture, which the company said combines the openness and flexibility of data lakes with the performance of traditional data warehouses. He said the Lakehouse model has since become “the standard for data infrastructure,” with enterprises increasingly migrating away from legacy warehouses.
Xin also said Databricks is expanding beyond analytics into operational workloads, arguing that online transaction processing (OLTP) systems, managing day-to-day business operations, are ready for a similar disruption. He said a significant portion of the founding team is now working on Lakebase, a new OLTP database category that separates storage in the data lake from compute.
As the year draws to a close, the company announced a set of product updates to Lakebase, including autoscaling that adjusts compute based on workload, scale-to-zero support with automatic resume in milliseconds, and instant provisioning that allows new database instances to be created in seconds.
The release also introduces instant database branching for git-like development, testing, and staging workflows, along with automated backups and point-in-time recovery.
Additional updates include support for open-source relational database Postgres 17 alongside Postgres 16, expanded storage capacity up to 8TB for production workloads, and a new Lakebase user interface designed to simplify common operational tasks.
The next phase of growth will test whether Databricks can convert ambitious product bets into broad enterprise data adoption.
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