feature-flags

Feature flags enable gradual rollouts by controlling feature availability at runtime. Use boolean or percentage flags to expose new features to a subset of users, then incrementally increase the percentage until full deployment. This reduces risk by catching issues early in production.

Canary releases use percentage flags to gradually expose a feature to increasing user cohorts, monitoring for errors. A/B testing uses multivariate flags to compare feature variants across user groups and measure impact. Both rely on feature flags but serve different goals: stability versus performance measurement.

Yes, targeted flags enable user-based feature control. Use deterministic hashing for consistent bucketing and rule-based evaluation to assign individual users or user groups to feature variants. This supports personalized rollouts, beta programs, and beta user targeting.

Kill switches are boolean flags that instantly disable features at runtime without redeployment. When toggled off, the flag evaluation engine immediately stops routing traffic to the feature, providing rapid incident response without code changes or service restarts.

Feature flags require Redis-backed storage for flag state, in-memory caching for low-latency evaluation, and a centralized evaluation engine. This setup supports versioning, auditing, and safe rollout patterns while keeping evaluation performant across distributed systems.

Yes, multivariate flags support experiments with more than two variants. Rule-based evaluation assigns users to variant groups using deterministic hashing for consistency. This enables multi-armed testing beyond simple on/off toggles or A/B comparisons.