Is Chalk Beating Databricks and Snowflake with Real Time AI?

Chalk, a San Francisco-based startup building infrastructure for real-time AI inference, has raised $50 million in Series A funding at a $500 million valuation. The round, announced Wednesday, was led by Felicis, with participation from Triatomic Capital and existing investors General Catalyst, Unusual Ventures, and Xfund. As part of the deal, Felicis founder Aydin Senkut will join Chalk’s board.

While most of the AI infrastructure landscape remains focused on training data pipelines and precomputed feature stores, Chalk is betting that the future lies in inference, the critical moment when AI models turn data into decisions.

“AI compute is shifting from training to inference,” said Marc Freed-Finnegan, Chalk’s co-founder and CEO. “That creates entirely new infrastructure needs that existing platforms weren’t built to handle.”

Chalk’s pitch is straightforward: where incumbents like Databricks and Snowflake specialize in batch processing and low-latency access to cached data, Chalk offers millisecond-level performance using fresh data at the time of inference. That capability is proving essential in industries like fintech, identity verification, and clean energy where decisions often need to be made in real time, not after a scheduled data refresh.