gradual rollout

**Gradual rollout** (also called canary deployment or progressive delivery) is a deployment strategy where a new version of a model, feature, or service is released to a **small subset of users first**, then progressively expanded to the full user base as confidence in the change grows. **How Gradual Rollout Works** - **Stage 1 (Canary)**: Route **1–5%** of traffic to the new version. Monitor closely for errors, latency, and quality regressions. - **Stage 2 (Early Adopters)**: If metrics look good, increase to **10–25%** of traffic. - **Stage 3 (Broad Rollout)**: Expand to **50%**, then **75%** of traffic. - **Stage 4 (Full Rollout)**: Route **100%** of traffic to the new version. - **Rollback**: If issues are detected at any stage, immediately route all traffic back to the previous version. **Why Gradual Rollout Matters for AI** - **Model Regression Detection**: A new model may perform well on benchmarks but poorly on specific real-world queries. Gradual rollout catches these issues before they affect all users. - **Prompt Sensitivity**: Small changes to system prompts can cause unexpected behavior that only manifests at scale. - **Safety**: A model that passes safety testing may still produce problematic outputs in production edge cases. - **User Experience**: Users may react negatively to different model behavior — gradual rollout limits the blast radius. **Rollout Criteria** - **Error Rate**: New version error rate must be ≤ old version. - **Latency**: p50, p95, and p99 latency must not regress significantly. - **Quality Metrics**: LLM-as-judge scores, user ratings, or task completion rates should be equal or better. - **Safety Metrics**: Content filter trigger rates, refusal rates, and toxicity scores within acceptable ranges. **Implementation** - **Traffic Splitting**: Use load balancers (NGINX, Envoy, Istio) to route percentages of traffic. - **Feature Flags**: Use feature flags to control which users see the new version. - **A/B Testing Platforms**: Use tools like **LaunchDarkly**, **Optimizely**, or custom frameworks. **Best Practice**: Automate rollout progression with **automated quality gates** — if key metrics meet thresholds for a defined period, automatically advance to the next rollout stage. If any metric breaches a threshold, automatically roll back. Gradual rollout is a **non-negotiable practice** for production AI systems — deploying a new model to 100% of users simultaneously is a recipe for incidents.