Kubernetes gives teams enormous flexibility, but that flexibility can easily turn into complexity if clusters are not set up with production in mind from day one. These are the practices we rely on when taking clients from a working demo to a resilient production deployment.
Resource Requests and Limits
Every container should declare CPU and memory requests and limits. Without them, the scheduler cannot make good placement decisions, and a single misbehaving pod can starve its neighbors. Start with conservative estimates from load testing and adjust based on real usage metrics.
Health Checks Done Right
- Liveness probes to restart containers that are stuck, not just slow
- Readiness probes to keep traffic away from pods that are still warming up
- Startup probes for applications with a slow initial boot sequence
Namespace and RBAC Strategy
Separate namespaces by environment and by team, and pair them with role-based access control so that engineers only have the permissions they actually need. This limits the blast radius of any mistake and makes audits far simpler.
Observability From the Start
Metrics, logs, and traces need to be in place before an incident happens, not during one. A combination of Prometheus for metrics, a centralized logging pipeline, and distributed tracing gives on-call engineers the context they need to resolve issues quickly.
The clusters that stay stable under load are the ones where every pod has clear limits, clear health checks, and clear ownership.
Progressive Rollouts
Use rolling updates with proper readiness gates, and consider canary or blue-green strategies for high-traffic services. Automated rollback based on error-rate thresholds turns a bad deploy from an incident into a non-event.
