Databricks IPO: Pros and Cons (coffee.link)

Databricks, the private cloud-data leader now valued north of $100B with a >$4B revenue run rate and 50%+ growth, is delaying an IPO until market conditions improve (targeting 2025–2026) despite strong unit economics—80% gross margins, 140% net dollar retention—and positive free cash flow. Its unified “lakehouse” platform (Spark heritage, Delta Lake, MLflow, Unity Catalog) powers 15,000 customers including 60% of the Fortune 500, with 650+ customers spending over $1M annually. Recent moves—$10B Series J interest at a $62B mark (with $19B demand), acquisitions like MosaicML and Tabular, and open-source DBRX models that beat GPT-3.5 on some benchmarks—underline its drive to own AI data + model infrastructure while preserving developer mindshare via open source. For the AI/ML community, Databricks is a strategic infrastructure supplier: it simplifies data engineering-to-model deployment, offers high-performance engines (Photon, UniForm), and builds governance features (Unity Catalog, Clean Rooms) that enable production-grade generative AI and agentic systems. The caveats are material: intense competition from Snowflake and hyperscalers, continual capital needs to keep pace with proprietary LLMs, and a frothy valuation that CEO Ali Ghodsi himself has warned may be “peak AI bubble.” The IPO timing and valuation will shape access to capital and pricing for critical AI infrastructure—making Databricks’ public debut one of the sector’s most consequential events.