Databricks is in talks to raise fresh capital at a valuation north of $165 billion, according to a report from The Information picked up by Reuters. The figure, if confirmed at close, would vault the data and AI platform into the top tier of privately held technology companies and extend a valuation climb that has tracked the broader surge in enterprise AI spending.
At $165 billion, Databricks would sit alongside the handful of AI-native firms — OpenAI, Anthropic, xAI — that have crossed the nine-figure private valuation line over the past 18 months. The new mark would represent a steep step up from the company's prior fundraises, and it lands at a moment when late-stage private capital has tightened for most categories outside frontier AI.
Databricks has not publicly confirmed the talks, and terms including the round size, lead investor, and structure have not been reported. The Information's reporting frames the discussions as ongoing rather than closed, meaning the final valuation could move before any announcement.
Key facts
- 01Databricks is in talks to raise new funding at a valuation above $165 billion, according to The Information.
- 02The figure would mark one of the highest private valuations in enterprise software, rivaling top AI-native peers.
- 03The round signals continued investor appetite for AI infrastructure plays despite tightening late-stage capital.
The company sells a unified data and AI platform built on its lakehouse architecture, used by enterprises to train, deploy, and serve machine learning and generative AI models against their own data. That positioning — picks and shovels for enterprise AI rather than a consumer-facing chatbot — has become one of the most defensible business models in the current cycle, and it underpins the investor appetite reflected in the $165 billion figure.
Databricks' core competitor, Snowflake, trades on public markets and has spent the past two years racing to add AI and machine learning workloads to a platform originally built for analytical SQL. The widening valuation gap between the two reflects investor conviction that the data substrate for enterprise AI — vector search, model training pipelines, governed access to proprietary corpora — is where the durable spending lands.
The company has also been an aggressive acquirer. Its $1.3 billion purchase of MosaicML in 2023 brought in-house model training capabilities, and subsequent deals have layered in serving infrastructure and developer tools. A round at $165 billion would give Databricks the balance-sheet flexibility to keep buying.
Investor enthusiasm for AI infrastructure has held up even as scrutiny of returns on AI capital expenditure intensifies. Hyperscalers have collectively committed hundreds of billions of dollars in data center buildouts, and the platforms that sit between raw compute and end-user applications — Databricks among them — are the conduits through which those workloads monetize.
The talks come against a backdrop of accelerating IPO chatter across the AI cohort. Perplexity has said it is targeting a 2028 listing, and OpenAI has reportedly begun preliminary IPO filings. A Databricks listing has been speculated about for years; an oversized private round would let the company defer that decision while still letting early employees and investors take partial liquidity.
The risks to a round at this mark are the obvious ones. Private valuations in AI have repriced sharply in both directions over the past two years, and a $165 billion entry point demands a growth trajectory that justifies it through any near-term tightening in enterprise IT budgets. Late-stage investors writing checks at this level are betting that Databricks' revenue growth — driven by AI workloads on top of its existing data platform business — sustains through the next two to three years without a step-down.
A close at over $165 billion would reset the comparable set for every other AI infrastructure company still in the private market, and it would sharpen the question of who the natural public-market acquirer or comparable is when Databricks eventually lists. The deal, if it lands at the reported mark, is less a signal about Databricks specifically and more a signal that the capital available for the enterprise AI stack has not yet found its ceiling.
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