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.