API Performance Tracking Template

Alert on p95 for early warning and p99 for urgent issues. Alerting on p50 (median) generates too much noise because medians fluctuate naturally. Alerting on p99 alone misses gradual degradation that affects a significant share of users.

Start with your current baselines and work backward from user impact. For checkout flows, research shows that conversions drop measurably above 200ms p95. For dashboard loads, users perceive anything under 1 second as fast. Set targets 20-30% better than your current baselines for endpoints that are already acceptable, and 50%+ better for endpoints with known complaints.

PMs should track the business impact of API performance, not the raw metrics. Know which endpoints map to revenue-critical flows (checkout, signup, search). Attend the weekly performance review. Ask engineering to flag when an optimization requires trade-offs that affect the product (e.g., adding caching that makes data slightly stale).

Re-baseline after any major architecture change, traffic growth exceeding 2x, or quarterly at minimum. Baselines drift as traffic patterns change, data volumes grow, and new features add complexity. Stale baselines lead to either false confidence or unnecessary alarm.

Use application performance monitoring (APM) tools like Datadog, New Relic, or Grafana with Prometheus. For external latency (what users actually experience), use synthetic monitoring (Pingdom, Checkly) or real user monitoring (RUM). The template works regardless of which tool you choose.