Aaron Montana

LaunchDarkly built a warehouse-native experimentation solution that seamlessly integrates with Snowflake while supporting a flexible hybrid approach. This session covers key architectural design choices, from embedding feature flagging and real-time experimentation directly into the AI Data Cloud to balancing in-warehouse processing with the performance needs of external services. We’ll explore the trade-offs between Snowflake Native and connected app deployment models, optimizing for scalability, and ensuring a seamless developer experience. Join us to see how we built an experimentation platform that powers both our customers and our own data-driven innovation.

Experimentation—using specific metrics to evaluate the impact of features you roll out—is essential to making data-driven technology decisions. But first, organizations must overcome the technical challenge of building a scalable experimentation program.