Statsig Enables Warehouse-Native Experiments
Statsig
Warehouse native turns Statsig from a tool that asks for raw product data into one that meets enterprises where their data already lives. In practice, a company can keep user level events and business metrics inside Snowflake, BigQuery, or Databricks, then let Statsig compute experiment results and analytics on top of that data without copying the sensitive records into a separate SaaS store. That matters most for banks, healthcare companies, and large global software teams with strict privacy and residency rules.
-
This changes the buyer inside the account. A cloud hosted experimentation tool is usually bought by product and engineering. A warehouse native tool also appeals to central data teams, because they can use warehouse tables they already trust, including revenue, fraud, or retention tables that are not present in app event tools.
-
The model also changes the cost structure. Statsig says warehouse native runs with no ETL, and its own materials frame the setup as analysis on customer infrastructure. That lowers Statsig's storage and compute burden, while giving the customer direct visibility into where warehouse compute is being spent.
-
It is quickly becoming table stakes in enterprise experimentation. LaunchDarkly now supports warehouse native experimentation with Snowflake, BigQuery, and Databricks workflows, and Datadog bought Eppo in May 2025 to fold experimentation and feature flags into its broader product analytics stack. The market is moving toward one system that joins releases, measurement, and existing warehouse data.
The next step is deeper convergence between feature delivery and the modern data stack. Vendors that can sit directly on warehouse data, while still giving product teams fast rollouts, auto rollback, and clear experiment readouts, will win larger enterprise budgets and become harder to replace once they are wired into both engineering workflows and finance grade metrics.