Progressive Delivery: Feature Flags, Canaries, and A/B Testing

Beyond All-or-Nothing Deployments

Traditional deployments are binary: old version or new version. If the new version has problems, everyone is affected. Rollback is disruptive and slow.

Progressive delivery changes this. Deploy to a small percentage of users, monitor, gradually increase. Problems affect few users and are caught early. Rollback is instant - just stop the rollout.

The Progressive Delivery Spectrum

Feature flags: Toggle features for specific users, cohorts, or percentages. Ship code without activating it. Enable for internal users first, then beta users, then everyone.

Canary releases: Deploy new version alongside old. Route small percentage of traffic to new version. Monitor metrics. If healthy, increase traffic. If not, route all traffic back to old version.

Blue-green deployments: Run two identical environments. Deploy to inactive environment, test, switch traffic. Instant rollback by switching back.

A/B testing: Route users to different variants to measure business metrics. Which checkout flow converts better? Statistical significance determines the winner.

Feature Flags Deep Dive

Feature flags separate deployment from release. Code ships to production but is inactive until the flag is enabled.

Use cases beyond simple toggles:

Kill switches: Disable features during incidents

Gradual rollout: 1%, 5%, 10%, 50%, 100%

User targeting: Beta users, enterprise customers, specific regions

Experimentation: A/B tests with statistical analysis

Choose a feature flag service (LaunchDarkly, Split, Flagsmith) or build your own for simple cases. The investment pays off in deployment confidence.

Canary Deployments in Practice

Canaries automate the rollout decision. Define success metrics (error rate, latency, business KPIs), deploy to small percentage, automatically promote or rollback based on metrics.

Tools like Flagger and Argo Rollouts integrate with Kubernetes. They manage the traffic shifting, run the analysis, and trigger rollback if metrics breach thresholds.

Key configuration decisions:

Analysis interval (how often to check metrics)

Step weight (how much to increase traffic each step)

Threshold (how many failures before rollback)

Metrics (what defines success)

A/B Testing for Business Decisions

A/B testing answers business questions: Does this feature improve conversion? Does this copy perform better?

Requirements for valid A/B tests:

Randomization: Users must be randomly assigned to variants

Sample size: Enough users to detect meaningful differences

Isolation: Only one variable changes between variants

Duration: Run long enough for statistical significance

Do not peek at results early and stop when one variant looks better - that is p-hacking. Commit to sample size and duration before starting.

Best Practices

Define metrics before rollout: Know what success looks like

Automate rollback: Human reaction time is too slow

Start small: 1% canary catches problems with minimal impact

Clean up flags: Technical debt accumulates fast

Instrument thoroughly: Cannot decide without data

Test the rollback: Verify it works before you need it