Monitoring and Alerting

Monitoring encompasses three pillars of observability: metrics for quantitative time-series data, logs for detailed event records, and traces for request flow across distributed services. Alerting rules define conditions that indicate problems, such as error rate exceeding 1%, latency p99 exceeding 500ms, or CPU utilization above 90%. Alerts should be actionable: every alert should require human investigation and have a documented response procedure.

For AI product teams, monitoring must extend to AI-specific signals: model inference latency, prediction confidence distributions, feature store freshness, data pipeline lag, and model drift indicators. Growth teams should monitor experiment health metrics to detect when A/B tests produce unexpected results that might indicate bugs rather than genuine treatment effects. The challenge is balancing comprehensive monitoring against alert fatigue. Teams should start with a small set of high-signal alerts tied to user-facing impact and expand coverage as they learn which signals matter most. Dashboards that correlate infrastructure metrics with product metrics help teams quickly determine whether an infrastructure issue is affecting user experience.