Progressive Feature Rollout
Why big-bang releases fail
Section titled “Why big-bang releases fail”Deploying a new feature to 100% of users at once is a bet that nothing will go wrong. When something does — a performance regression, an edge case crash, a UX confusion — every user is affected simultaneously. Rollback means a full redeployment under pressure.
Progressive rollout replaces this all-or-nothing approach with controlled exposure. Feature flags gate access to the new feature. You start with 1% of traffic, monitor key metrics, then increase the percentage at each stage. If metrics degrade at any stage, you reduce the flag percentage without deploying code.
Architecture overview
Section titled “Architecture overview”┌────────────────────────────────────────────────┐│ Feature Experimentation SDK ││ Application checks flag for each request │└───────────────────────┬────────────────────────┘ │ ┌─────────────┼─────────────┐ ▼ ▼ ▼ ┌────────────┐ ┌──────────┐ ┌──────────┐ │ Stage 1 │ │ Stage 2 │ │ Stage 3 │ │ 1% users │ │ 10% │ │ 50% │ │ internal │ │ canary │ │ broad │──► 100% └──────┬─────┘ └────┬─────┘ └────┬─────┘ │ │ │ ▼ ▼ ▼ ┌────────────────────────────────────────┐ │ Monitoring Gates │ │ Error rate < 0.1% │ │ P95 latency < 200ms │ │ No critical alerts │ └────────────────────────────────────────┘Step 1: Create the feature flag
Section titled “Step 1: Create the feature flag”In Feature Experimentation, create a flag for your new feature. Define variables for any configuration values the feature needs (API endpoints, display thresholds, copy strings). This lets you adjust behavior without code changes.
Set the default state to off. No user sees the feature until you explicitly enable it.
Step 2: Implement the flag check in code
Section titled “Step 2: Implement the flag check in code”Wrap the new feature code path behind the flag check. Users who evaluate to “on” see the new feature. Users who evaluate to “off” see the existing behavior. Both code paths must be production-ready.
Ensure the flag check is fast and handles SDK failures gracefully — if the SDK cannot reach the server, the default (off) state should apply.
Step 3: Define rollout stages
Section titled “Step 3: Define rollout stages”| Stage | Audience | Traffic | Duration | Gate criteria |
|---|---|---|---|---|
| Internal | Employee email domain | 100% of employees | 2 days | No critical bugs reported |
| Canary | Random 1% | 1% of all users | 3 days | Error rate < 0.1%, latency < 200ms |
| Limited | Random 10% | 10% of all users | 5 days | Same metrics + no support tickets |
| Broad | Random 50% | 50% of all users | 3 days | Conversion rate neutral or positive |
| Full | Everyone | 100% | Permanent | Remove flag in next release |
Step 4: Monitor at each gate
Section titled “Step 4: Monitor at each gate”At each stage, check your monitoring dashboard for:
- Error rates — any increase in exceptions related to the feature area
- Latency — P95 response time for endpoints the feature touches
- Business metrics — conversion rate, engagement, revenue per session
- Support volume — new tickets mentioning the feature area
Only advance to the next stage when all gate criteria pass for the full stage duration.
Step 5: Handle rollback
Section titled “Step 5: Handle rollback”If metrics breach thresholds at any stage, reduce the flag percentage to 0%. This immediately stops exposing users to the new feature without deploying code. Investigate the issue, fix it, redeploy, and restart the rollout from Stage 1.
When to use this pattern
Section titled “When to use this pattern”Use progressive rollout for any user-facing feature change that could impact performance, conversion, or user experience. Skip it for backend refactors with no user-visible effect, or for urgent hotfixes where the risk of not deploying outweighs the risk of deploying broadly.