All three major cloud providers offer managed Kubernetes: AWS Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), and Azure Kubernetes Service (AKS). “Managed” means the control plane is handled for you—you don’t run etcd, the API server, or the scheduler. But beyond that baseline, the products diverge significantly in maturity, cost, operational experience, and ecosystem integration.
Control Plane Management
GKE manages the control plane completely and transparently. Upgrades happen automatically within a release channel (Rapid, Regular, Stable) you select. You can also upgrade manually. The control plane is free for Zonal clusters; Regional clusters (three control plane replicas across zones) cost $0.10/hour (~$73/month). GKE’s Autopilot mode takes this further—Google manages nodes as well, billing per pod rather than per node.
EKS charges $0.10/hour per cluster (~$73/month) for a managed control plane. AWS handles upgrades, but the upgrade process requires more manual intervention than GKE—you upgrade the control plane first, then each node group separately. In-place node upgrades are possible with managed node groups, but less seamless than GKE’s approach. Add-ons (CoreDNS, kube-proxy, VPC CNI) require separate upgrade management.
AKS provides a free control plane—you pay only for the worker nodes. This is AKS’s most obvious cost advantage. The control plane is managed by Microsoft, with automatic upgrades configurable by channel. AKS has historically lagged GKE and EKS in Kubernetes version availability—new releases take longer to reach AKS.
Node Management
GKE Autopilot is the furthest abstraction: you declare pods, GKE provisions the right nodes, and you’re billed for pod resources (CPU, memory, ephemeral storage). No node management, no capacity planning, no patching. Regular (Standard) mode gives you node control with managed node pools that auto-upgrade.
EKS offers managed node groups (EC2 instances that EKS manages) and Fargate (serverless, per-pod billing similar to Autopilot). The Fargate experience is less polished than GKE Autopilot—not all Kubernetes features work on Fargate, and debugging is harder. Karpenter (an open-source node autoscaler from AWS) has become the preferred approach for efficient node provisioning on EKS.
AKS uses node pools backed by Azure VMs or Virtual Machine Scale Sets. Azure Spot instances for preemptible workloads are first-class, and the spot eviction integration is mature. AKS’s Virtual Nodes (backed by Azure Container Instances) provide burst capacity similar to Fargate but with limitations.
Networking
Networking is where the platforms diverge most for operators.
GKE uses its own VPC-native networking with Alias IPs. Pods get real VPC IP addresses—no overlay network, native routing, no NAT for pod-to-pod traffic within the VPC. This simplicity is a genuine advantage. Google Cloud Load Balancing integrates deeply with GKE’s Ingress. Dataplane V2 (based on Cilium/eBPF) is available for network policy enforcement.
EKS uses the VPC CNI plugin by default, giving pods ENI (Elastic Network Interface) addresses from the VPC. This is powerful but has a hard limit: each EC2 instance type supports a finite number of ENIs and IPs. A moderately-sized node might support 30 pods max due to ENI limits, which surprises teams expecting Kubernetes’s default pod density. Prefix delegation and custom networking modes exist to work around this. EKS supports multiple CNI plugins (Calico, Cilium, Weave) if you want to replace the default.
AKS supports Azure CNI (pods get VNet IPs, similar to EKS VPC CNI) or kubenet (overlay network). Azure CNI has IP exhaustion issues similar to EKS’s ENI limits, mitigated in newer Azure CNI Overlay mode. Network Policy is handled by Azure Network Policy Manager or Calico.
Ecosystem and Cloud Integration
EKS shines for AWS-native teams. IAM Roles for Service Accounts (IRSA) lets pods assume IAM roles without credential files. Integration with ALB, NLB, EBS, EFS, RDS, ElastiCache, and 200+ AWS services is production-grade. For teams deciding between EKS and simpler container alternatives, the ECS vs EKS comparison is worth reading first. AWS Load Balancer Controller, EBS CSI driver, and EFS CSI driver are maintained by AWS and work reliably. If your stack is AWS-native, EKS’s integration is hard to match.
GKE shines for Google Cloud teams. Workload Identity (the equivalent of IRSA) is more seamless to configure. GKE integrates deeply with Cloud Monitoring, Cloud Logging, and Cloud Trace—the default observability setup is better out-of-the-box than EKS. Config Connector lets you manage Google Cloud resources via Kubernetes CRDs, which is either useful or alarming depending on your philosophy.
AKS integrates with Azure AD for authentication (AAD-integrated RBAC is a strong enterprise feature), Azure Monitor, Azure Policy, and Azure Defender for Containers. For organizations standardized on Microsoft’s enterprise tooling (Active Directory, Azure DevOps, Microsoft Sentinel), AKS’s integration is the most cohesive.
Cost Comparison
A realistic production cluster with 3 nodes (8 CPU, 32GB RAM each), multi-zone, running 24/7:
GKE (Regional): ~$73/month control plane + ~$600/month nodes (n2-standard-8 × 3) = ~$673/month. With committed use discounts, nodes drop ~30%.
EKS: ~$73/month control plane + ~$700/month nodes (m5.2xlarge × 3) = ~$773/month. Reserved instances reduce node cost ~40%.
AKS: $0/month control plane + ~$650/month nodes (Standard_D8s_v3 × 3) = ~$650/month. Reserved instances reduce ~40%.
Raw node pricing is comparable across providers. The control plane cost difference (GKE/EKS $73/month vs AKS $0) is meaningful at small cluster counts but irrelevant at scale. The real cost differences emerge from data egress, load balancer pricing, and storage class choices.
Operational Maturity
GKE is the most mature product—Kubernetes was born at Google, and GKE has been GA since 2015. Features like node auto-provisioning, vertical pod autoscaling, and multi-cluster management are more polished than on competing platforms. Googlers who built Kubernetes work on GKE.
EKS launched in 2018 and has caught up significantly, but still trails GKE in operational smoothness. Upgrades require more steps, add-on management is more manual, and some EKS-specific behaviors (ENI limits, Fargate limitations) require workarounds. That said, EKS’s AWS integration compensates if you’re deep in the AWS ecosystem.
AKS has improved substantially since its rocky early years. Enterprise features (AAD integration, Azure Policy, Defender for Containers) are production-grade. Version availability and upgrade experience still lag GKE, but the gap has narrowed.
When to Use Each
Choose GKE if:
- You’re on Google Cloud or willing to be
- You want the most mature, lowest-friction Kubernetes experience
- Autopilot’s node-abstraction model appeals to your team
- You’re starting a greenfield project and cloud choice is open
Choose EKS if:
- You’re already AWS-native and need deep AWS service integration
- Your team is comfortable with AWS’s operational model
- You’re using Karpenter for node provisioning (the best-in-class autoscaling experience)
- Lock-in to AWS is already accepted
Choose AKS if:
- You’re standardized on Azure and Microsoft’s enterprise ecosystem
- Azure AD integration for cluster authentication matters
- The free control plane is meaningful at your cluster count
- Azure DevOps and Microsoft compliance tooling is already in use
The Multi-Cloud Question
All three support the same Kubernetes API, which means workloads are portable in theory. In practice, cloud-specific integrations (storage classes, load balancer annotations, IAM/RBAC bindings) create coupling that makes migration painful. If multi-cloud portability is a genuine requirement rather than a hedge, invest in abstractions (Crossplane, Backstage, cloud-agnostic service mesh) from day one—don’t count on Kubernetes API compatibility to make migration easy.