Are Snowflake and Databricks Overpaying for Database Startups?

Snowflake is about to spend $250 million on Crunchy Data, which generates just over $30 million annually. Databricks, meanwhile, dropped about $1 billion on Neon—probably with even less revenue—followed by a $1 billion–$2 billion deal for Tabulr simply to keep it out of Snowflake’s hands. Both companies claim these buys are strategic for “agentic AI,” but the underlying reality is much more cynical. These multibillion-dollar checks serve as a brutal display of one-upmanship that punishes common sense.

Snowflake’s Crunchy Data Gamble

At first glance, Snowflake’s $250 million acquisition of Crunchy Data seems reasonable. Crunchy Data provides a hardened, cloud-hosted PostgreSQL distribution with security and performance tweaks. Snowflake can integrate it into a managed service and channel more data into its analytics engine. Crunchy Data brings around 100 employees, a loyal customer base, and an open-source pedigree.

Yet Snowflake passed on buying Neon last year—a startup with similar PostgreSQL roots but boasting serverless compute, branching, and time-travel features that align perfectly with Databricks’ AI-centric vision. Instead of grabbing Neon, Snowflake pivoted to Crunchy Data, suggesting it cares less about pure technology and more about sending a market signal: “We’ll still play the database game even if we missed Neon.” The lesson? Often these acquisitions are driven by ego and optics rather than by product-fit or customer value.

Databricks’ Billion-Dollar Playbook

Databricks never pretends to flinch at high price tags. It shelled out roughly $1 billion for Neon—plausibly over thirty times Neon’s annual revenue. Then it outbid Snowflake for Tabulr at up to $2 billion. Tabulr’s pitch revolved around a code-centric interface that fused data prep with AI suggestions—ideal for a future built on generative AI. Databricks couldn’t risk Snowflake getting that edge, so it paid a sky-high premium.