I conclude that Databricks prefers the stability of private capital over the scrutiny of a public listing. The company secured $5 billion in equity this February, maintaining its $134 billion valuation, and added $2 billion in new debt capacity with JPMorgan leading the financing. Investors including Goldman Sachs, Morgan Stanley, Glade Brook Capital, Neuberber Berman, and the Qatar Investment Authority participated in the equity round. This capital funds the development of Lakebase, an AI-focused database, and Genie, a conversational assistant. Databricks reported an annualized revenue run-rate of $5.4 billion in the fourth quarter, marking a 65% increase, while AI products alone contribute $1.4 billion to that total. I find the $134 billion valuation aggressive, especially as the company must fund expensive AI infrastructure.
The AI Data Rivalry
The technical rivalry between Databricks and Snowflake defines the current data market. Databricks utilizes a Lakehouse architecture built on Apache Spark and Delta Lake, providing model serving and integrated notebook development for its 20,000 customers, which include over 60% of the Fortune 500. It uses Unity Catalog to manage governance across data and AI assets. Snowflake operates a cloud-native data platform that specializes in enterprise analytics, governed data sharing, and high concurrency for BI users through its virtual warehouses.
| Metric | Databricks | Snowflake |
|---|---|---|
| AI Revenue Run-rate | $1.4 billion | $100 million |
| Total Customers | 20,000 | 12,600 |
| Core Architecture | Lakehouse | Cloud Warehouse |
Snowflake reached a $100 million AI revenue run rate in Q3 FY26. It also signed a $200 million partnership with OpenAI to provide models like GPT-5.2 within Snowflake Intelligence. This allows customers such as Canva and WHOOP to use OpenAI models directly on their proprietary data. Snowflake also provides Cortex AI and Snowpark for developer workflows. Databricks competes with its model serving features and its acquisition of Neon, a serverless database company. I believe Snowflake currently holds the lead in enterprise-ready AI deployment because its partnership provides immediate access to OpenAI’s models.
Market Volatility and Private Capital
The 2026 IPO market looks difficult for high-growth technology firms. CEO Ali Ghodsi described 2026 as a "terrible" year to list because blockbuster offerings from SpaceX and Anthropic will absorb investor capital. Instead of a public debut, Databricks pursues a valuation between $165 billion and $175 billion in fresh private funding. This target marks a massive leap from the $134 billion valuation. I suspect the company stays private to avoid the volatility that hit Snowflake after its 2020 debut. You likely recognize that maintaining control is a primary motivation for remaining private.
The company’s rise has been rapid, with its valuation climbing from $38 billion in 2021 to $134 billion in 2026, and this is one of the steepest private market climbs in the enterprise AI sector. This $175 billion target implies a multiple above 30 times revenue. Databricks must also manage a significant capital requirement for employee liquidity, as the company needs roughly $5 billion to meet internal commitments. I find the tension between rapid growth and massive AI infrastructure expenses to be a pressing economic factor for this firm. A September 2026 PitchBook report estimated an operating value near $69 billion. Will the company eventually face a correction if AI infrastructure costs continue to pressure gross margins?
