How Feature Flags Can Safely Manage A/B Testing of AI Models in Production

Feature flags are a practical tool for rolling out AI model changes, but they introduce unique challenges because AI outputs are non-deterministic, quality signals are delayed, and poor variants can be costly rather than just broken. Model selection must always be handled server-side to prevent clients from manipulating which model gets called — and therefore who pays for it. Stable, hash-based user assignment is critical to ensure consistent model behavior within a session, as mid-session variant switches can corrupt both user experience and experiment results. Developers must also distinguish between experiment flags and entitlement flags, since conflating the two risks accidentally gating paid features behind rollout percentages. Choosing success metrics before launching an experiment — including cost and latency — is essential to avoid promoting an inferior model based on coincidental data movement.