Kubernetes in Production: 12 Practices That Prevent Pager Fatigue
From cluster sizing and node hygiene to autoscaling, progressive delivery, and policy-as-code: a practical checklist we use to keep Kubernetes boring, predictable, and cost-efficient.
Kubernetes is powerful, but production reliability comes from the routines around it. Below is a battle-tested checklist we use to reduce outages, surprise bills, and 2am firefights—without slowing teams down.
How to use this checklist
Treat this as an engineering "definition of done" for platform reliability. Apply it in small iterations: start with SLOs, GitOps, and scaling validation, then tighten security and incident readiness.
Key Takeaways
- ✓Define SLOs first (availability, latency, error budget) and wire alerts to symptoms—not noise.
- ✓Standardize deployments with GitOps (Argo CD/Flux) and make rollbacks a first-class pathway.
- ✓Use requests/limits intentionally; track over/under-provisioning and right-size continuously.
- ✓HPA + Cluster Autoscaler are table stakes—validate scaling behavior with load tests.
- ✓Separate node pools by workload class (system, bursty, GPU, stateful) and isolate risk.
- ✓Adopt PodDisruptionBudgets and topology spread constraints to survive failures gracefully.
- ✓Implement progressive delivery (canary/blue-green) with automated health gates.
- ✓Centralize observability (metrics, logs, traces) and enforce consistent labeling.
- ✓Treat ingress, certs, DNS, and rate limiting as shared platform capabilities.
- ✓Use policy-as-code (OPA/Gatekeeper/Kyverno) for guardrails, not manual review gates.
- ✓Harden supply chain (SBOMs, image signing, minimal base images, vulnerability SLAs).
- ✓Practice incident readiness: runbooks, game days, and "time-to-rollback" drills.
Ready to Implement This?
If you want, we can apply this checklist to your environment and deliver a short, prioritized plan (reliability + cost + security) tailored to your release process and org structure.
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