EKS vs AKS vs GKE: Choosing a Managed Kubernetes Provider#
All three major managed Kubernetes services run certified, conformant Kubernetes. The differences lie in networking models, identity integration, node management, upgrade experience, cost, and ecosystem strengths. Your choice should be driven by where the rest of your infrastructure lives, your team’s existing expertise, and specific feature requirements.
Feature Comparison#
Control Plane#
GKE has the most polished upgrade experience. Release channels (Rapid, Regular, Stable) provide automatic upgrades with configurable maintenance windows. Surge upgrades handle node pools with minimal disruption. Google invented Kubernetes, and GKE reflects that pedigree in control plane operations.
EKS is the most customizable. You control when upgrades happen, which add-ons to install, and how the control plane is configured. This flexibility comes at the cost of more operational decisions – EKS does not auto-upgrade by default.
AKS sits in the middle. It supports automatic upgrades via channels (patch, stable, rapid, node-image) and provides a reasonable out-of-box experience. The Azure Kubernetes Service is tightly integrated with the Azure portal for monitoring and management.
Node Management#
| Feature | EKS | AKS | GKE |
|---|---|---|---|
| Managed node groups | Yes (EKS Managed Node Groups) | Yes (VMSS-based node pools) | Yes (GKE node pools) |
| Serverless / fully managed nodes | Fargate (pods, not nodes) | Virtual Nodes (ACI, limited) | Autopilot (fully managed) |
| Autoscaling | Cluster Autoscaler or Karpenter | Cluster Autoscaler or Karpenter | Cluster Autoscaler or NAP |
| Spot/preemptible nodes | Spot Instances | Spot VMs | Spot VMs |
| ARM node support | Graviton instances | Ampere Arm VMs | Tau T2A Arm VMs |
| GPU support | NVIDIA (P4, V100, A100, H100) | NVIDIA (T4, V100, A100, H100) | NVIDIA (T4, L4, A100, H100), TPUs |
GKE Autopilot is the most hands-off option: Google manages node provisioning, scaling, OS patching, and security. You define pod resource requests and Google provisions the infrastructure. This eliminates node management entirely but limits customization (no privileged pods, no host networking by default, no DaemonSets in Autopilot standard mode).
Karpenter (originally AWS, now multi-cloud) is becoming the de facto autoscaler for right-sizing nodes to workloads. It replaces Cluster Autoscaler with a model that provisions nodes based on pending pod requirements, choosing instance types dynamically.
Networking#
| Feature | EKS | AKS | GKE |
|---|---|---|---|
| Default CNI | VPC CNI (pods get VPC IPs) | Azure CNI or kubenet | GKE VPC-native (alias IPs) |
| Network policy | Calico (add-on) | Azure NPM or Calico | Calico (Dataplane V2 / Cilium) |
| Pod IP model | Pods share VPC IP space | Pods share VNet IP space (Azure CNI) or NAT (kubenet) | Pods use alias IP ranges |
| Service mesh | App Mesh (deprecated), Istio | Istio-based add-on, Open Service Mesh (deprecated) | Istio-based (Anthos Service Mesh) |
| Gateway API | Via third-party controllers | Via third-party controllers | Native GKE Gateway controller |
| Load balancer | ALB/NLB via AWS LB Controller | Azure Load Balancer, App Gateway | Cloud Load Balancing (native) |
EKS VPC CNI assigns each pod a real VPC IP address. This simplifies security group rules and VPC routing but can exhaust IP addresses in smaller subnets. Plan CIDR ranges carefully – a /24 subnet supports roughly 250 pods, not 250 nodes.
GKE was the first to adopt Gateway API natively, providing a GKE Gateway controller that maps Gateway resources directly to Google Cloud Load Balancing. This is the most mature cloud-native Gateway API implementation.
Identity and Security#
| Feature | EKS | AKS | GKE |
|---|---|---|---|
| Workload identity | EKS Pod Identity (newer) or IRSA | Workload Identity (Entra ID) | Workload Identity Federation |
| Control plane auth | IAM + aws-auth ConfigMap or Access Entries | Entra ID + Azure RBAC | Google IAM + RBAC |
| Secrets encryption | KMS envelope encryption | Azure Key Vault + KMS | Cloud KMS envelope encryption |
| Pod security | Pod Security Standards + admission | Azure Policy (OPA-based) | Pod Security Standards + GKE Policy Controller |
All three providers support workload identity – mapping Kubernetes service accounts to cloud IAM roles/identities so pods can access cloud services without static credentials. The implementation details differ:
- EKS Pod Identity is the newer, simpler approach (replacing IRSA). It uses an EKS-managed agent to provide credentials
- AKS Workload Identity federates Kubernetes service accounts with Entra ID (formerly Azure AD) managed identities
- GKE Workload Identity Federation maps Kubernetes service accounts to Google Cloud service accounts
Storage#
| Feature | EKS | AKS | GKE |
|---|---|---|---|
| Block storage | EBS CSI driver | Azure Disk CSI | Persistent Disk CSI |
| File storage | EFS CSI driver | Azure Files CSI | Filestore CSI |
| High-performance | io2 Block Express, FSx | Ultra Disk, ANF | Hyperdisk, Parallelstore |
| Object (CSI) | Mountpoint for S3 | Blob CSI | GCS FUSE CSI |
Cost#
| Component | EKS | AKS | GKE |
|---|---|---|---|
| Control plane | $74.40/month per cluster | Free (no SLA) or $74.40/month (Standard) | Free (one zonal), $74.40/month (regional/additional) |
| Extended support | Premium tier pricing | Premium tier pricing | Premium tier pricing |
| Autopilot / serverless | Fargate: per-pod vCPU/memory pricing | N/A | Autopilot: per-pod resource pricing |
AKS offers a free control plane tier, but it comes without a financially-backed SLA. For production, the Standard tier at $74.40/month is recommended. GKE provides one free zonal cluster (Standard or Autopilot), making it the cheapest entry point for a single cluster.
Ecosystem Strengths#
AWS (EKS): Broadest service catalog. If your workloads integrate with dozens of AWS services (RDS, SQS, DynamoDB, Lambda, S3), EKS provides the smoothest integration path. The third-party ecosystem is also the largest – most Kubernetes tools test on EKS first. Karpenter originated here and remains most mature on AWS.
Azure (AKS): Strongest choice for Microsoft-centric organizations. Entra ID provides unified identity across Azure services and Kubernetes. .NET workloads benefit from first-class Azure integration. Azure DevOps pipelines have native AKS deployment tasks. If your organization runs on Microsoft 365, Entra ID, and Azure services, AKS minimizes friction.
GCP (GKE): Best Kubernetes experience from the team that created it. GKE Autopilot provides the most managed node experience. GKE Gateway API support is the most mature. For data and ML workloads, GCP offers TPUs alongside GPUs, BigQuery integration, and Vertex AI. If Kubernetes operations quality is the top priority, GKE is the strongest choice.
Decision Recommendations#
Choose EKS when:
- Your organization is AWS-centric with existing VPC infrastructure, IAM policies, and AWS service dependencies
- You need maximum flexibility in cluster configuration and upgrade timing
- Karpenter-based cost optimization is a priority (most mature on EKS)
- Your third-party tooling ecosystem is tested primarily against AWS
- You need the broadest selection of instance types including Graviton ARM and specialized GPU instances
Choose AKS when:
- Your organization uses Microsoft Azure, Entra ID, and Microsoft 365
- Unified identity management through Entra ID is important
- .NET workloads are a significant portion of your services
- Free control plane tier matters for non-production clusters
- You want Azure Policy integration for compliance and governance
Choose GKE when:
- You want the best out-of-box Kubernetes experience with the least operational friction
- GKE Autopilot’s fully managed node model fits your operational preferences
- Data and ML workloads are primary (BigQuery, Vertex AI, TPU access)
- Gateway API is a core part of your networking strategy
- You prioritize Kubernetes feature adoption speed (GKE often ships new K8s features first)
Multi-Cloud Considerations#
If multi-cloud is a realistic possibility (not a hypothetical one – most organizations overestimate their multi-cloud needs), minimize provider-specific features:
Portable (use freely):
- Standard Kubernetes APIs: Deployments, Services, ConfigMaps, Secrets, RBAC
- Helm charts with parameterized cloud-specific values
- Terraform/Crossplane for infrastructure provisioning with provider-specific modules
- Standard CSI drivers, Ingress controllers, cert-manager
Not portable (use deliberately):
- Cloud-specific CRDs: GKE BackendConfig, EKS ENIConfig, AKS-specific extensions
- Provider IAM bindings: IRSA/Pod Identity, Workload Identity, Entra ID federation
- Provider-specific storage classes and their performance tiers
- Cloud load balancer annotations and configurations
- Managed add-ons: EKS add-ons, AKS extensions, GKE-managed services
The pragmatic approach: commit to one provider for a given workload set and use standard Kubernetes APIs where possible. True multi-cloud Kubernetes adds significant complexity with marginal benefit for most organizations. If you need multi-cloud for compliance or disaster recovery, invest in Crossplane or Terraform abstractions rather than trying to write provider-agnostic Kubernetes manifests.
Switching Providers#
Migrating between managed Kubernetes providers is possible but non-trivial. The Kubernetes workload manifests (Deployments, Services, ConfigMaps) are portable. Everything else – networking, identity, storage, load balancing, monitoring integration – must be rebuilt. Budget 3-6 months for a production migration between cloud providers, including testing and validation.