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Your container image and application code may move to Amazon EKS unchanged, but the platform around them usually needs deliberate work. Networking controllers, load balancers, storage, IAM, secrets, monitoring, and traffic routing all depend on how the destination cluster is configured. Treat the move as a staged platform migration—not as proof that existing Kubernetes manifests will work unchanged in AWS.
What “unchanged” does—and doesn’t—mean
If the application’s runtime assumptions are satisfied, the same image and application code can run on EKS. Kubernetes resources such as Deployments and Services still describe workloads and service discovery, but the controllers that interpret those resources and connect them to cloud infrastructure may differ.
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For example, a Kubernetes Service can provide stable discovery for Pods inside the cluster. External access through an Ingress depends on an ingress controller, while a Service of type LoadBalancer depends on cloud integration that provisions a load balancer. Those integrations, along with storage and identity, are part of the environment—not the application binary.
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What to decide about networking and load balancers
Inventory each external endpoint and the Kubernetes resource that creates it. A source cluster’s ingress controller or cloud provider integration may not exist on EKS, and annotations that worked with one controller may not be supported by another.
| Need | Common EKS fit | What to verify |
|---|---|---|
| HTTP or HTTPS routing using Ingress rules | Application Load Balancer (ALB), Layer 7 | Ingress controller ownership, supported annotations, routing rules, certificates, and the intended AWS Load Balancer Controller or EKS Auto Mode configuration. |
TCP or UDP exposure through a Service of type LoadBalancer |
Network Load Balancer (NLB), Layer 4 | Protocol, source-IP requirements, service annotations, and whether clients require a static NLB IP rather than a DNS name. |
AWS recommends the AWS Load Balancer Controller for reconciling EKS Service and Ingress resources with AWS load balancers. Its documentation distinguishes that controller’s behavior from legacy cloud-provider behavior. Establish which controller owns each resource before deploying: conflicting or ambiguous ownership can leave the wrong controller responsible for provisioning or reconciling infrastructure.
EKS Auto Mode is a separate target choice, not merely a different name for the self-managed controller. Its load-balancing configuration and supported annotations differ. AWS documents that load balancers managed by a self-managed AWS Load Balancer Controller cannot be migrated to EKS Auto Mode management. Decide on the target mode and resource ownership before recreating or cutting over those resources, and check current AWS documentation for supported configuration.
What to check for persistent storage
A workload that starts successfully can still fail when it needs a persistent volume. Compare the source provisioner and StorageClasses with what the destination cluster supports, and test provisioning and persistence before moving production traffic.
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| Storage concern | Migration check |
|---|---|
| Provisioner compatibility | Confirm the target CSI driver and StorageClass. AWS gives kubernetes.io/aws-ebs to ebs.csi.aws.com as an example of converting an old EBS provisioner name; that example is not a universal replacement for every storage backend. |
| Volume creation | Install and configure the required driver, create required StorageClasses, and test dynamic provisioning in a representative environment. |
| Existing data | Plan how persistent-volume data will be copied or restored and verify application consistency. Recreating a claim or changing a provisioner does not itself move the data. |
| Recovery | Set up and test a backup solution appropriate to the workload before cutover. |
Do not assume that a manifest referencing a volume means the data is portable. Validate the actual storage class, access modes, mount behavior, data transfer, and recovery procedure with the application.
How identity, secrets, and operations change
Cloud permissions are another destination dependency. Review how workloads obtain AWS permissions and whether the target IAM configuration is ready; AWS’s EKS pre-migration checklist specifically calls out IAM roles for service accounts. Avoid treating a successful Kubernetes deployment as proof that a Pod has the AWS permissions it needs.
Secrets require separate handling. AWS’s migration procedure describes snapshots that include Secret metadata but not secret values, so destination values must be populated using an appropriate secrets mechanism. Confirm that the target application can retrieve the right values without exposing them in migration artifacts or logs.
Monitoring and logging also need to be in place before the move is considered complete. Recreate or adapt the collection and alerting needed to observe workload health, resource use, and failures in the destination environment. The AWS checklist treats monitoring, logging, security, and secrets as distinct pre-migration workstreams rather than incidental consequences of deploying workloads.
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A staged migration and validation sequence
AWS Prescriptive Guidance presents migration as extraction followed by transformation and deployment, with validation and post-migration tasks. Its example sequences cluster resources in phases; use it as an approach to adapt, not as a guarantee that a tool can translate every cluster automatically.
- Inventory and prepare the target. Record source Kubernetes version, manifests, namespaces, RBAC, ConfigMaps, Secret metadata, storage resources, CRDs and their controllers, Helm releases, policies, and network dependencies. Choose the EKS operating mode and establish target networking, IAM, storage, monitoring, and secrets arrangements.
- Check compatibility before deployment. Compare source and target Kubernetes versions and inspect for deprecated APIs. AWS recommends testing application behavior against a new Kubernetes version before an upgrade; a migration alone is not necessarily a version upgrade, but the version difference still needs review.
- Extract and review the resources. Use the chosen migration procedure to capture the source resources. Review dry-run output before making live changes, paying particular attention to resource transformations, cloud-specific annotations, storage references, and secrets handling.
- Deploy in dependency-aware phases. The AWS example handles namespaces, RBAC, storage, configuration, CRDs, workloads, networking, and Helm releases in sequence. Adapt the ordering to your controllers and dependencies; ensure a CRD’s controller and required infrastructure are available when the dependent workload is introduced.
- Validate the destination before cutover. Check that expected resources and CRDs exist, controllers are reconciling them, load balancers and Ingresses have the intended configuration, and application connectivity works. Run workload-specific acceptance tests: infrastructure checks alone do not establish that business behavior is correct.
- Shift traffic and observe. Update DNS or other traffic routing only after validation. For critical workloads, consider a gradual traffic shift rather than changing every client’s destination at once, then observe production behavior as traffic moves.
The AWS procedure also describes reviewing dry-run results and resuming from a phase after problems are corrected. If deployment fails, fix the underlying dependency or transformation issue, then resume from an appropriate phase rather than assuming a fresh full run is always necessary.
Pre-cutover checks for the application team
- Can each workload start with the target cluster’s Kubernetes version and APIs?
- Are the intended ingress and load-balancer controllers installed, and is ownership unambiguous?
- Do DNS names, routing rules, certificates, and external connectivity point to the intended destination?
- Can the application provision and mount the expected volumes, and is the required data present and recoverable?
- Are destination IAM permissions and secret values available to the workloads that need them?
- Do monitoring, logging, alerting, and workload-specific acceptance tests show the expected behavior?
- Is the traffic-shift and rollback approach understood by the people operating the cutover?
The exact edits depend on the source distribution and version, installed controllers, storage backend, IAM model, network design, and chosen EKS mode. AWS’s pre-migration checklist and Auto Mode documentation identify these as areas to assess, but they cannot determine the changes required by a particular cluster without those details.
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