Full-Stack Observability: Implementing Prometheus, Grafana & Loki for Microservices

Traditional monitoring simply asks: "Is the server alive?" Observability asks: "Why did a specific user in Harare experience a 12-second checkout delay at 2:15 PM?"

By integrating metrics (Prometheus), logs (Loki), and traces (OpenTelemetry), engineers trace a single API request through every microservice, cache, and database call.

Below is a production Prometheus alert rule that triggers PagerDuty notifications when API error rates exceed acceptable SLA thresholds.

groups:
- name: api_service_alerts
  rules:
  - alert: HighAPIErrorRate
    expr: |
      sum(rate(http_requests_total{status=~"5.."}[5m]))
      /
      sum(rate(http_requests_total[5m])) * 100 > 2.0
    for: 2m
    labels:
      severity: critical
    annotations:
      summary: "High 5xx error rate detected on {{ $labels.instance }}"
      description: "HTTP 5xx error rate is currently {{ $value | printf \"%.2f\" }}% for the last 2 minutes."

Keep executive dashboards simple: top row displays active users, transaction volume, and overall error rate.

Bottom panels provide granular breakdowns by API endpoint, database query latency, and Redis cache hit ratios.