Kubernetes Cluster Deployment Architecture Diagram

What this diagram shows

This diagram illustrates how code moves from source control through a continuous integration and continuous deployment pipeline into a running Kubernetes cluster. It shows the path from git repositories through build systems (typically Jenkins or GitLab CI), image registries (Docker Hub, ECR, or similar), and then deployment objects (Deployments, StatefulSets, DaemonSets) that orchestrate pods across cluster nodes. The diagram captures how the control plane manages desired state, how the scheduler places pods on nodes with sufficient resources, and how horizontal pod autoscalers respond to metrics by scaling replica counts up or down.

Key components

• Git Repository — Source of truth holding application code and deployment manifests that trigger the CI/CD pipeline.
• CI/CD Pipeline (Jenkins/GitLab CI) — Automated system that builds Docker images, runs tests, and pushes artifacts when code is committed.
• Container Registry — Central store for built Docker images that the cluster pulls from during pod creation.
• Kubernetes Control Plane — API server and scheduler that receive deployment requests and decide where to place pods based on resource requests and constraints.
• Worker Nodes — Physical or virtual machines running the kubelet agent that starts and manages pods assigned to them.
• Pods — Smallest deployable unit containing one or more containers running your application.
• Horizontal Pod Autoscaler (HPA) — Controller that monitors CPU or custom metrics and automatically adjusts replica counts to match demand.

When to use it

Use this diagram when documenting how your team delivers applications to Kubernetes or explaining the infrastructure to stakeholders unfamiliar with container orchestration. It is essential for runbooks, onboarding new engineers, architecture reviews, and justifying resource allocation decisions. This template works well for teams using GitOps, traditional CI/CD, or any automated deployment model where code changes flow predictably into a running cluster.

Common mistakes

• Omitting the container registry layer, which causes confusion about where images come from and how the cluster actually pulls them.
• Treating the control plane as a single monolithic box rather than showing the API server, scheduler, and controller manager as distinct components that each have a role in deployment.
• Ignoring resource requests and limits in pod specifications, which leads to diagrams that do not explain why pods fail to schedule or why nodes become overloaded.

Adapting it to your system

Replace the generic CI/CD pipeline label with your actual tools (Jenkins, Argo CD, Flux, CircleCI). Add your specific container registry endpoint. Include ingress controllers if external traffic reaches your pods. Layer in namespace separation if your cluster isolates teams or environments. Show persistent volume claims if your pods store state. Add security scanning steps between the build stage and registry if your organisation requires image scanning. Tailor autoscaling thresholds and metrics to match what you actually measure.