Migrating Existing Amazon EKS Clusters to EKS Auto Mode | Rafay

Why It Matters

Migrating to EKS Auto Mode reduces operational overhead and improves scaling efficiency, while requiring careful manual migration to avoid production disruption.

Key Takeaways

• AWS lacks automated app migration for EKS Auto Mode
• Rafay streamlines cluster‑level migration, not application steps
• Access entries and custom NodeClasses required for Auto Mode
• EBS CSI migration tool moves PVCs to gp3 StorageClass
• Legacy add‑ons must be removed after migration

Pulse Analysis

EKS Auto Mode represents Amazon’s push toward fully managed Kubernetes clusters, taking over node provisioning, scaling, patching, and add‑on lifecycle. By offloading these operational chores, enterprises can lower staffing overhead, accelerate deployment pipelines, and achieve more consistent security postures. However, the transition is not a one‑click upgrade; AWS deliberately excludes automated migration of workloads, storage classes, and ingress resources to avoid unintended production disruptions. Understanding these limits is essential before committing existing clusters to the new model.

Rafay’s platform fills the gap by providing a guided, cluster‑level migration path that automates access‑entry conversion, node‑pool creation, and the switch to Auto Mode control planes. The process begins with a sample application that exercises storage, service, and ingress components, then proceeds through steps such as cordoning legacy nodes, deploying a gp3 StorageClass, and installing an Auto‑mode‑specific IngressClass. Critical to the workflow is the eks‑auto‑mode‑ebs‑migration‑tool, which safely re‑creates PersistentVolumeClaims under the new CSI driver after a dry‑run verification. These actions ensure workloads continue to run while the underlying infrastructure is re‑architected.

After the migration, teams should decommission legacy managed node groups and remove built‑in add‑ons such as vpc‑cni, coredns, and the aws‑load‑balancer‑controller, allowing Auto Mode to manage them natively. This cleanup reduces surface area for configuration drift and aligns cost allocation with actual usage, as Auto Mode scales nodes on demand. Organizations that follow the prescribed checklist can achieve a smoother transition, minimize downtime, and position their Kubernetes environment for future enhancements like serverless workloads or multi‑cluster federation under a single, automated control plane.