Databricks has closed a $5 billion funding round at a $190 billion valuation, with Coatue leading the investment. The company also disclosed that its revenue run-rate has passed $7 billion, with growth accelerating to more than 80 percent year over year in the second quarter. The round adds to a growing list of massive private financing deals in the artificial intelligence sector and gives Databricks additional financial firepower as it expands its data platform.
A Second Massive Round in 2026
The investment was led by Coatue and included Blackstone, MGX, T. Rowe Price, and new investor Sixth Street Growth. It is the company's second major round this year. In February, Databricks raised money at a $134 billion valuation. The new round values the company at $190 billion, a 42 percent increase. Earlier reports had suggested the valuation would land at $188 billion, but the closing price was $2 billion higher.
The ability to raise $5 billion in a single round reflects the enormous investor appetite for companies that provide infrastructure for data-intensive AI workloads. Databricks is not alone in attracting such capital. Many of the largest private companies in technology have chosen to raise in the private markets rather than pursue an initial public offering, and Databricks is following that pattern.
Growth Accelerates at Scale
The most notable part of the announcement is the acceleration in growth. In February, Databricks reported a revenue run-rate of $5.4 billion and year-over-year growth of 65 percent. By the second quarter, the run-rate had passed $7 billion and growth had accelerated to more than 80 percent. This is unusual for a company of this size. Most software businesses see their growth slow as revenue increases, but Databricks has expanded its addressable market and moved into adjacent categories such as data warehousing and database management.
The company said its data warehousing business has passed a $1.5 billion run-rate and is growing at more than 100 percent year over year. Lakebase, its database product, has passed $100 million in run-rate. Databricks also said it has been adjusted free cash flow positive over the last twelve months, a rare milestone for a private company still investing heavily in growth.
Enterprise Customers Are Spending Big
Databricks also shared customer metrics that are not commonly disclosed in private rounds. More than 1,000 accounts currently spend at a $1 million run-rate, and more than 100 spend at a $10 million run-rate. Those figures highlight the company's focus on large enterprises, which rely on Databricks to process, store, and analyze massive amounts of data.
This customer concentration is both a strength and a risk. Large accounts can provide stable, recurring revenue and expand quickly, but they can also create concentration risk if a major customer decides to cut spending. Databricks' top-of-market metrics suggest that it has become a core part of the technology stack for many of the world's largest companies.
How the Valuation Compares With Snowflake
The $190 billion valuation looks expensive in absolute terms, but compared with public market peers it may be less extravagant than it appears. At a $7 billion run-rate, Databricks is valued at roughly 27 times revenue. Snowflake, one of its closest competitors, trades near 23 times trailing revenue of $5.03 billion. Snowflake's growth is around 30 percent, while Databricks says its growth is above 80 percent. A four-point multiple premium for nearly three times the growth rate could be considered modest.
However, the comparison requires caution. Revenue run-rate annualizes the most recent quarterly or monthly revenue, which can flatter a fast-growing company. Snowflake's multiple is based on twelve trailing months of reported revenue, a more conservative baseline. If Databricks' actual trailing revenue is significantly below its run-rate, the 27 times figure may understate the true valuation multiple. Still, the market appears to be giving Databricks credit for stronger growth without overpaying relative to public alternatives.
Why Stay Private?
Databricks chief executive Ali Ghodsi has said that 2026 is a bad year to go public. His comments refer to a crowded IPO pipeline that includes SpaceX, OpenAI, and Anthropic, three companies expected to absorb roughly $200 billion of listing capital. That amount of supply could put downward pressure on valuations and make it difficult for new issuers to trade well in the secondary market.
By raising private capital at a $190 billion valuation, Databricks can delay an IPO without sacrificing access to funding. The company is also under less pressure to manage quarterly earnings expectations and can continue to invest in products with longer payback periods. Ghodsi's stance is a clear signal that Databricks believes the private markets are the better source of capital for now.
From Apache Spark to AI Agents
Databricks was founded in 2013 by the team behind Apache Spark, an open-source framework for large-scale data processing. The company built a business around making Spark easier to use in enterprises, then expanded into a broader data platform. It popularized the data lakehouse concept, which combines the cost and flexibility of data lakes with the performance and governance of data warehouses.
The company competes with Snowflake, as well as with Microsoft's Fabric, Google's BigQuery, and Amazon's Redshift and S3 ecosystem. Its differentiation lies in open standards and the ability to run on top of existing cloud infrastructure. Databricks has also made significant investments in AI, including its own large language models and tools for custom model building.
AI Agents Are the Next Growth Driver
The new funding will be directed to three products focused on enterprise AI agents. Ghodsi has described the vision as agents that hold context, stay accurate, and respect a budget, rather than chatbots that answer generic questions. Enterprise customers want AI that can act on their data, follow business rules, and operate within cost constraints.
This is a different approach from consumer AI products. For Databricks, the opportunity is to become the trusted data layer for AI agents, ensuring that they have access to the right data and that their outputs can be audited. The company has been building governance and lineage features that allow enterprises to trace how an AI agent arrived at a decision, which is critical in regulated industries.
A Changing Private Market Landscape
The Databricks raise is further evidence that the most valuable technology companies are staying private longer. The availability of large private checks means that companies can grow to public-market scale without the scrutiny and volatility of a stock exchange listing. That trend has implications for public markets, which lose the opportunity to participate in the early growth of these companies, and for late-stage private investors, who take on more risk.
Databricks has been transparent about its financial performance in this round, sharing revenue run-rate, growth, free cash flow, and customer metrics. This level of disclosure is closer to what a public company would offer and suggests the company wants to establish a track record of transparency. It also gives investors confidence that the valuation is grounded in measurable performance.
The company's ability to accelerate growth while maintaining a multi-billion-dollar revenue run-rate is rare. Whether it can sustain that pace as the enterprise AI market matures will determine whether the $190 billion valuation looks reasonable in hindsight. For now, Databricks has both the capital and the product momentum to remain a major force in the data and AI software market. The next few years will test whether its strategy of staying private and betting on AI agents pays off.