Moving Kubernetes workloads between cloud providers is not simply a matter of copying YAML files from one cluster to another. A deployment that works on Amazon Elastic Kubernetes Service (EKS) can behave differently on Google Kubernetes Engine (GKE) because the surrounding infrastructure, identity model, networking configuration, storage integrations, and operational assumptions may differ.

The challenge becomes more significant when a migration includes dozens of services, persistent volumes, ingress rules, monitoring agents, and automated deployment pipelines. A manifest can remain valid Kubernetes YAML while still depending on an AWS-specific component that does not exist in the target environment.

Google Cloud's EKS-to-GKE migration tooling addresses this problem by helping teams analyze existing Kubernetes workloads, identify migration requirements, and validate changes as they prepare to move to GKE. Guardrails are important because an automated migration agent should not blindly translate every resource and assume the result is production-ready.

For platform engineers, the key is to use migration automation to accelerate repetitive work while retaining explicit validation for security, compatibility, reliability, and application behavior.

Why EKS-to-GKE Migration Requires More Than Manifest Conversion

Kubernetes provides a common API for deploying and managing containerized workloads. However, managed Kubernetes services also integrate with their respective cloud platforms.

EKS and GKE differ in areas such as cloud identity, load balancing, persistent storage, networking, and operational tooling. Those differences can affect application availability even when the application containers remain unchanged.

Consider a workload that uses a Kubernetes Deployment, a Service, and an Ingress resource. The Deployment might transfer with little modification, but the Ingress configuration could depend on an AWS-specific controller or annotations. The target GKE cluster may require a different controller, a different set of annotations, or a different networking design.

Common migration challenges include:

A migration tool can help identify and transform these dependencies, but it cannot safely infer every application-specific requirement from Kubernetes manifests alone.

How Migration Automation Fits Into the Process

An AI-assisted migration workflow can reduce manual analysis by examining existing resources, identifying likely incompatibilities, proposing changes, and helping validate the target configuration.

A practical migration process separates the work into distinct stages.

<box gap={1} padding={3} border radius="lg"> <box background="surface-secondary" radius="md" padding={3} gap={1}> 1. Discover

<text size="sm">Inventory namespaces, workloads, services, storage, controllers, policies, and cloud-specific dependencies.</text>

</box> <row justify="center"> <icon name="arrow-down" size="lg" color="secondary" /> </row> <box background="surface-secondary" radius="md" padding={3} gap={1}> 2. Analyze

<text size="sm">Identify unsupported resources, provider-specific configuration, and dependencies that require redesign.</text>

</box> <row justify="center"> <icon name="arrow-down" size="lg" color="secondary" /> </row> <box background="surface-secondary" radius="md" padding={3} gap={1}> 3. Transform

<text size="sm">Generate proposed GKE-compatible manifests and infrastructure changes while preserving application intent.</text>

</box> <row justify="center"> <icon name="arrow-down" size="lg" color="secondary" /> </row> <box background="surface-secondary" radius="md" padding={3} gap={1}> 4. Validate

<text size="sm">Check schemas, policies, permissions, infrastructure dependencies, and workload behavior.</text>

</box> <row justify="center"> <icon name="arrow-down" size="lg" color="secondary" /> </row> <box background="surface" border radius="md" padding={3} gap={1}> 5. Deploy gradually

<text size="sm">Test representative workloads, shift traffic in controlled stages, and retain a rollback path.</text>

</box> </box>

These stages are a recommended engineering workflow, not a claim that every migration agent automatically performs each action. Teams should verify the actual capabilities and supported resource types of their selected Google Cloud migration tooling.

The most important distinction is between a generated configuration and a validated migration. A generated manifest is a proposed change. It should not be treated as proof that the workload will behave correctly on GKE.

Identify AWS-Specific Kubernetes Dependencies

Before transforming manifests, establish which parts of the application are portable and which depend on EKS or AWS services.

Start by inspecting the resources stored in source control and the live cluster. Useful targets include Deployments, StatefulSets, Services, Ingresses, PersistentVolumeClaims, StorageClasses, service accounts, custom resources, and admission policies.

Search for AWS-specific references in manifests and deployment scripts:

grep -RniE \
  'eks.amazonaws.com|alb.ingress.kubernetes.io|ebs.csi.aws.com|aws-load-balancer' \
  ./kubernetes/

This is a discovery aid rather than a complete compatibility checker. It will not find every provider dependency, particularly those hidden in Helm charts, generated manifests, container configuration, or application code.

Review the results in context.

For example, a service account annotation that associates a Kubernetes identity with an AWS IAM role cannot simply be copied to GKE. The target configuration needs an appropriate Google Cloud identity and permission model.

Likewise, an AWS Load Balancer Controller annotation should not be removed without replacing the behavior it provides. The target ingress configuration must preserve routing, TLS termination, health checks, and any other requirements used by the application.

Document each dependency, its replacement, and the validation needed before proceeding.

Use Guardrails to Review Generated Changes

Guardrails reduce the risk of accepting an incorrect or unsafe migration proposal. They should operate at multiple levels rather than relying on a single manifest validation command.

Schema and API compatibility

First, verify that the target cluster supports the Kubernetes API versions and resource types in the generated manifests.

A configuration can fail because it uses a removed API version, references a missing custom resource definition, or relies on a controller that is not installed in GKE.

Use Kubernetes-aware validation tools and server-side dry runs against a representative target cluster where possible. A successful YAML parse is not sufficient because it only establishes that the document is syntactically valid.

Security and identity

Inspect service accounts, RBAC rules, security contexts, secret references, and workload permissions.

A migration should not compensate for an identity mismatch by granting broad project-level permissions. Map each workload to the minimum Google Cloud permissions it requires, and validate access to the specific services it uses.

Also review container privileges, host access, network policies, and secret-delivery mechanisms. Changes to these settings can introduce security risks even when the application starts successfully.

Infrastructure dependencies

Validate that required storage classes, ingress controllers, DNS configuration, certificates, and network connectivity exist in the target environment.

A manifest may reference a StorageClass that is absent from the GKE cluster. A Service may depend on load-balancer behavior that differs from its EKS implementation. A readiness probe may succeed while the application still cannot reach its database or external API.

Guardrails should check both Kubernetes resources and the infrastructure those resources depend on.

Application behavior

Finally, validate the workload itself. Kubernetes can accept a Deployment without proving that the application can serve requests, process messages, or preserve data correctly.

Run integration tests, inspect logs, verify health endpoints, and exercise representative traffic. For stateful workloads, include backup, restore, replication, and consistency checks appropriate to the application.

Validate GKE Manifests Before Deployment

A useful guardrail is to run static and cluster-aware checks before allowing a generated manifest into the deployment pipeline.

For example, suppose a migration produces a Deployment for a stateless application. You can validate the manifest with a Kubernetes-aware tool such as kubectl and a dry run against the target cluster:

kubectl apply \
  --dry-run=server \
  -f ./gke-manifests/

A server-side dry run asks the Kubernetes API server to validate the requested changes without persisting them. It can reveal schema, admission-policy, and cluster-specific validation failures.

It does not verify that the application will start, that external dependencies are reachable, or that the final service will receive traffic correctly.

For that reason, a robust pipeline combines several checks:

  1. Parse and validate the manifests.

  2. Check security policies and required fields.

  3. Verify that referenced resources and controllers exist.

  4. Run server-side dry runs against the target cluster.

  5. Deploy to a nonproduction environment.

  6. Run smoke tests and application-specific integration tests.

  7. Require approval for high-risk changes before production deployment.

These checks should be adapted to the actual migration. A stateless API and a database-backed StatefulSet need different acceptance criteria.

Example: Replace an EKS-Specific Load Balancer Configuration

Consider a Kubernetes Ingress that uses AWS Load Balancer Controller annotations:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: orders-api
  annotations:
    alb.ingress.kubernetes.io/scheme: internet-facing
spec:
  ingressClassName: alb
  rules:
    - host: orders.example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: orders-api
                port:
                  number: 8080

The manifest expresses an application routing requirement, but its controller and annotation are specific to the AWS implementation.

Copying it directly to GKE does not guarantee that the target cluster will create the equivalent load balancer. The target needs a supported ingress implementation and an explicit mapping for exposure, TLS, routing, and health checks.

For a basic GKE configuration using a compatible Ingress controller, the provider-specific annotation can be removed and the ingress class changed to match the installed controller. The following example illustrates the structural change; the actual class and TLS configuration must match the target environment.

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: orders-api
spec:
  ingressClassName: nginx
  rules:
    - host: orders.example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: orders-api
                port:
                  number: 8080

This example assumes that an appropriate NGINX Ingress controller is installed and configured on the target cluster. It is not a universal replacement for AWS Application Load Balancer behavior.

If the target design uses GKE's native load-balancing integration, use the corresponding supported configuration instead. Then validate TLS certificates, public or internal exposure, health checks, firewall rules, and DNS behavior.

This illustrates why migration guardrails should validate semantic equivalence, not just whether the transformed YAML is accepted.

Handle Persistent Storage as a Separate Migration Workstream

Persistent storage deserves special attention because changing a StorageClass or volume provisioner does not move the data stored on the original volume.

An EKS workload might use the Amazon EBS CSI driver and an EBS-backed StorageClass. The corresponding GKE workload may use a Persistent Disk or another supported storage option, depending on its performance, availability, and access requirements.

Before changing the storage configuration, determine:

A safer migration may involve provisioning target storage, copying data, validating application-level consistency, and coordinating a controlled cutover.

For databases and other stateful systems, consider native replication or a purpose-built migration strategy rather than treating the task as a Kubernetes manifest conversion.

Roll Out the Migration in Controlled Stages

Once the generated manifests pass validation, deploy representative workloads to a nonproduction GKE cluster.

Start with services that have limited dependencies and straightforward recovery procedures. Use the results to refine the migration rules before moving more complex workloads.

For production cutover, define measurable acceptance criteria, such as:

If both clusters run simultaneously during migration, ensure that the architecture supports the resulting traffic and data flow. Running two copies of a stateless API may be straightforward; running two independently writable copies of a stateful service can create consistency problems.

Avoid making irreversible changes to the source environment until the target has met the migration criteria and the team has approved the cutover.

Common Migration Mistakes

Assuming valid YAML means a successful migration: Kubernetes schema validation cannot prove application compatibility. Include runtime tests and dependency checks.

Removing provider-specific annotations without replacing their behavior: An annotation may control security, exposure, or routing. Identify the behavior it implements before removing it.

Copying IAM assumptions into GKE: Map each workload to the target identity model and validate least-privilege access.

Changing storage definitions without moving data: Provisioning a new volume does not migrate the contents of an existing volume. Plan data transfer and consistency verification separately.

Allowing generated changes to reach production automatically: Require policy checks, test results, and human approval for changes with substantial security or availability impact.

Ignoring observability during cutover: A workload that appears healthy in Kubernetes may still be failing requests or losing messages. Validate application-level signals, not only Pod status.

Summary

Migrating workloads from EKS to GKE requires understanding both Kubernetes resources and the cloud-specific systems surrounding them. AI-assisted migration tooling can help discover dependencies, propose configuration changes, and reduce repetitive engineering work, while guardrails provide the checks needed to prevent unsafe changes from reaching production.

The most effective approach combines dependency discovery, controlled manifest transformation, security and infrastructure validation, application testing, and staged deployment. Treat generated configurations as proposals, validate the target behavior, and maintain a tested rollback path.

A successful migration is not one in which every manifest has been converted. It is one in which workloads operate reliably on GKE with the required security, networking, storage, observability, and recovery guarantees intact.