Kubernetes (K8s) has won the container orchestration war. It is a highly capable, infinitely scalable system, but it comes with an infamously steep learning curve. After managing production clusters on AWS EKS and Google Cloud GKE for over three years, our DevOps team has gathered critical insights.
The most common mistake teams make is manually allocating pod resources without profiling their applications. Setting CPU and Memory requests too low leads to constant Pod Evictions and Out Of Memory (OOM) kills. Setting them too high leads to massive wasted server costs. Always use tools like Goldilocks or Vertical Pod Autoscaler to profile actual usage.
Autoscaling must be configured at two levels immediately. First, configure Horizontal Pod Autoscaling (HPA) based on custom Prometheus metrics (like active HTTP requests, not just CPU) to handle sudden load surges. Second, configure Cluster Autoscaler so the cloud provider automatically provisions new underlying nodes when your pods have nowhere to run.
Security configurations cannot be ignored. By default, pods can communicate with any other pod in the cluster. Implement Network Policies immediately to enforce zero-trust networking within the cluster. A compromised frontend pod should never have direct network access to your internal billing database pods.
Furthermore, enforce strict secret isolation. Never mount secrets as environment variables where they can be accidentally dumped into application crash logs. Use external secret managers like AWS KMS or HashiCorp Vault, and inject them directly into memory at runtime using CSI drivers.
Stateful applications in Kubernetes remain a significant challenge. While it is entirely possible to run PostgreSQL or Redis inside K8s using StatefulSets and Persistent Volumes, the operational overhead of managing backups and replication is rarely worth it. We heavily advise offloading databases to managed cloud services (like AWS RDS) while keeping K8s strictly for stateless application APIs.
Finally, invest heavily in observability. When a microservice fails in a distributed cluster of 500 pods, checking standard logs is impossible. Implement OpenTelemetry for distributed request tracing, and aggregate logs centrally using the EFK stack (Elasticsearch, Fluentd, Kibana) or Datadog to ensure rapid incident response.