Databricks closed a $5 billion round at a $190 billion valuation, betting that enterprise AI agents will need a new infrastructure layer to control token costs and unlock organizational context.
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Databricks raises $5 billion at $190 billion valuation for AI agents
Databricks closed a $5 billion round at a $190 billion valuation, betting that enterprise AI agents will need a new infrastructure layer to control token costs and unlock organizational context. The company said it surpassed a $7 billion annualized revenue run-rate, growing more than 80 percent year over year in the second quarter.
"ARR growing at 50%+ and gross margin stabilizing at 70%+ over the next few years should justify the valuation," Owen Lau, equity analyst at Clear Street, said. "The ROI debate at the application layer is still not settled. If the enterprises can't monetize these AI tools or increase productivity, they will likely cut back these data and AI investments."
Coatue led the round alongside Blackstone, MGX and T. Rowe Price, with Sixth Street Growth joining as a new investor. The financing came six months after Databricks raised $5 billion at a $134 billion valuation, and the final $190 billion figure topped the $188 billion term sheet announced in July as the company issued additional shares.
The capital will fund three areas Databricks sees as essential to putting AI to work inside enterprises: Unity AI Gateway, which routes workloads across models and controls spending; Lakebase, its serverless Postgres database for agent-built software that has crossed a $100 million revenue run-rate; and Genie, which gives AI access to the context buried across an enterprise. The raise also lets Databricks delay an IPO, with cofounder and CEO Ali Ghodsi saying it is "very unlikely" the company lists before Anthropic or OpenAI.
Token Costs Turn Model Routing Into a Business
Ghodsi argues that artificial general intelligence has already arrived by the definition the industry used before 2022 — a system smarter than most people most of the time — and that the real bottleneck is no longer model capability but enterprise context and the rising cost of running AI agents. Companies are deploying coding agents across their organizations while still relying on powerful models for routine tasks, driving inference costs beyond the value of the work performed.
That is the opening for Unity AI Gateway, which lets companies route all their tokens through one system, set budgets for groups or individuals, and switch between proprietary and open models as cheaper or better options appear. Ghodsi said more than a quadrillion tokens have passed through the gateway, and that some customers send the same important question to two models and pay twice to compare answers. Databricks has open-sourced the gateway through MLflow and extended the same philosophy upward through Omnigent, its open-source meta-harness that sits above coding agents.
The strategy faces a neutrality test: Databricks has financial relationships with major model providers while acting as the layer above them. Ghodsi said the company is multicloud and supports open-source alternatives, so customers are not pushed toward a single vendor.
Lakebase Targets the Agent Database Boom
The database business is where the agent thesis becomes tangible, because AI-generated software behaves differently from software written by humans. An AI coding agent can create several versions of an application, test them simultaneously and discard most of the work within minutes. Ghodsi estimates humanity could write more software in the next nine to 12 months than it has written throughout its entire history, and every application needs a database.
Databricks reports more than 16 million Postgres database starts a day, with Lakebase able to launch a database in less than one second compared with minutes for some competing systems. Its branching architecture lets a company create a branch of a petabyte-scale database in about a second without a full physical copy, because Lakebase tracks only the changes.
The Lakehouse architecture — which combines a data warehouse's structure with a data lake's flexibility — has been widely adopted, and Ghodsi acknowledged competitors including Snowflake "are not quite there, but moving in the same direction." Snowflake is defending a powerful analytical franchise while moving into adjacent database territory, while Oracle starts from the opposite position, owning the traditional operational database market Databricks is trying to penetrate. SingleStore challenges the novelty of the architecture but must prove comparable production revenue at scale.
"Databricks can expand, but AI frontier labs can move downstream while hyperscalers can go upstream," Lau said. "A key advantage for Databricks is its open format for data, allowing enterprises to move data in and out freely."
The company's valuation already exceeds public market rival Snowflake's market value, and the funding round signals investor confidence in the AI data infrastructure buildout. Databricks is among a growing group of companies delaying public listings given the funding opportunities in private markets, with SpaceX's volatile debut and the pending IPOs of Anthropic and OpenAI reshaping the calendar. Ghodsi said the company is adopting public-company practices and waiting for "a little more stability in the markets" before listing.
This article is for informational purposes only and does not constitute investment advice.
[1] Databricks raises $5 billion in financing at $190 billion valuation[2] Databricks Hits $190 Billion Valuation As CEO Ali Ghodsi Claims AGI Already Arrived[3] Databricks wraps $5 billion funding round at $190 billion valuation[4] Databricks closes $5B funding round at $190B valuation | Seeking Alpha[5] Databricks valued at $190 billion in latest funding round
