Sticky bucketing

Without it, a 10% percentage rollout would re-roll on every request and a user would flicker in and out of the feature — a jarring experience and useless experiment data. Because the bucket is deterministic, raising the rollout from 10% to 25% only adds users to the enabled cohort; the original 10% never move. The same property lets an A/B test hold a clean, stable split for the life of the experiment.

The SDK hashes the user identifier and the flag key together and maps the result onto 0–99. Folding the flag key into the input means one user gets independent buckets across different flags, so unrelated rollouts never correlate. The computation runs locally against in-memory configuration, so it is sub-millisecond, needs no coordination between servers, and produces the same deterministic result across every Featureflip SDK. It is the mechanism a targeted rollout layers on top of when it rolls out to a percentage within a chosen audience; the rollout strategies guide covers the serving model.