This is the consolidated rollout strategy for AI features. It pulls together feature flags, canary deployment, eval harness, monitoring, and rollback into one coherent process. Reference for any team rolling out a new AI feature or model change.
Five phases: (1) internal-dogfood (10 employees, 1 week), (2) private beta (50 customers, 2 weeks, eval-gated), (3) canary 5% (general traffic, 7 days, alert-gated), (4) canary 25-75% (gradual ramp over 7-14 days), (5) full (100%, monitoring continues 30 days). Rollback at any phase via feature flag.
Phases
| Phase | Audience | Duration | Gate to next phase |
|---|---|---|---|
| Internal dogfood | 10 employees | 1 week | No critical bugs |
| Private beta | 50 customers | 2 weeks | Eval score holds + qualitative review |
| Canary 5% | Random 5% | 7 days | Metrics nominal |
| Canary 25% | 25% | 5 days | Metrics nominal |
| Canary 75% | 75% | 5 days | Metrics nominal |
| Full | 100% | Ongoing | Continued monitoring |
Controls
- Feature flags: GrowthBook or LaunchDarkly for traffic split
- Canary rollback: sub-1-minute via feature flag flip
- Eval gates: per-phase eval harness check
- Monitoring: p99 TTFT, error rate, eval drift, user feedback per cohort
- User segmentation: free vs paid; rollout to free first if risk-sensitive
- Comms: customer-facing changelog at each phase boundary
Playbook
- Pre-flight: eval harness on representative test set + load test + soak test
- Phase 1: 10 internal users get feature flag turned on; 1-week observation
- Phase 2: 50 friendly customers; 2-week feedback + eval
- Phase 3: 5% random; 7-day metrics watch
- Phases 4-5: ramp 25% → 75% → 100% with metric-gated promotions
- 30-day post-full monitoring before declaring stable
Verdict
The consolidated rollout strategy is mature SaaS practice applied to AI: feature flags + canary + eval + monitoring + rollback. Total elapsed time: 4-6 weeks. The discipline catches regressions early; manual rollouts skip steps and cause incidents.
Bottom line
5-phase rollout; flag-gated. See canary.
