This content originally appeared on DEV Community and was authored by Latchu@DevOps
In this tutorial, we’ll walk through how to create a Google Kubernetes Engine (GKE) Autopilot cluster, deploy a sample application, and understand how GKE Autopilot manages infrastructure for you.
Autopilot mode lets you focus entirely on your workloads — while Google handles the node provisioning, scaling, and security patching automatically.
🧩 Step 01: Introduction
We’ll perform the following tasks:
- Create a GKE Autopilot Cluster
- Understand how Autopilot works under the hood
- Deploy a sample NGINX app and test it
What is GKE Autopilot?
- A fully managed Kubernetes mode by Google.
- You don’t manage nodes — GKE provisions, scales, and maintains them.
- You only pay for running pods, not idle nodes.
- It enforces best practices for security, networking, and reliability.
⚙️ Step 02: Create a GKE Autopilot Cluster
Navigate to:
Kubernetes Engine → Clusters → CREATE
Then choose:
GKE Autopilot → CONFIGURE
Cluster Basics
| Setting | Value |
|---|---|
| Name | autopilot-cluster-public-1 |
| Region | us-central1 |
Leave the remaining fields as defaults.
Fleet Registration
✅ Leave to defaults.
Networking
| Setting | Value |
|---|---|
| Network | default |
| Node subnet | default |
| IPv4 Network Access | Public cluster |
| Access control plane using external IP | Checked (default) |
| Control plane IP range | 172.18.0.0/28 |
Advanced Settings
Leave all to defaults and click CREATE.
💻 Equivalent gcloud Command
gcloud container clusters create-auto "autopilot-cluster-public-1" \
--region "us-central1" \
--master-ipv4-cidr "172.18.0.0/28" \
--network "default" \
--subnetwork "default"
🧠 Step 03: Access the Autopilot Cluster from Cloud Shell
Once the cluster is created, configure your kubectl context.
# Configure kubeconfig
gcloud container clusters get-credentials autopilot-cluster-public-1 --region us-central1 --project gcp-zero-to-hero-468909
Verify Access
# List Nodes
kubectl get nodes
# List System Pods
kubectl get pods -n kube-system
✅ You should see your system pods and automatically managed nodes listed.
📝 Step 04: Review Kubernetes Manifests
We’ll deploy a simple NGINX app to test the cluster.
📄 Step 04-01: Deployment (01-kubernetes-deployment.yaml)
apiVersion: apps/v1
kind: Deployment
metadata:
name: myapp1-deployment
spec:
replicas: 5
selector:
matchLabels:
app: myapp1
template:
metadata:
labels:
app: myapp1
spec:
containers:
- name: myapp1-container
image: ghcr.io/stacksimplify/kubenginx:1.0.0
ports:
- containerPort: 80
resources:
requests:
memory: "128Mi"
cpu: "200m"
limits:
memory: "256Mi"
cpu: "400m"
📄 Step 04-02: Service (02-kubernetes-loadbalancer-service.yaml)
apiVersion: v1
kind: Service
metadata:
name: myapp1-lb-service
spec:
type: LoadBalancer
selector:
app: myapp1
ports:
- name: http
port: 80
targetPort: 80
🚀 Step 05: Deploy Kubernetes Manifests
# Deploy manifests
kubectl apply -f kube-manifests/
# List deployments
kubectl get deploy
# List pods
kubectl get pods
🕒 Observation:
- Pods may take 2–3 minutes to move from Pending → Running.
- GKE Autopilot automatically provisions nodes as needed.
Verify Services
kubectl get svc
Verify Nodes
kubectl get nodes
Observation:
- Nodes are dynamically created by GKE.
- You can’t see these nodes under Compute Engine → VM Instances.
- They’re fully managed by GKE.
🌐 Access the Application
Copy the External IP from the LoadBalancer service output and open in a browser:
http://<EXTERNAL-IP>
You should see the NGINX welcome page.
📈 Step 06: Scale the Application
Autopilot clusters scale seamlessly as demand grows.
# Scale the deployment
kubectl scale --replicas=15 deployment/myapp1-deployment
# Verify pods
kubectl get pods
# Verify nodes
kubectl get nodes
✅ You’ll notice that GKE automatically provisions additional nodes to meet the workload requirements.
🧹 Step 07: Clean-Up
When you’re done testing:
# Delete application resources
kubectl delete -f kube-manifests/
Verify Cluster Scaling Down
kubectl get nodes
Observation:
After 5–10 minutes, GKE will automatically de-provision unused nodes.
🎯 Summary
| Step | Description |
|---|---|
| 1 | Created an Autopilot GKE Cluster |
| 2 | Deployed a sample NGINX app |
| 3 | Observed automatic scaling and node management |
| 4 | Cleaned up resources and cluster |
🧠 Key Takeaways
- GKE Autopilot automates cluster operations, freeing you from node management.
- You only pay for running pods, making it cost-efficient.
- Resources like CPU/memory are automatically optimized.
- Scaling and node provisioning are completely managed by Google.
🌟 Thanks for reading! If this post added value, a like ❤️, follow, or share would encourage me to keep creating more content.
— Latchu | Senior DevOps & Cloud Engineer
☁️ AWS | GCP | ☸️ Kubernetes | 🔐 Security | ⚡ Automation
📌 Sharing hands-on guides, best practices & real-world cloud solutions
This content originally appeared on DEV Community and was authored by Latchu@DevOps
Latchu@DevOps | Sciencx (2025-10-12T05:27:00+00:00) Part-123: 🚀Create a GKE Autopilot Cluster and Deploy an Application. Retrieved from https://www.scien.cx/2025/10/12/part-123-%f0%9f%9a%80create-a-gke-autopilot-cluster-and-deploy-an-application/
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