Making Kubernetes Production-Ready: A Practical Checklist

Moving from a development Kubernetes cluster to a production-ready environment is a significant leap that requires careful planning and attention to detail. After deploying multiple production Kubernetes clusters across different industries, I've compiled this comprehensive checklist to help you avoid common pitfalls and ensure your cluster is truly production-ready.

1. Cluster Architecture and High Availability

Multi-Zone Deployment

Your cluster should span multiple availability zones to ensure resilience against zone failures:

apiVersion: v1
kind: Node
metadata:
  name: node-1
  labels:
    topology.kubernetes.io/zone: eastus-1
    topology.kubernetes.io/region: eastus

For Azure Kubernetes Service (AKS), enable zone redundancy:

resource "azurerm_kubernetes_cluster" "main" {
  name                = "production-aks"
  location            = azurerm_resource_group.main.location
  resource_group_name = azurerm_resource_group.main.name
  dns_prefix          = "production-aks"

  default_node_pool {
    name       = "system"
    node_count = 3
    vm_size    = "Standard_D4s_v3"
    zones      = ["1", "2", "3"]

    # Enable auto-scaling
    enable_auto_scaling = true
    min_count          = 3
    max_count          = 10
  }
}

Separate Node Pools

Use dedicated node pools for different workload types:

System pool: For system components (kube-system, monitoring)
Application pool: For your application workloads
Batch pool: For batch processing jobs (with taints and tolerations)

2. Security Hardening

Pod Security Standards

Implement Pod Security Standards to enforce security policies:

apiVersion: v1
kind: Namespace
metadata:
  name: production-apps
  labels:
    pod-security.kubernetes.io/enforce: restricted
    pod-security.kubernetes.io/audit: restricted
    pod-security.kubernetes.io/warn: restricted

Network Policies

Implement network segmentation with Network Policies:

apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: deny-all-ingress
  namespace: production-apps
spec:
  podSelector: {}
  policyTypes:
  - Ingress
  - Egress
  egress:
  - to: []
    ports:
    - protocol: TCP
      port: 53
    - protocol: UDP
      port: 53

RBAC Configuration

Implement least-privilege access control:

apiVersion: rbac.authorization.k8s.io/v1
kind: Role
metadata:
  namespace: production-apps
  name: app-deployer
rules:
- apiGroups: ["apps"]
  resources: ["deployments", "replicasets"]
  verbs: ["get", "list", "watch", "create", "update", "patch"]
- apiGroups: [""]
  resources: ["pods", "services"]
  verbs: ["get", "list", "watch"]

3. Resource Management and Limits

Resource Quotas

Prevent resource exhaustion with namespace quotas:

apiVersion: v1
kind: ResourceQuota
metadata:
  name: production-quota
  namespace: production-apps
spec:
  hard:
    requests.cpu: "10"
    requests.memory: 20Gi
    limits.cpu: "20"
    limits.memory: 40Gi
    pods: "50"

Pod Disruption Budgets

Ensure availability during updates and maintenance:

apiVersion: policy/v1
kind: PodDisruptionBudget
metadata:
  name: web-app-pdb
spec:
  minAvailable: 2
  selector:
    matchLabels:
      app: web-app

Horizontal Pod Autoscaler

Configure automatic scaling based on metrics:

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: web-app-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-app
  minReplicas: 3
  maxReplicas: 50
  metrics:
  - type: Resource
    resource:
      name: cpu
      target:
        type: Utilization
        averageUtilization: 70
  - type: Resource
    resource:
      name: memory
      target:
        type: Utilization
        averageUtilization: 80

4. Monitoring and Observability

Comprehensive Monitoring Stack

Deploy a monitoring solution with Prometheus and Grafana:

apiVersion: v1
kind: ConfigMap
metadata:
  name: prometheus-config
data:
  prometheus.yml: |
    global:
      scrape_interval: 15s
    scrape_configs:
    - job_name: 'kubernetes-pods'
      kubernetes_sd_configs:
      - role: pod
      relabel_configs:
      - source_labels: [__meta_kubernetes_pod_annotation_prometheus_io_scrape]
        action: keep
        regex: true

Centralized Logging

Implement centralized logging with proper retention policies:

apiVersion: logging.coreos.com/v1
kind: ClusterLogForwarder
metadata:
  name: instance
spec:
  outputs:
  - name: azure-storage
    type: azureMonitor
    azureMonitor:
      customerId: "${WORKSPACE_ID}"
      sharedKey: "${WORKSPACE_KEY}"
  pipelines:
  - name: application-logs
    inputRefs:
    - application
    outputRefs:
    - azure-storage

5. Backup and Disaster Recovery

Velero Backup Configuration

Set up automated backups with Velero:

apiVersion: velero.io/v1
kind: Schedule
metadata:
  name: daily-backup
spec:
  schedule: "0 2 * * *"
  template:
    includedNamespaces:
    - production-apps
    - monitoring
    storageLocation: azure-backup-location
    ttl: 720h0m0s  # 30 days

Database Backups

For stateful workloads, implement application-consistent backups:

apiVersion: batch/v1
kind: CronJob
metadata:
  name: postgres-backup
spec:
  schedule: "0 3 * * *"
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: postgres-backup
            image: postgres:14
            command:
            - /bin/bash
            - -c
            - |
              pg_dump -h postgres-service -U $POSTGRES_USER $POSTGRES_DB | \
              gzip > /backup/backup-$(date +%Y%m%d_%H%M%S).sql.gz
            env:
            - name: POSTGRES_USER
              valueFrom:
                secretKeyRef:
                  name: postgres-secret
                  key: username

6. GitOps and Deployment Strategies

ArgoCD for GitOps

Implement GitOps for declarative deployments:

apiVersion: argoproj.io/v1alpha1
kind: Application
metadata:
  name: production-app
  namespace: argocd
spec:
  project: default
  source:
    repoURL: https://github.com/yourorg/k8s-manifests
    targetRevision: main
    path: production
  destination:
    server: https://kubernetes.default.svc
    namespace: production-apps
  syncPolicy:
    automated:
      prune: true
      selfHeal: true

Blue-Green Deployments

Use Argo Rollouts for advanced deployment strategies:

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: web-app
spec:
  replicas: 10
  strategy:
    blueGreen:
      activeService: web-app-active
      previewService: web-app-preview
      autoPromotionEnabled: false
      scaleDownDelaySeconds: 30
      prePromotionAnalysis:
        templates:
        - templateName: success-rate
        args:
        - name: service-name
          value: web-app-preview
  selector:
    matchLabels:
      app: web-app

7. Performance and Cost Optimization

Vertical Pod Autoscaler

Right-size your pods automatically:

apiVersion: autoscaling.k8s.io/v1
kind: VerticalPodAutoscaler
metadata:
  name: web-app-vpa
spec:
  targetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: web-app
  updatePolicy:
    updateMode: "Auto"
  resourcePolicy:
    containerPolicies:
    - containerName: web-app
      maxAllowed:
        cpu: 2
        memory: 4Gi

Cluster Autoscaler

Configure cluster-level autoscaling:

apiVersion: v1
kind: ConfigMap
metadata:
  name: cluster-autoscaler-status
  namespace: kube-system
data:
  nodes.max: "100"
  nodes.min: "3"
  scale-down-delay-after-add: "10m"
  scale-down-unneeded-time: "10m"

Production Readiness Checklist

Before going live, verify:

[ ] Multi-zone cluster deployment
[ ] RBAC properly configured
[ ] Network policies implemented
[ ] Resource quotas and limits set
[ ] Pod disruption budgets configured
[ ] Monitoring and alerting operational
[ ] Centralized logging implemented
[ ] Backup strategy tested
[ ] Disaster recovery plan documented
[ ] Security scanning integrated
[ ] Performance testing completed
[ ] Runbooks created for common scenarios

Conclusion

Production-ready Kubernetes requires attention to detail across multiple domains. The key is to implement these practices incrementally and test thoroughly. Remember, production readiness isn't a destination—it's an ongoing process of improvement and refinement.

Start with the fundamentals (HA, security, monitoring) and gradually add more sophisticated features as your team's expertise grows. The investment in proper setup pays dividends in operational stability and team confidence.

Have you faced challenges getting Kubernetes production-ready? What practices have worked best in your environment? Connect with me on LinkedIn to share your experiences.