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EKS vs GKE: A Practical Guide to Kubernetes Migration Decisions

Your team just got approval for a major Kubernetes initiative, and now you’re staring at two tabs: the EKS pricing calculator and the GKE documentation. Both promise managed Kubernetes nirvana, but six months from now, one choice will feel obvious and the other will haunt your infrastructure decisions. The marketing materials won’t tell you this, but I’ve seen teams burn through entire quarters untangling themselves from the wrong choice.

This guide cuts through the marketing noise to examine what actually matters when choosing between Amazon Elastic Kubernetes Service and Google Kubernetes Engine. We’ll cover the real costs, the hidden gotchas, and the migration paths you’ll wish you’d known about before signing that cloud contract.

The Fundamental Philosophy Difference

Before diving into specifics, it’s worth understanding how these two platforms approach managed Kubernetes differently. AWS built EKS as another service in their ecosystem—it plugs into VPCs, IAM, and CloudWatch the same way every other AWS service does. Google, on the other hand, built Kubernetes in the first place (it evolved from their internal Borg system), and GKE reflects that heritage with tighter integration and more opinionated defaults.

This philosophical difference manifests in practical ways. EKS gives you more rope—you can configure nearly everything, swap out components, and integrate with third-party tooling freely. GKE offers a more curated experience with sensible defaults that work well together. Neither approach is inherently better, but your team’s experience level and operational preferences should influence which feels more comfortable.

Think of it this way: EKS is like buying a house with excellent bones that you’ll renovate yourself. GKE is more like a well-designed apartment where everything works, but you can’t knock down walls. Both give you a place to live, but they attract different types of residents.

Control Plane Costs: The First Surprise

The first number most teams look at is control plane pricing, and here’s where GKE makes an appealing first impression. Google offers one free zonal cluster per billing account, while EKS charges $0.10 per hour (roughly $73 per month) from day one. For a development environment or proof-of-concept, that’s real money.

But here’s what the pricing page doesn’t emphasize: that free GKE cluster is zonal, meaning your control plane runs in a single availability zone. For production workloads, you’ll want a regional cluster with a control plane spread across zones for high availability. Regional GKE clusters cost the same $0.10 per hour as EKS.

Where GKE really differentiates is with Autopilot mode. In Autopilot clusters, you pay nothing for the control plane—Google bakes that cost into the per-pod pricing. If you’re running workloads where pod density varies significantly, Autopilot can be remarkably cost-effective because you’re not paying for idle node capacity.

The hidden cost in both platforms isn’t the control plane itself—it’s the ecosystem services you’ll inevitably need. Managed Prometheus, log aggregation, secrets management, and load balancers all add up. A realistic comparison needs to account for the full stack, not just the Kubernetes layer.

Node Management: Where Operational Burden Lives

For most teams, the control plane is set-and-forget. Nodes are where you’ll spend your operational calories. Both platforms offer managed node solutions, but they work quite differently.

EKS provides three options: managed node groups, self-managed nodes, and Fargate for serverless pods. Managed node groups handle AMI updates, draining, and replacement automatically. They’re a reasonable middle ground between full control and hands-off operation. Self-managed nodes give you complete flexibility at the cost of maintaining launch templates, handling updates, and managing the node lifecycle yourself.

GKE structures things around node pools—groups of nodes with identical configurations. You define the machine type, disk size, and other parameters, then GKE handles provisioning. GKE’s Autopilot mode takes this further by eliminating node management entirely. You deploy pods, and Google figures out where to run them.

Here’s a GKE cluster configuration using Terraform that demonstrates the node pool approach:

resource "google_container_cluster" "production" {
  name     = "production-cluster"
  location = "us-central1"


  # Start with a minimal default pool, we'll add our own
  remove_default_node_pool = true
  initial_node_count       = 1


  # Enable Workload Identity for secure pod authentication
  workload_identity_config {
    workload_pool = "${var.project_id}.svc.id.goog"
  }


  # Use VPC-native networking for better pod IP management
  networking_mode = "VPC_NATIVE"
  ip_allocation_policy {
    cluster_secondary_range_name  = "pods"
    services_secondary_range_name = "services"
  }


  # Enable Dataplane V2 for eBPF-based networking
  datapath_provider = "ADVANCED_DATAPATH"


  # Configure maintenance windows for predictable updates
  maintenance_policy {
    recurring_window {
      start_time = "2026-01-01T09:00:00Z"
      end_time   = "2026-01-01T17:00:00Z"
      recurrence = "FREQ=WEEKLY;BYDAY=SA"
    }
  }
}


resource "google_container_node_pool" "primary" {
  name       = "primary-pool"
  cluster    = google_container_cluster.production.id
  location   = "us-central1"


  # Autoscaling configuration
  autoscaling {
    min_node_count = 2
    max_node_count = 10
  }


  node_config {
    machine_type = "e2-standard-4"
    disk_size_gb = 100
    disk_type    = "pd-ssd"


    # Use Container-Optimized OS for security
    image_type = "COS_CONTAINERD"


    # Enable Workload Identity on nodes
    workload_metadata_config {
      mode = "GKE_METADATA"
    }


    # Shielded VM features for enhanced security
    shielded_instance_config {
      enable_secure_boot          = true
      enable_integrity_monitoring = true
    }


    oauth_scopes = [
      "https://www.googleapis.com/auth/cloud-platform"
    ]
  }


  # Surge upgrade for safer updates
  upgrade_settings {
    max_surge       = 1
    max_unavailable = 0
  }
}

The real operational difference emerges in autoscaling. EKS teams increasingly adopt Karpenter, an open-source autoscaler that provisions nodes incredibly fast by working directly with EC2. Karpenter can spin up exactly the right instance type for pending pods, rather than relying on pre-defined node group templates. This flexibility is powerful but requires additional setup and tuning.

GKE’s cluster autoscaler is built-in and requires no additional installation. It’s not as fast or flexible as Karpenter, but it works out of the box. GKE also offers node auto-provisioning, which automatically creates new node pools when existing ones can’t satisfy workload requirements—a middle ground between standard autoscaling and Karpenter’s flexibility.

For teams optimizing for operational simplicity, GKE Autopilot eliminates node decisions entirely. You specify resource requests in your pod specs, and Google handles the rest. The trade-off is less control—you can’t SSH into nodes, can’t run privileged containers, and must accept Google’s security hardening. For many workloads, these restrictions are actually features.

Networking: Where the Real Lock-in Hides

Kubernetes networking is complex enough on its own, but managed Kubernetes adds another layer. Both EKS and GKE make specific networking choices that have long-term implications for your architecture.

EKS uses the AWS VPC CNI plugin by default. This plugin assigns real VPC IP addresses to each pod, making them directly routable within your VPC. The advantage is simplicity—pods can communicate with other VPC resources without NAT or proxies. The disadvantage is IP address consumption. A cluster running thousands of pods can exhaust IP space quickly, especially in smaller VPCs or when you’re also running other AWS services.

GKE uses alias IP ranges for pods through VPC-native networking. Instead of consuming primary VPC IPs, pods get addresses from secondary ranges specifically allocated for Kubernetes. This approach scales better and integrates cleanly with GCP’s firewall rules and routing. The trade-off is slightly more complex initial setup and less direct visibility into pod traffic from VPC flow logs.

Here’s where things get interesting: GKE recently introduced Dataplane V2, an eBPF-based networking stack built on Cilium. This isn’t just a different CNI—it fundamentally changes how network policy enforcement and observability work. With Dataplane V2, you get network policy enforcement without installing Calico, built-in network policy logging, and significantly better visibility into pod-to-pod traffic.

For EKS, achieving similar observability requires installing Cilium yourself or using a combination of VPC flow logs and application-level tracing. It’s doable, but it’s additional work.

Load balancer integration also differs significantly. Both platforms can provision cloud-native load balancers automatically, but the implementation details matter. EKS uses the AWS Load Balancer Controller to provision ALB (Application Load Balancer) for ingress and NLB (Network Load Balancer) for TCP/UDP services. The controller watches for Ingress resources and Service annotations, then provisions the appropriate load balancer.

GKE integrates with Google Cloud Load Balancing more tightly. The GKE Ingress controller provisions global HTTP(S) load balancers that can route traffic to clusters in multiple regions. This is particularly powerful for globally distributed applications, though it requires careful configuration to avoid unexpected costs.

A practical consideration: if you’re using the Kubernetes Gateway API (the successor to Ingress), GKE has more mature support. The Gateway API is still evolving, but GKE’s implementation covers more features and edge cases. EKS support for Gateway API exists but requires additional components.

Identity and Access: The Authentication Maze

Authentication is where I’ve seen the most migration pain. Both platforms integrate Kubernetes RBAC with their cloud IAM systems, but they do it differently enough that switching requires meaningful application changes.

EKS historically used the aws-auth ConfigMap to map AWS IAM users and roles to Kubernetes groups. This approach worked but was fragile—a typo in the ConfigMap could lock everyone out of the cluster. AWS has since introduced Access Entries, an API-based approach that’s more robust and supports gradual migration from ConfigMap-based authentication.

For pod-level authentication to AWS services, EKS offers two mechanisms: IAM Roles for Service Accounts (IRSA) and the newer EKS Pod Identity. IRSA works by configuring the EKS cluster as an OIDC identity provider, then creating IAM roles that trust tokens from specific Kubernetes service accounts. It’s secure and granular, but the setup involves multiple steps across IAM, EKS, and Kubernetes.

GKE’s approach is more streamlined. Workload Identity maps Kubernetes service accounts directly to GCP service accounts. The configuration is simpler, and the mental model is clearer: one Kubernetes service account maps to one GCP service account. Here’s what that configuration looks like in practice:

## Kubernetes ServiceAccount with Workload Identity annotation
apiVersion: v1
kind: ServiceAccount
metadata:
  name: application-sa
  namespace: production
  annotations:
    # This annotation links to the GCP service account
    iam.gke.io/gcp-service-account: [email protected]
---
## Deployment using the service account
apiVersion: apps/v1
kind: Deployment
metadata:
  name: application
  namespace: production
spec:
  replicas: 3
  selector:
    matchLabels:
      app: application
  template:
    metadata:
      labels:
        app: application
    spec:
      serviceAccountName: application-sa
      # Workload Identity requires running on GKE nodes with metadata server
      nodeSelector:
        iam.gke.io/gke-metadata-server-enabled: "true"
      containers:
      - name: app
        image: gcr.io/my-project/application:v1.2.3
        resources:
          requests:
            cpu: "500m"
            memory: "512Mi"
          limits:
            cpu: "1000m"
            memory: "1Gi"
        env:
        - name: GOOGLE_CLOUD_PROJECT
          value: "my-project"
        # Application code uses default credentials - no keys needed
        # The GCP client libraries automatically use Workload Identity

On the GCP side, you need to grant the Kubernetes service account permission to impersonate the GCP service account:

Terminal window
## Allow the Kubernetes service account to impersonate the GCP service account
gcloud iam service-accounts add-iam-policy-binding \
  [email protected] \
  --role="roles/iam.workloadIdentityUser" \
  --member="serviceAccount:my-project.svc.id.goog[production/application-sa]"

The migration implication here is significant. If you’ve built an application on EKS using IRSA, moving to GKE means reconfiguring all pod identity bindings. The Kubernetes manifests change (different annotations), the cloud IAM bindings change (different trust relationships), and you need to verify that your application code handles default credentials correctly in both environments.

For teams planning for portability, consider abstracting cloud authentication behind an interface in your application code. The standard approach is to use environment variables for service-specific configuration while letting client libraries handle credential acquisition automatically.

Storage: The Underrated Migration Complexity

Kubernetes storage seems straightforward—PersistentVolumeClaims request storage, and the cluster provisions it. In practice, storage class behaviors, backup strategies, and cross-zone replication work differently between EKS and GKE.

EKS integrates with EBS (Elastic Block Store) for block storage and EFS (Elastic File System) for shared storage. The EBS CSI driver handles dynamic provisioning, and you can configure storage classes for different performance tiers. EBS volumes are zonal, meaning a pod using an EBS volume can only run in the zone where that volume exists. For stateful workloads, this creates constraints on scheduling and failover.

GKE uses Persistent Disks (standard and SSD variants) for block storage and Filestore for shared NFS storage. Like EBS, Persistent Disks are zonal by default, but GKE also offers regional persistent disks that replicate across zones. Regional disks cost more but enable truly zone-agnostic stateful workloads.

The practical difference shows up during failover scenarios. If an EKS node fails and your pod needs to move zones, it can’t take the EBS volume with it—you need to restore from a snapshot. With GKE regional persistent disks, the volume is available in multiple zones, so the pod can reschedule immediately.

For migration specifically, Velero is the standard tool for backing up Kubernetes resources and persistent volumes. Both EKS and GKE support Velero, making it possible to backup from one platform and restore to another. However, storage class configurations won’t transfer directly—you’ll need to map source storage classes to destination storage classes during restore.

Observability: What You See Depends on Where You Look

Both platforms offer integrated observability, but the depth and default configuration differ significantly. This is an area where GKE has historically been stronger out of the box, though EKS has improved substantially.

EKS integrates with CloudWatch Container Insights for metrics and logs. Container Insights provides pre-built dashboards showing cluster, node, pod, and container metrics. The integration requires installing the CloudWatch agent as a DaemonSet, and you’ll pay for CloudWatch metrics and log ingestion. AWS also offers Amazon Managed Service for Prometheus (AMP) for teams preferring PromQL and Grafana.

GKE automatically sends metrics and logs to Cloud Monitoring and Cloud Logging without additional agent installation. The GKE dashboard in Cloud Console provides Kubernetes-specific views including workload status, node conditions, and resource utilization. Google Cloud Managed Service for Prometheus provides a fully managed Prometheus experience that scales automatically.

Where GKE particularly shines is with Dataplane V2’s observability features. Because networking runs through eBPF, you get visibility into network flows, policy decisions, and packet drops without instrumenting applications. This is invaluable for debugging connectivity issues—instead of wondering why traffic isn’t reaching a pod, you can see exactly where packets are being dropped and why.

For teams already invested in observability tooling like Datadog, New Relic, or Dynatrace, both platforms integrate well through standard Kubernetes mechanisms. The cloud-native options matter more for teams building their observability stack from scratch or looking to minimize third-party dependencies.

The Real Migration Costs

Let’s talk about what actually happens when teams migrate between EKS and GKE. I’ve seen this go both directions, and certain patterns consistently cause problems.

IAM and authentication always take longer than expected. Both platforms have proprietary mechanisms for pod identity that don’t translate directly. Plan for at least a week of work per major application to reconfigure authentication, test permissions, and verify that service accounts have correct access.

Networking changes ripple through everything. If your application relies on specific network behaviors—pod CIDR ranges, direct VPC routing, or specific ingress configurations—expect those to change. Applications that use Kubernetes DNS and service discovery internally are more portable than those with hardcoded IPs or cloud-specific load balancer behaviors.

Storage migration is the hidden bottleneck. For stateless applications, migration is straightforward—redeploy and point traffic at the new cluster. For stateful applications, you need to migrate data. This usually means backup, transfer, and restore cycles that introduce downtime or require application-level synchronization.

Helm charts need attention. Most Helm charts for production applications include cloud-specific annotations for load balancers, storage classes, and service accounts. Review every values file for platform-specific configuration. The core application might be portable, but the deployment configuration rarely is.

CI/CD pipelines need updates. Your build pipeline probably authenticates to the cluster, pushes to a container registry, and triggers deployments. Moving from EKS to GKE (or vice versa) means updating authentication mechanisms, registry endpoints, and deployment commands throughout your automation.

A Decision Framework That Actually Works

After all these details, how should you actually decide? Here’s a framework based on what I’ve seen work in practice.

Choose EKS if:

  • Your organization has standardized on AWS for other services
  • You need deep integration with AWS-native services like SQS, DynamoDB, or Lambda
  • Your team has strong AWS expertise and is comfortable with more configuration options
  • You’re planning to use Karpenter for advanced autoscaling
  • You need EKS Anywhere for on-premises or hybrid deployments

Choose GKE if:

  • You want the lowest possible operational overhead (Autopilot mode)
  • Advanced networking observability is important (Dataplane V2)
  • You’re building ML/AI workloads that integrate with Vertex AI
  • Multi-cluster and multi-region deployments are in your roadmap
  • Your team prefers opinionated defaults over configuration flexibility

Either platform works well for:

  • Standard microservices architectures
  • CI/CD integration with GitOps
  • Compliance and security requirements (both achieve common certifications)
  • Cost optimization at scale (both offer significant discount programs)

The honest answer is that for most standard Kubernetes workloads, either platform will serve you well. The differences matter most at the edges—when you need specific integrations, when you’re optimizing for operational simplicity, or when you’re planning for specific growth patterns.

Making the Migration Smoother

If you’re already on one platform and considering a move, here are concrete steps to reduce friction.

Abstract cloud-specific configuration early. Use external-secrets instead of cloud-specific secrets managers. Use cert-manager instead of cloud-specific certificate provisioning. The more you can move to cloud-agnostic tooling, the less you’ll need to change during migration.

Standardize on Helm charts with clearly separated values. Keep cloud-specific configuration in environment-specific values files rather than the default values. This makes it obvious what needs to change when deploying to a different platform.

Use Velero for backup and disaster recovery now. Even if you’re not planning to migrate, Velero gives you cluster-independent backups. When migration time comes, you’ll have the tooling and process already in place.

Document your networking architecture thoroughly. Draw diagrams showing how traffic flows from external users through load balancers, ingress controllers, services, and pods. This documentation becomes invaluable when planning network configuration in the new platform.

Test critical paths in the target platform early. Don’t wait until migration day to discover that your application behaves differently. Spin up a small cluster, deploy your application, and verify that core functionality works. Identify problems while you have time to address them.

Looking Forward: Platform Evolution

Both platforms continue to evolve rapidly. EKS has been improving its developer experience with better console visibility, the new EKS Dashboard for multi-cluster management, and simplified authentication through Access Entries and Pod Identity. The EKS Dashboard, in particular, addresses a long-standing gap by providing centralized visibility across multiple clusters—something that previously required third-party tooling or custom solutions.

GKE continues to expand Autopilot capabilities and deepen Anthos integration for hybrid scenarios. The investment in Dataplane V2 and Gateway API support suggests Google is pushing toward a more modern networking stack as the default rather than an option.

The Kubernetes ecosystem itself is converging on standards that reduce platform lock-in. Gateway API is replacing Ingress with a more powerful, standardized approach. The Container Storage Interface (CSI) standardizes storage provisioning. OpenTelemetry is becoming the standard for observability instrumentation. As these standards mature, migration between platforms should become less painful.

That said, both cloud providers have strong incentives to add proprietary features that create stickiness. The platforms will likely remain different enough that migration requires meaningful effort. The question is whether the effort is worth the benefit for your specific situation.

Key Takeaways

Cost comparison requires full-stack analysis. Control plane pricing is just the beginning. Include load balancers, observability tooling, secrets management, and networking costs for an accurate comparison. GKE’s free zonal cluster is attractive for development, but production costs are similar between platforms.

Operational burden varies significantly. GKE Autopilot provides the lowest operational overhead for teams willing to accept its constraints. EKS with Karpenter offers excellent scaling performance but requires more configuration. Consider your team’s capacity and preferences honestly.

Authentication is the stickiest migration problem. IRSA and Workload Identity serve similar purposes but work differently. Plan for substantial effort to reconfigure pod authentication when migrating. Abstracting cloud credentials in application code helps but doesn’t eliminate the work.

Networking decisions have long-term implications. The VPC-CNI versus alias IP approach affects IP planning, observability, and network policy enforcement. GKE’s Dataplane V2 provides built-in observability that requires additional setup on EKS. Evaluate your networking requirements carefully.

Start with portability if migration is possible. Use cloud-agnostic tooling where practical: external-secrets, cert-manager, Velero, and standard Kubernetes primitives. Keep cloud-specific configuration isolated and well-documented. The effort pays off whether you migrate or not.

Neither platform is wrong. Both EKS and GKE are mature, production-ready Kubernetes offerings. The choice often comes down to existing cloud investments, team expertise, and specific integration requirements rather than fundamental capability differences. Choose the platform that fits your context, then focus on building great applications.