Start with Data, Prove Value, Then Scale Is Snowflake’s Counter to the AI Hype

“A lot of folks went out and bought GPU capacity or model licenses without thinking about where that’s going to create value,” said Sridhar Ramaswamy, CEO of Snowflake. His perspective on enterprise AI investments has reflected a broader product philosophy: start with data, build small, prove value, and expand from there.

Ramaswamy pointed to a lightweight chat interface for Snowflake’s internal sales enablement content as an example. “It didn’t cost a lot of money to build,” he said, “but it’s getting a lot of use. That told us we were onto something worth growing.”

That same principle underpins the launch of the Cortex LLM Playground in January 2025. The no-code tool was designed for Snowflake customers to experiment with large language model (LLM) prompts through a simple chat interface. Users can run side-by-side comparisons of model outputs and directly integrate generated SQL code into their workflows. The product is deliberately lightweight and focused on usability which is a contrast to high-cost, large-scale deployments.

“We made $3.5 billion last year, growing at 30%,” Ramaswamy said. “And if we fulfill our mission to truly help enterprises mobilize data, we should be growing at faster than that clip for the next 10-ish years.” He compared the opportunity to his time at Google Search, where revenue scaled from $1.5 billion to nearly $100 billion by the time he left.