Kubernetes Comparison: EKS vs AKS vs GKE

Managed Kubernetes Overview

Kubernetes has become the de facto standard for container orchestration, and every major cloud provider now offers a managed Kubernetes service: Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), and Google Kubernetes Engine (GKE). These services abstract away control plane management, etcd operations, and API server availability, letting your team focus on deploying workloads instead of babysitting infrastructure. But the similarities end at the surface. Each provider has made distinct design decisions around networking, autoscaling, security integration, and pricing that can profoundly affect your operational experience.

Choosing between EKS, AKS, and GKE is not simply a matter of which cloud you already use. Organizations with multi-cloud strategies need to understand how cluster federation, service mesh interoperability, and workload portability differ across providers. Even single-cloud shops benefit from understanding the competitive landscape. Knowing what GKE does well can help you push for similar capabilities on EKS or AKS through add-ons and configuration.

This guide provides a thorough, side-by-side comparison of all three managed Kubernetes services. We cover architecture, networking, autoscaling, security, cost modeling, and multi-cluster strategies. Every section includes real CLI commands, configuration snippets, and tables so you can make an informed decision, or operate confidently across all three.

EKS Architecture & Features

Amazon EKS runs a highly available Kubernetes control plane across at least three Availability Zones in your chosen AWS region. The control plane includes the Kubernetes API server, etcd (using a custom, optimized distribution), and controllers. AWS manages patching, scaling, and availability of these components, and you never interact with etcd directly.

EKS supports two compute models for worker nodes: self-managed EC2 instances and EKS-managed node groups (which automate provisioning, lifecycle management, and upgrades). A third option, Fargate, runs pods on serverless infrastructure without any node management at all.

Creating an EKS Cluster

The eksctl tool is the official CLI for creating and managing EKS clusters. It generates the required VPC, subnets, IAM roles, and node groups with sensible defaults.

apiVersion: eksctl.io/v1alpha5
kind: ClusterConfig

metadata:
  name: production-cluster
  region: us-east-1
  version: "1.29"

managedNodeGroups:
  - name: general-workloads
    instanceType: m6i.xlarge
    minSize: 3
    maxSize: 15
    desiredCapacity: 5
    volumeSize: 100
    volumeType: gp3
    labels:
      workload-type: general
    iam:
      withAddonPolicies:
        albIngress: true
        cloudWatch: true
        ebs: true

  - name: gpu-workloads
    instanceType: g5.2xlarge
    minSize: 0
    maxSize: 8
    desiredCapacity: 0
    labels:
      workload-type: gpu
    taints:
      - key: nvidia.com/gpu
        value: "true"
        effect: NoSchedule

addons:
  - name: vpc-cni
    version: latest
  - name: coredns
    version: latest
  - name: kube-proxy
    version: latest
  - name: aws-ebs-csi-driver
    version: latest

iam:
  withOIDC: true

# Create the cluster from the configuration file
eksctl create cluster -f eksctl-cluster.yaml

# Verify cluster access
kubectl get nodes
kubectl cluster-info

EKS Networking

EKS uses the Amazon VPC CNI plugin by default, which assigns each pod a real VPC IP address from your subnet CIDR. This means pods are directly routable within the VPC without overlay networks, simplifying debugging and network policy enforcement. The trade-off is IP address consumption; large clusters can exhaust subnet space quickly. AWS mitigates this with prefix delegation, which assigns /28prefixes to nodes instead of individual IPs, increasing pod density per node.

EKS Key Differentiators

• EKS Pod Identity: Simplified IAM role assignment to pods without OIDC federation complexity
• Karpenter: AWS-native node autoscaler that provisions right-sized nodes in seconds
• EKS Blueprints: Terraform and CDK patterns for production-ready clusters with add-ons preconfigured
• EKS Anywhere: Run EKS on your own infrastructure (on-premises or edge)
• Fargate Profiles: Serverless pods with no node management overhead

AKS Architecture & Features

Azure Kubernetes Service provides a managed control plane with a unique pricing advantage: the control plane is free. You pay only for the worker nodes (VMs) and associated resources like disks and load balancers. AKS integrates deeply with Azure Active Directory (now Entra ID), Azure Monitor, Azure Policy, and Azure Key Vault, making it the natural choice for organizations already invested in the Microsoft ecosystem.

AKS supports system node pools (which run critical cluster components like CoreDNS and metrics-server) and user node pools (which run your application workloads). This separation improves reliability by ensuring cluster infrastructure pods are not evicted due to resource pressure from application pods.

Creating an AKS Cluster

# Create a resource group
az group create --name rg-k8s-prod --location eastus

# Create an AKS cluster with system and user node pools
az aks create \
  --resource-group rg-k8s-prod \
  --name aks-prod-cluster \
  --kubernetes-version 1.29.2 \
  --node-count 3 \
  --node-vm-size Standard_D4s_v5 \
  --network-plugin azure \
  --network-policy calico \
  --enable-managed-identity \
  --enable-aad \
  --aad-admin-group-object-ids <ADMIN_GROUP_ID> \
  --enable-defender \
  --enable-oidc-issuer \
  --enable-workload-identity \
  --zones 1 2 3 \
  --generate-ssh-keys

# Add a user node pool for application workloads
az aks nodepool add \
  --resource-group rg-k8s-prod \
  --cluster-name aks-prod-cluster \
  --name userpool \
  --node-count 5 \
  --node-vm-size Standard_D8s_v5 \
  --max-pods 50 \
  --zones 1 2 3 \
  --mode User

# Get cluster credentials
az aks get-credentials \
  --resource-group rg-k8s-prod \
  --name aks-prod-cluster

AKS Key Differentiators

• Free control plane: No hourly charge for the Kubernetes API server (optional paid tier for uptime SLA)
• Azure AD / Entra ID integration: Native RBAC binding to enterprise identity with Conditional Access
• AKS Automatic: Opinionated, fully managed configuration similar to GKE Autopilot
• Azure Policy for AKS: Enforce governance rules (e.g., no privileged containers) via Azure Policy
• Workload Identity: Federated identity for pods accessing Azure resources without secrets
• Draft: Built-in tool to generate Dockerfiles, Helm charts, and GitHub Actions workflows

GKE Architecture & Features

Google Kubernetes Engine is the most mature managed Kubernetes offering. Google invented Kubernetes based on its internal Borg system, and GKE has been generally available since 2015. GKE provides two operational modes: Standard (you manage node pools) and Autopilot (Google manages everything including nodes, scaling, and security hardening).

GKE Autopilot is a genuine differentiator. In Autopilot mode, you simply deploy pods, and GKE automatically provisions nodes, right-sizes them, applies security best practices, and charges per pod resource request rather than per VM. This eliminates over-provisioning waste and node management toil.

Creating a GKE Cluster

# Create a GKE Autopilot cluster
gcloud container clusters create-auto gke-prod-cluster \
  --region us-central1 \
  --release-channel regular \
  --enable-master-authorized-networks \
  --master-authorized-networks 10.0.0.0/8 \
  --enable-private-nodes \
  --master-ipv4-cidr 172.16.0.0/28 \
  --network vpc-prod \
  --subnetwork subnet-gke \
  --cluster-secondary-range-name pods \
  --services-secondary-range-name services

# Or create a Standard cluster with more control
gcloud container clusters create gke-standard-cluster \
  --region us-central1 \
  --num-nodes 3 \
  --machine-type e2-standard-4 \
  --enable-autoscaling \
  --min-nodes 3 \
  --max-nodes 20 \
  --release-channel regular \
  --enable-ip-alias \
  --enable-network-policy \
  --workload-pool=PROJECT_ID.svc.id.goog \
  --enable-shielded-nodes

# Get credentials
gcloud container clusters get-credentials gke-prod-cluster \
  --region us-central1

GKE Key Differentiators

• Autopilot mode: Fully managed pods-only experience with per-pod billing
• GKE Enterprise: Multi-cluster management, service mesh, policy controller, and Config Sync
• Release channels: Rapid, Regular, and Stable channels with automated upgrades
• Binary Authorization: Enforce deploy-time attestation policies for container images
• Workload Identity Federation: Map Kubernetes service accounts to Google Cloud IAM
• Multi-cluster Services: Native service discovery across GKE clusters
• GKE Sandbox (gVisor): Kernel-level isolation for untrusted workloads

Feature-by-Feature Comparison

The following table summarizes the critical differences across all three managed Kubernetes services. Use this as a quick reference when evaluating providers.

Networking & Service Mesh

Networking is where the three services diverge most significantly. Each provider has built a CNI plugin that integrates with their native virtual network infrastructure, and the trade-offs in IP management, policy enforcement, and service mesh integration have real operational impact.

CNI Comparison

Ingress Controllers

Each provider offers a native ingress solution that integrates with their respective load balancer services:

• EKS: AWS Load Balancer Controller creates ALBs for Ingress resources and NLBs for LoadBalancer services
• AKS: Application Gateway Ingress Controller (AGIC) or the managed NGINX ingress add-on
• GKE: GKE Ingress controller natively creates Google Cloud Load Balancers with health checks and CDN integration

Service Mesh Options

For service-to-service communication, mTLS, and traffic management, each provider now offers a managed Istio-based service mesh. GKE has the most mature offering (Cloud Service Mesh, formerly Anthos Service Mesh), while EKS and AKS have added Istio-based add-ons more recently. All three also support bring-your-own mesh solutions like Linkerd and Consul.

Autoscaling & Node Management

Autoscaling in Kubernetes operates at two levels: pod-level scaling (HPA, VPA, KEDA) and node-level scaling. All three providers support the Kubernetes Cluster Autoscaler, but each has developed additional capabilities that improve scaling speed, cost efficiency, or ease of use.

Node Autoscaling Comparison

# EKS Karpenter NodePool configuration
apiVersion: karpenter.sh/v1beta1
kind: NodePool
metadata:
  name: default
spec:
  template:
    spec:
      requirements:
        - key: kubernetes.io/arch
          operator: In
          values: ["amd64", "arm64"]
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand", "spot"]
        - key: karpenter.k8s.aws/instance-category
          operator: In
          values: ["m", "c", "r"]
        - key: karpenter.k8s.aws/instance-generation
          operator: Gt
          values: ["5"]
      nodeClassRef:
        apiVersion: karpenter.k8s.aws/v1beta1
        kind: EC2NodeClass
        name: default
  limits:
    cpu: "1000"
    memory: 1000Gi
  disruption:
    consolidationPolicy: WhenUnderutilized
    expireAfter: 720h

Security & IAM Integration

Kubernetes security spans multiple layers: cluster access control, pod-level identity, network segmentation, image signing, and runtime protection. Each cloud provider integrates their IAM system differently with Kubernetes RBAC, and the ergonomics vary significantly.

Workload Identity Comparison

All three providers now support a mechanism to map Kubernetes service accounts to cloud IAM identities without embedding credentials in pods:

# EKS Pod Identity association
aws eks create-pod-identity-association \
  --cluster-name production-cluster \
  --namespace app \
  --service-account app-sa \
  --role-arn arn:aws:iam::123456789012:role/app-s3-reader

# AKS Workload Identity setup
az identity create --name app-identity --resource-group rg-k8s-prod
az identity federated-credential create \
  --name app-fed-cred \
  --identity-name app-identity \
  --resource-group rg-k8s-prod \
  --issuer "$(az aks show -g rg-k8s-prod -n aks-prod-cluster --query oidcIssuerProfile.issuerUrl -o tsv)" \
  --subject system:serviceaccount:app:app-sa

# GKE Workload Identity binding
gcloud iam service-accounts add-iam-policy-binding \
  app-sa@PROJECT_ID.iam.gserviceaccount.com \
  --role roles/iam.workloadIdentityUser \
  --member "serviceAccount:PROJECT_ID.svc.id.goog[app/app-sa]"

kubectl annotate serviceaccount app-sa \
  --namespace app \
  iam.gke.io/gcp-service-account=app-sa@PROJECT_ID.iam.gserviceaccount.com

Security Hardening

Beyond workload identity, each provider offers distinct security capabilities:

• EKS: GuardDuty EKS Protection detects threats in audit logs and runtime behavior. EKS supports Amazon VPC Security Groups for pods.
• AKS: Defender for Containers provides vulnerability assessment, runtime protection, and compliance scanning. Azure Policy enforces admission controls.
• GKE: Security Command Center integrates with GKE for vulnerability scanning. Binary Authorization enforces image signing. GKE Sandbox provides gVisor kernel isolation.

Cost Comparison & Optimization

Kubernetes costs come from three primary sources: the control plane, the worker nodes (compute), and data transfer. The relative weight of each depends on cluster size and traffic patterns, but compute typically accounts for 70–85% of total spend.

Control Plane Pricing

Cost Optimization Strategies

The following strategies work across all three providers to reduce Kubernetes spend:

• Right-size resource requests: Use VPA recommendations to set accurate CPU and memory requests. Over-requesting wastes capacity; under-requesting causes throttling.
• Spot / preemptible instances: Use spot instances for fault-tolerant workloads like batch jobs, CI/CD runners, and stateless services with graceful shutdown handlers.
• Bin packing: Karpenter (EKS) and NAP (GKE) consolidate pods onto fewer nodes. On AKS, use the Cluster Autoscaler with --scale-down-utilization-threshold tuning.
• Committed use discounts: AWS Savings Plans, Azure Reservations, and GCP Committed Use Discounts all offer 30–60% savings for predictable base capacity.
• Namespace resource quotas: Prevent teams from over-provisioning by setting CPU and memory quotas per namespace.

# Apply resource quotas per namespace (works on all providers)
apiVersion: v1
kind: ResourceQuota
metadata:
  name: team-alpha-quota
  namespace: team-alpha
spec:
  hard:
    requests.cpu: "40"
    requests.memory: 80Gi
    limits.cpu: "80"
    limits.memory: 160Gi
    pods: "100"
    services.loadbalancers: "5"
    persistentvolumeclaims: "20"

Migration & Multi-Cluster Strategies

Organizations often run Kubernetes across multiple providers for resilience, compliance, or vendor negotiation leverage. Multi-cluster and multi-cloud Kubernetes introduces significant complexity, but the right tooling can make it manageable.

Multi-Cluster Federation Tools

• GKE Enterprise (Anthos): The most comprehensive multi-cluster solution. Provides Config Sync (GitOps), Policy Controller (OPA Gatekeeper), Service Mesh, and a unified fleet view across GKE, EKS, AKS, and on-premises clusters.
• Azure Arc-enabled Kubernetes: Project clusters from any provider into Azure for unified management, policy enforcement, and GitOps configuration via Flux.
• EKS Connector: Register external clusters (including AKS and GKE) with the EKS console for visibility, though management capabilities are limited compared to Anthos and Arc.
• Open-source tools: Submariner (cross-cluster networking), Liqo (resource sharing), and KubeFed (API federation) provide vendor-neutral multi-cluster capabilities.

Migration Between Providers

Migrating workloads between EKS, AKS, and GKE is straightforward for stateless applications that use standard Kubernetes manifests. Challenges arise with:

• Storage: PersistentVolume implementations differ. Use Velero with cloud-specific plugins for backup and restore across providers.
• Ingress: Provider-specific annotations on Ingress resources do not transfer. Abstract with Gateway API (now GA) for portable ingress configuration.
• IAM: Workload identity mechanisms are entirely provider-specific. Standardize on SPIFFE/SPIRE for portable workload identity in multi-cloud deployments.
• Observability: Use OpenTelemetry for portable telemetry collection rather than provider-specific agents.

# Portable Gateway API configuration (works on EKS, AKS, GKE)
apiVersion: gateway.networking.k8s.io/v1
kind: Gateway
metadata:
  name: production-gateway
  namespace: infra
spec:
  gatewayClassName: gke-l7-global-external-managed  # Change per provider
  listeners:
    - name: https
      protocol: HTTPS
      port: 443
      tls:
        mode: Terminate
        certificateRefs:
          - name: wildcard-cert
      allowedRoutes:
        namespaces:
          from: All
---
apiVersion: gateway.networking.k8s.io/v1
kind: HTTPRoute
metadata:
  name: app-route
  namespace: app
spec:
  parentRefs:
    - name: production-gateway
      namespace: infra
  hostnames:
    - "app.example.com"
  rules:
    - matches:
        - path:
            type: PathPrefix
            value: /api
      backendRefs:
        - name: api-service
          port: 8080
          weight: 90
        - name: api-service-canary
          port: 8080
          weight: 10

Related Resources

Dive deeper into provider-specific Kubernetes topics with these related guides:

• AWS ECS vs EKS Decision Guide
• Azure AKS vs App Service Guide
• GCP GKE vs Cloud Run Decision Guide