Free Capacity Is a Boundary: Load-Test Your AI Reviewer Before Production

Описание и применение

Команды могут измерить пределы AI‑ревью, определяя, подходит ли бесплатный тариф, и избежать задержек в CI.

Adding an AI reviewer can slow merges and break builds, but teams rarely test the reviewer’s operational limits.

Teams gain a clear operational boundary for AI reviewers, enabling informed decisions on tier usage and mitigating merge delays.

Run a local probe script to send burst review requests and collect latency, token usage, and retry data.
Create a GitHub Actions workflow that triggers the probe manually and logs results.
Analyze p50, p95 latency, average tokens per review, and retry rate to locate capacity boundaries.
Use the data to decide if free tier meets workload needs or if paid tier is required.
Implement retry limits and alerting to prevent runaway load.
Deploy a feature flag for AI review and rollback if thresholds are exceeded.
Pin prompt and model version to maintain consistency.
Monitor and adjust based on observed metrics.

Send a burst of review requests. Watch the latency curve. Watch the token burn rate. Watch the error rate.

Track four numbers. Latency median. Latency tail. Token consumption. Retry ratio.