Implementation Patterns
Feature flags are a versatile tool. The same mechanism can serve many purposes — from simple on/off toggles to sophisticated A/B experiments. These patterns represent proven approaches that teams use to ship faster, safer, and smarter.
Basic Toggle
The simplest pattern: wrap new code in a boolean flag check. Deploy the code with the flag OFF, then flip it ON when you're ready to release. If something goes wrong, turn the flag OFF — no rollback needed.
When to use: Every new feature or code change that you want to decouple from deployment. Start here and graduate to more advanced patterns as needed.
Canary Release
Enable a feature for a small, specific subset of users first — internal employees, beta testers, or a single region. Monitor metrics and expand the audience gradually. This minimizes blast radius if something breaks.
When to use: High-risk changes, infrastructure migrations, or any feature where you want early signals before a broad rollout.
Kill Switch
An operational toggle that can immediately disable a subsystem or feature path. Kill switches are pre-deployed and tested — when a downstream dependency fails or a performance incident begins, you flip the switch instead of deploying a hotfix.
When to use: Third-party API integrations, payment processing, search indexing, or any dependency that can fail outside your control. Use the ops toggle category.
Gradual Rollout
Progressively increase the percentage of users who see a feature — 5% → 25% → 50% → 100%. FeatureSignals uses consistent hashing so the same user always gets the same experience during the rollout. Monitor error rates and latency at each step.
When to use: Features where you want to validate performance and reliability under increasing load. Combine with monitoring dashboards and automated rollback triggers.
A/B Testing Setup
Split users into two or more variants to measure the impact of different feature implementations. Use the AB flag type to define variants with weights, track impression events, and analyze results in your analytics platform.
When to use: When you have a hypothesis about user behavior and want data-driven decisions. Always define success metrics before starting the experiment. Use mutual exclusion groups if running multiple experiments.
Permission Flags
Gate access to features based on user attributes — plan tier, team, region, or custom properties. Permission flags typically use segment-based targeting: create a 'Premium Users' segment and target it to the premium-feature flag.
When to use: Feature gating by pricing tier, beta features for specific customers, region-specific compliance requirements, or internal-only admin features.
Config Flags
Use string, number, or JSON flags to store operational configuration that can change at runtime — rate limits, feature parameters, UI themes, or algorithm thresholds. Config flags let you tune behavior without deploying code.
When to use: Any operational parameter you might need to adjust in production without a full deploy cycle. Keep security-sensitive config in environment variables or a secrets manager.
General Advice
- Start simple. Use a basic boolean toggle. Graduate to gradual rollouts or A/B tests only when you need them.
- Set a category and expiration date. The toggle category sets lifecycle expectations. An expiration date prevents stale flags from accumulating.
- Clean up after rollout. Once a feature is at 100% and stable, remove the flag and the old code path. Every flag left behind is technical debt.
- Keep flags short-lived. The longer a flag exists, the more context is lost and the harder it becomes to remove safely.
- Use the right flag type.Boolean for on/off, string/number/JSON for config, AB for experiments. Don't abuse a boolean flag to carry configuration values.