A/B Testing Rollout

Route different user segments to different versions to compare outcomes.

experiment

Detailed Description

A/B testing focuses on product impact, while canary focuses on release safety.

You can combine both: canary a new build first, then run A/B experiments on stable cohorts.

Segment users deterministically (for example by userId hash) so the same user stays in one variant.

Evaluate success with business KPIs such as conversion, retention, and revenue, not just system latency.

A/B tests may run long-term, while canary typically ends once rollout reaches stable 100% traffic.

Segment users by rule
Group A -> version A
Group B -> version B
Compare conversion and engagement

Pros

Optimizes product decisions with measured business impact

Cons

Needs strong experiment design and metrics interpretation

Examples: LaunchDarkly, Statsig, Optimizely, custom feature-flag routers