Kubernetes has won the container orchestration battle. But running Kubernetes in production is significantly more complex than running a local development cluster. Here are the lessons we have learned from deploying and operating Kubernetes at scale.
Lesson 1: Start with Managed Kubernetes
Running your own Kubernetes control plane is a full-time job. Unless you have specific requirements that mandate self-hosting, use a managed service like EKS (AWS), GKE (Google), or AKS (Azure).
Managed services handle control plane upgrades, etcd backups, and high availability — letting your team focus on deploying applications rather than maintaining infrastructure.
Lesson 2: Resource Requests and Limits Are Critical
Every container should have CPU and memory requests and limits defined. Without them, a single misbehaving pod can consume all cluster resources and take down other services.
Set requests to the typical usage and limits to the maximum acceptable usage. Monitor actual consumption and adjust over time.
Lesson 3: Observability is Non-Negotiable
In a distributed system, you cannot troubleshoot what you cannot see. Invest in the three pillars of observability:
- Logging — Centralized log aggregation with tools like Elasticsearch or Loki
- Metrics — Prometheus for time-series metrics, Grafana for visualization
- Tracing — Distributed tracing with Jaeger or Zipkin to follow requests across services
Lesson 4: Network Policies Matter
By default, every pod in a Kubernetes cluster can communicate with every other pod. This is a security risk. Network policies should restrict traffic to only the connections that are necessary.
Lesson 5: Automate Everything
Manual operations do not scale. Every aspect of your Kubernetes workflow should be automated:
- Infrastructure as Code with Terraform or Pulumi
- GitOps with ArgoCD or Flux for declarative deployments
- Automated scaling with Horizontal Pod Autoscaler and Cluster Autoscaler
- Automated certificate management with cert-manager
Lesson 6: Plan for Failure
Pods crash. Nodes fail. Networks partition. Your application must be designed to handle these failures gracefully:
- Run multiple replicas of every service
- Use pod disruption budgets to ensure availability during maintenance
- Implement health checks (liveness and readiness probes)
- Test failure scenarios regularly with chaos engineering
Conclusion
Kubernetes is powerful but complex. The teams that succeed are those that invest in automation, observability, and operational discipline. Start simple, add complexity as needed, and always prioritize reliability over features.
