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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11To deploy a microservice application on Google Kubernetes Engine (GKE), create a Google Cloud project, enable billing and the required APIs, provision a cluster, connect kubectl, apply Kubernetes resources that reference your container images, and expose only the services that need external access. This walkthrough uses Google’s Autopilot quickstart pattern for the cluster and distinguishes its single-container smoke test from a genuine multi-service deployment.
What you need before creating a cluster
Google Cloud’s GKE quickstart is aimed at operators and developers who provision cloud resources and deploy applications. For this learning deployment, prepare:
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- A Google Cloud project selected or created for the work.
- Billing enabled on that project.
- The GKE and Artifact Registry APIs enabled where required by your workflow.
- An identity with permission to create clusters and related resources, access container images, and deploy workloads.
- A terminal with the Google Cloud CLI and
kubectl. Google’s documented quickstart uses Cloud Shell, which includes both.
Before running commands, verify that the CLI is targeting the intended project. A cluster and its connected resources are billed to the project, and later kubectl commands act on the current context.
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Choose Autopilot or Standard
GKE offers two cluster modes. Google recommends Autopilot for most production use cases; it manages more of the cluster configuration and resource provisioning. Standard is the alternative when a team needs more direct control over node configuration and operations. Workload requirements, operational capacity, and network design should determine the choice rather than the fact that one mode is simpler for a tutorial.
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| Mode | What to weigh |
|---|---|
| Autopilot | Google manages more configuration and resource provisioning. Google recommends it for most production use cases. |
| Standard | Offers a more directly managed node and cluster configuration path for teams that need that control. |
The sample below creates an Autopilot cluster in us-central1; use a location that fits your latency, availability, compliance, and cost requirements instead of treating the example region as universal. Google cautions that “A production deployment of your own applications requires more careful IP address planning.”
Create the cluster and configure kubectl
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Confirm the active project:
gcloud config get-value projectIf it is not the project you intend to use, select the correct one before continuing:
gcloud config set project PROJECT_ID -
Create an Autopilot cluster. This follows Google’s documented quickstart example:
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Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.gcloud container clusters create-auto hello-cluster --location=us-central1Cluster creation takes time. For a different cluster name or location, use those same values in the next command.
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Fetch credentials so
kubectlcan communicate with the cluster:gcloud container clusters get-credentials hello-cluster --location=us-central1 -
Check the active context before applying resources:
kubectl config current-contextThis is a useful safeguard if you work with multiple clusters; deployment commands apply to the currently selected context.
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Deploy a smoke test or a real microservice application
Smoke-test the cluster with one container
Google’s quickstart creates a single Deployment using the versioned hello-app:1.0 sample image in Artifact Registry:
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kubectl create deployment hello-app --image=us-docker.pkg.dev/google-samples/containers/gke/hello-app:1.0A Deployment manages the desired state of the application workload; Kubernetes schedules a Pod to run the specified container image. This is a practical check that the cluster can run a workload, but it is not a multi-service application.
Deploy multiple services from manifests
A real microservice application needs a container image and Kubernetes configuration for each component. Build and push each service’s image to Artifact Registry, then make sure the image references in your manifests match the repository, image name, and tag you pushed. Apply the manifests to create the application’s Deployments, Pods, and Services together:
kubectl apply -f PATH_TO_MANIFESTSGoogle’s Cymbal Books tutorial is an example of this pattern: it uses Artifact Registry images and Kubernetes manifests for Services, a Deployment, and Pods. Within the cluster, a service can address another service using its Kubernetes Service name. Configure each component to listen on the expected port and use the correct service name and port for its dependencies; a container image alone does not define how the services communicate.
Keep application-specific configuration and secrets appropriate to your environment. A tutorial’s sample manifests are a starting point, not a substitute for reviewing access, networking, and operational requirements before production use.
Expose the application and verify it
Pods run inside the cluster; a Kubernetes Service supplies a stable network endpoint for reaching selected Pods. For the quickstart’s public test endpoint, expose the Deployment with a LoadBalancer Service:
kubectl expose deployment hello-app --type=LoadBalancer --port 80 --target-port 8080This maps external port 80 to application port 8080 and provisions a Compute Engine load balancer. Check that the Pod is ready and inspect the Service address:
kubectl get podskubectl get service hello-appThe external IP can remain pending for several minutes while networking resources are provisioned. Once an address appears, open it in a browser using HTTP. For a multi-service app, expose only the component that needs to receive external traffic; internal services can communicate through their Kubernetes Service names without each receiving a public endpoint. Production exposure should follow the application’s security and ingress requirements rather than defaulting every service to a public load balancer.
Estimate costs before leaving the cluster running
Google Cloud’s GKE pricing page, accessed in 2026, lists a management fee of $0.10 per cluster per hour across cluster modes and topologies. It also lists a $74.40 monthly GKE free-tier credit per billing account, described as equivalent to one Autopilot or zonal Standard cluster per month. That credit does not cover every charge category, including compute charges, and does not cover the cluster management fee for regional clusters.
These figures are pricing-page details, not a guaranteed total for this walkthrough. VM or other compute charges may apply, and the LoadBalancer Service adds load-balancer billing. Actual cost depends on the cluster mode and location, workload resources and duration, network resources, and other services. Check Google Cloud’s current GKE pricing page and pricing calculator for an estimate that matches your configuration.
Delete the learning resources
When finished, remove the public Service first so its associated load balancer is deleted, then delete the cluster. Use the same cluster name and location created above:
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Delete the Service:
kubectl delete service hello-app -
Delete the cluster:
gcloud container clusters delete hello-cluster --location=us-central1 -
Check the project for any resources you created that remain and could continue generating charges. If the project was created solely for this learning run, deleting that dedicated project is another cleanup option.
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When GKE is the right deployment target
GKE is a fit when an application benefits from Kubernetes orchestration, specific resource control, stateful services, or a more complex microservice architecture. For stateless request- or event-driven workloads, Cloud Run may offer a simpler operating model with pay-per-use pricing. The decision depends on the workload, the infrastructure control the team needs, its scaling model, and its preferred pricing structure; neither platform is the universal choice.
Choose a provisioning workflow
| Workflow | Best suited to | Trade-off |
|---|---|---|
| Google Cloud console | People who prefer guided, visual setup and inspection. | Useful for learning, but manual steps are harder to reproduce consistently. |
| Google Cloud CLI | A concise walkthrough or direct command-driven provisioning. | Fast to repeat when documented, but commands still depend on the active project and context. |
| Terraform | Teams that want infrastructure described and managed as code. | Supports repeatable configuration, but requires maintaining infrastructure code and its lifecycle. |
Google documents console and CLI workflows, and provides Terraform guidance for repeatable infrastructure management. Select the workflow that matches how the cluster will be reviewed, changed, and maintained after the initial deployment.
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