Databricks is a US enterprise software company that sells a unified data-and-AI platform. It was founded in 2013 by the team behind Apache Spark, the open-source distributed-computing framework, and is led by co-founder and chief executive Ali Ghodsi. Its core product combines data warehousing and data-lake functions in an architecture the company markets as the "lakehouse," and it has extended that platform toward machine-learning and generative-AI workloads, including tools for building and serving models on enterprise data.
Snapshot
Valuation
| Date | Valuation | Note | Source |
|---|---|---|---|
| 2026-08-13 | $190B ($5B raise, closed) | Round led by Coatue, Blackstone, MGX, T. Rowe Price and Sixth Street Growth. CNBC describes it as following the $134B round six months earlier and does not mention the July $188B reporting | cnbc.com |
| 2026-07-20 | $188B ($3B raise) | Round led by Coatue Management confirmed; 40% above the December valuation; company cited surging AI demand | techcrunch.com; theinformation.com |
| 2026-07-16 | $188B (round in progress) | ~40% jump; new round led by Coatue | wsj.com |
| Jun 2026 | $165–175B (in talks) | Round reported in discussion, could launch within a month | investing.com |
| Dec 2025 | ~$134B | Series L round (~$5B raised); among the most valuable private software companies | tech-insider.org |
Revenue
| Date | Metric | Source |
|---|---|---|
| Q2 2026 (rep. 2026-08-13) | >$7B revenue run-rate, up more than 80% year over year; Lakebase past a $100M run rate; Lakehouse past a $1.5B run rate | cnbc.com |
| Feb 2026 | $5.4B revenue run-rate, up ~65% year over year | databricks.com |
The relationship between the July and August 2026 rounds is not stated by either source. The July reporting described a $3B raise at $188B led by Coatue; the August 13 close is a $5B raise at $190B with Coatue among five named leads, and CNBC positions it against the $134B round rather than against the July figure. Whether these are two rounds or one round upsized at close is not established by the available reporting.
AI strategy
Databricks positions itself as infrastructure for enterprises building AI systems on their own data, rather than as a developer of frontier foundation models. Its 2023 acquisition of MosaicML brought in model-training capabilities, and the company has released open model families and data-intelligence tooling aimed at letting customers fine-tune and deploy models against governed enterprise data. This places it in the applied-AI and enterprise-platform layer alongside competitors such as Snowflake, and as a partner-and-competitor to the frontier labs whose models run on its platform.
Path to public markets
Ghodsi has repeatedly indicated that Databricks intends to go public but has resisted committing to a near-term listing, at points calling 2026 a poor year to debut given market conditions while signaling an IPO could come as soon as 2027. The June 2026 discussions of a private round at a $165–175 billion valuation were reported alongside continued private fundraising rather than an imminent offering, distinguishing Databricks from the frontier labs (OpenAI, Anthropic) that filed confidentially for IPOs in the same period (Source: investing.com; cryptobriefing.com).
Ghodsi restated the position on the day the August 2026 round closed: "We're not just a company that wants to stay in the private, but right now I just think there would be too much distraction in the public market." CNBC placed Databricks among a group of companies deferring a listing because private markets have supplied the capital instead (Source: cnbc.com).
Enterprise demand and model-cost pressure
Ghodsi attributed the August 2026 demand picture to enterprise adoption of AI agents, naming the Lakebase database, the Genie business agent, and the AI Gateway tool that governs model use and costs. He described rising inference costs as a driver of demand for AI Gateway and for open-source tooling, and said customers were adopting Chinese models more readily than before: "The attitude a year or two ago was we just need frontier proprietary, and we can just ignore Chinese models… What has happened is that this token maxing has freaked out the CFOs" (Source: cnbc.com). The account connects the enterprise-platform layer to the open-weight cost argument tracked on Open-Weight Frontier Models and US-China AI Competition: Different Races, Different Metrics.
Relationships
- related: OpenAI, Anthropic — frontier labs whose models run on the Databricks platform; contemporaneous IPO-track peers
- deploys-in: Financial Services — AI Deployment, Healthcare — AI Deployment — enterprise sectors that use the platform for data and AI workloads
Provenance note: Built from secondary reporting (CNBC for the August 2026 round, Q2 metrics and the Ghodsi quotes; TechCrunch, WSJ, The Information and Investing.com for the earlier valuation rows; Crypto Briefing for the IPO position) and a Databricks press release on the February 2026 run-rate. Snapshot figures are fast-decay (3-month window).