Hypertune offers a streamlined approach to managing experiments by integrating them directly within feature flags, rather than handling them separately. This architecture simplifies rollout logic, improves data accuracy, and accelerates iteration by eliminating the need for separate "experiment flags" and the associated complexity and risk of managing them alongside main feature flags. By embedding experiments within the feature flag targeting logic, Hypertune prevents the logging of incorrect experiment exposures and ensures that users only enter experiments after meeting all necessary conditions. This integration maintains the integrity of experiment data by avoiding mismatches between experiment assignments and actual feature exposure, ultimately keeping code clean and experiments more reliable.