Kubernetes Monitoring Guide — Operating Prometheus and Grafana Effectively
A practical guide to Kubernetes monitoring with Prometheus and Grafana. Covers what metrics matter, how to think about alerts, and the common monitoring mistakes teams make in production.
TestForge Team ·
Without Monitoring, Operations Turn Into Guesswork
Kubernetes failures can occur at many layers:
- node
- pod
- deployment
- ingress
- application
- database
Monitoring is what makes diagnosis possible.
Common Monitoring Stack
- Prometheus for collection
- Alertmanager for alerting
- Grafana for dashboards
- kube-state-metrics for Kubernetes object metrics
- node-exporter for node-level visibility
Metrics Worth Tracking
Cluster Layer
- node CPU and memory
- disk pressure
- node readiness
Workload Layer
- pod restart count
- unavailable replicas
- CPU throttling
- memory working set
Service Layer
- request rate
- error rate
- latency percentiles
Business Layer
- login success rate
- order failures
- payment success rate
Business metrics often matter even more than infrastructure metrics during incidents.
Alerting Principles
- page only for issues that need immediate action
- use chat alerts for trends and warnings
- reduce duplication
Examples:
- node down: page
- deployment replicas unavailable: high priority
- sustained high CPU: Slack warning
Common Mistakes
- collecting everything without a clear purpose
- building dashboards but weak alerts
- watching infrastructure only, not application behavior
Closing Thoughts
Good Kubernetes monitoring is not about having the most graphs. It is about narrowing incidents quickly and making the next action obvious.
That requires careful metric selection and alert design, not just installing Prometheus.