Running Kubernetes in production requires careful attention to security, reliability, and operational practices that tutorials and documentation often gloss over. This guide covers the practical considerations for production Kubernetes deployments across major cloud providers.
Cluster Architecture Decisions
The first production decision involves managed versus self-managed Kubernetes. Each approach has valid use cases, but most organizations benefit from managed services.
Managed Kubernetes Services
EKS, AKS, and GKE handle control plane management, upgrades, and availability. You focus on deploying and managing workloads rather than cluster infrastructure.
Control plane costs vary significantly. GKE Autopilot includes control plane costs in node pricing. EKS charges $0.10/hour per cluster. AKS provides free control planes with paid uptime SLA options.
Managed services handle Kubernetes version upgrades with varying automation levels. GKE offers fully automated upgrades. EKS and AKS provide more manual control.
Self-Managed Considerations
Self-managed Kubernetes makes sense for specific requirements: air-gapped environments, specific version requirements, or specialized hardware integration.
Budget for ongoing operational effort. Control plane high availability, etcd management, and security patching require dedicated expertise.
Tools like kubeadm, Kubespray, and Rancher simplify self-managed deployments but don’t eliminate operational complexity.
Multi-Cluster Strategies
Production environments often span multiple clusters. Development, staging, and production typically run separate clusters for isolation.
Multi-region deployments may use clusters per region with federation or service mesh for cross-cluster communication.
Right-sizing cluster count balances isolation benefits against management overhead. More clusters mean more operational burden.
Node Pool Design
Node configuration significantly impacts cost, performance, and reliability. Thoughtful node pool design supports diverse workload requirements.
Instance Type Selection
General purpose instances suit most workloads. Reserve compute-optimized or memory-optimized instances for specific requirements.
Burstable instances work for development and variable workloads. Production workloads typically need consistent capacity.
ARM-based instances offer cost savings for compatible workloads. Container images need multi-architecture builds.
Spot and Preemptible Nodes
Spot nodes dramatically reduce costs for fault-tolerant workloads. Batch jobs, stateless services, and CI/CD work well on spot capacity.
Configure node pools with spot and on-demand mix. Critical workloads schedule to on-demand nodes. Spot handles overflow and batch processing.
Kubernetes handles spot interruption through pod disruption budgets and graceful termination. Configure adequate disruption budgets for production services.
Auto-Scaling Configuration
Cluster autoscaler adds and removes nodes based on pending pods and utilization. Configure scaling limits to prevent runaway costs.
Scale-down delays prevent thrashing during variable load. Default 10-minute delay works for most workloads. Reduce for faster cost optimization, increase for stability.
Consider separate node pools with different scaling behaviors. Batch workloads might scale aggressively while production pools scale conservatively.
Security Hardening
Default Kubernetes configurations prioritize ease of use over security. Production clusters require explicit security measures.
RBAC Configuration
Role-based access control restricts who can do what within the cluster. Default installations often grant excessive permissions.
Follow least privilege principles. Developers need deployment permissions in their namespaces, not cluster-admin everywhere.
Use service accounts for automated processes. Never share credentials between humans and automation.
Audit RBAC regularly. Tools like rbac-manager and kubectl-who-can help identify excessive permissions.
Pod Security Standards
Pod Security Standards replaced Pod Security Policies in Kubernetes 1.25. Enforce standards at namespace level using admission controllers.
Three levels exist: Privileged allows anything, Baseline blocks known privilege escalations, Restricted enforces best practices.
Start with Baseline for existing workloads. Move toward Restricted as applications are modernized.
Warn mode helps identify violations before enforcement. Apply warn labels to namespaces, review logs, then enforce.
Network Policies
Default Kubernetes networking allows all pod-to-pod communication. Network policies restrict traffic to explicit allow rules.
Start with default deny policies. Allow specific communication paths as needed. This approach catches unexpected dependencies.
Network policies require a CNI that supports them. Calico, Cilium, and cloud provider CNIs generally support network policies.
Test policies thoroughly. Incorrectly configured policies break applications in subtle ways.
Secrets Management
Kubernetes secrets are base64 encoded, not encrypted at rest by default. Enable encryption at rest for production clusters.
Consider external secrets managers. HashiCorp Vault, AWS Secrets Manager, and Azure Key Vault integrate with Kubernetes through operators.
External secrets management provides audit trails, rotation, and centralized policy enforcement beyond native Kubernetes capabilities.
Observability Stack
Production clusters require comprehensive monitoring, logging, and tracing. Cloud providers offer managed solutions alongside self-managed options.
Metrics Collection
Prometheus remains the standard for Kubernetes metrics. Cloud-managed options include Amazon Managed Prometheus, Azure Monitor, and GCP Managed Prometheus.
Configure appropriate retention periods. High-resolution metrics for recent data, downsampled historical data for capacity planning.
Alert on meaningful conditions. Alert fatigue from excessive notifications leads to ignored pages.
Log Aggregation
Container logs require aggregation for practical use. Fluentd, Fluent Bit, and Vector collect and forward logs to central systems.
Cloud-native options include CloudWatch Logs, Azure Monitor Logs, and Cloud Logging. Third-party options like Datadog, Splunk, and Elastic provide additional analysis capabilities.
Structure logs as JSON for easier parsing and querying. Application logging libraries should output structured formats.
Distributed Tracing
Tracing tracks requests across service boundaries. Essential for debugging microservices architectures.
OpenTelemetry provides vendor-neutral instrumentation. Applications instrument once, export to any supported backend.
Jaeger and Zipkin offer open-source tracing backends. Cloud providers offer managed tracing through X-Ray, Azure Monitor, and Cloud Trace.
Deployment Strategies
How you deploy changes affects availability and risk. Kubernetes supports multiple deployment strategies with different trade-offs.
Rolling Updates
Rolling updates replace pods gradually while maintaining availability. The default strategy for most deployments.
Configure maxSurge and maxUnavailable to control rollout pace. Conservative settings prevent capacity gaps. Aggressive settings complete updates faster.
Readiness probes prevent traffic to pods before they’re ready. Health checks should verify actual functionality, not just process existence.
Blue-Green Deployments
Blue-green maintains two complete environments. Switch traffic instantly between versions. Rollback is equally instant.
Requires double capacity during transitions. Cost implications matter for large deployments.
Service mesh or ingress controller manages traffic shifting. Istio, Linkerd, and ingress-nginx support traffic splitting for blue-green.
Canary Releases
Canary releases direct small traffic percentages to new versions. Expand traffic as confidence grows. Detect problems before full rollout.
Argo Rollouts and Flagger automate canary analysis. Automatic promotion or rollback based on metrics.
Define success metrics before deployment. Error rates, latency percentiles, and business metrics inform promotion decisions.
Disaster Recovery
Production clusters need recovery plans for various failure scenarios. Plan and test recovery procedures before you need them.
Backup Strategies
Velero backs up Kubernetes resources and persistent volumes. Schedule regular backups to cloud object storage.
Test restores regularly. Untested backups might not work when needed. Include restore testing in operational runbooks.
Consider backup scope. Full cluster backups enable complete recovery. Namespace backups support more granular restoration.
Multi-Region Considerations
Multi-region deployments protect against regional failures. Active-active or active-passive configurations trade complexity for availability.
Data replication across regions adds latency and cost. Consider which data truly needs multi-region availability.
DNS-based failover routes traffic to healthy regions. Cloud provider traffic managers automate failover based on health checks.
Recovery Testing
Game days and chaos engineering test recovery procedures under controlled conditions. Better to discover problems during planned exercises than real incidents.
Start with tabletop exercises discussing hypothetical scenarios. Progress to actual failure injection as confidence grows.
Document lessons learned. Update runbooks based on exercise findings.
Cost Management
Kubernetes abstracts infrastructure in ways that complicate cost visibility. Explicit cost management practices prevent surprise bills.
Resource Requests and Limits
Resource requests reserve capacity on nodes. Limits cap actual usage. Both affect scheduling and costs.
Requests that are too high waste capacity. Requests that are too low lead to resource contention and scheduling problems.
Use vertical pod autoscaler to recommend appropriate requests based on actual usage.
Cost Allocation
Kubernetes cost allocation attributes cluster costs to teams, applications, or projects. Labels enable allocation reporting.
Kubecost, CloudHealth, and cloud-native tools provide Kubernetes cost visibility. Without explicit tooling, container costs hide in general compute charges.
Chargeback or showback models create accountability. Teams seeing their costs make more informed decisions.
Rightsizing Opportunities
Review resource utilization regularly. Over-provisioned pods waste money. Under-provisioned pods risk performance problems.
Horizontal pod autoscaling adjusts replica counts based on demand. Vertical pod autoscaling adjusts resource requests.
Both approaches complement each other. Many workloads benefit from horizontal scaling for demand variation and vertical optimization for base efficiency.
Moving Forward
Production Kubernetes requires ongoing attention. Security patches, version upgrades, and cost optimization need continuous focus.
Start with fundamentals: security hardening, basic observability, and tested backup procedures. Add sophistication over time.
Build operational runbooks for common scenarios. Clear procedures help during incidents when stress impairs thinking.
Invest in team training. Kubernetes expertise enables better decisions and faster incident response.
Production Kubernetes is a journey of continuous improvement. Each incident and optimization exercise builds organizational capability.
