EKS vs GKE vs AKS: Choosing Your Managed Kubernetes Platform

Kubernetes was born at Google, matured in the AWS ecosystem, and became a first-class citizen on Azure. Each cloud provider brings a different philosophy to managed Kubernetes — different defaults, different cost models, different opinions on how much of the operational burden they should absorb.

If your team is cloud-agnostic (greenfield, multi-cloud evaluation, or planning a migration), this comparison covers what actually matters in production: not just feature lists, but the sharp edges you'll hit six months in.

For the AWS vs Azure two-way comparison, see EKS vs AKS. This post covers all three.

The One-Line Summary

• GKE: Most opinionated, most automated, best autoscaling, steepest per-node cost
• EKS: Most ecosystem support, most AWS-native integration, per-cluster control plane fee
• AKS: Free control plane, best Microsoft/Entra ID integration, most operational flexibility on upgrades

If you're not already locked into a cloud, GKE is the technically superior Kubernetes platform in most dimensions. If you're AWS-native, EKS is the right call. If you're Azure-native, AKS.

Control Plane

GKE

GKE Standard clusters cost $0.10/hour per cluster (~$73/month), matching EKS. This applies to both zonal and regional clusters — Google ended the free-tier exemption for Standard clusters in August 2023. The distinction between zonal and regional is availability, not price:

• Zonal cluster: single-zone control plane (not HA), $0.10/hour — API server unavailable during a zone outage
• Regional cluster: HA control plane across 3 zones, $0.10/hour — recommended for production
• Autopilot: $0.10/hour for the cluster + per-pod resource pricing (no node costs)

GKE's regional clusters are the recommended production configuration. The $73/month buys you a control plane that survives a full zone outage — the API server remains available, workloads keep running, and Kubernetes keeps scheduling. This is meaningfully different from EKS and AKS, where a zone outage affecting the control plane (rare but possible) takes down the API server.

GKE Autopilot is a mode where Google manages not just the control plane but all nodes. You define pods; Google provisions the right nodes. You pay per pod resource request, not per node. No node pools to manage, no node upgrades, no DaemonSet capacity to plan for. The trade-off: you can't run privileged containers, and the per-pod pricing is higher than equivalent on-demand node pricing at scale.

EKS

$0.10/hour per cluster, single-zone control plane high-availability built in (AWS manages multiple API server replicas internally). No free tier. Every cluster costs $73/month before a single node.

AKS

Free control plane. Pay only for nodes. For organisations running many small or short-lived clusters (dev environments, per-PR clusters, testing), this is a material cost difference.

Node Management and Autoscaling

This is where the platforms diverge most significantly.

GKE Node Auto-Provisioning + Cluster Autoscaler

GKE's Node Auto-Provisioning (NAP) automatically creates and removes node pools based on workload requirements. Unlike Karpenter (which provisions individual nodes), NAP creates node pools (groups of nodes) and scales them. The effect is similar: you define constraints (machine families, GPU types, spot preference), and GKE handles the rest.

Combined with Vertical Pod Autoscaler (VPA) — which GKE manages as a first-class component — and Horizontal Pod Autoscaler, GKE provides the most complete autoscaling stack of the three platforms without installing anything.

GKE Autopilot takes this further: zero node management. Google bins-packs pods, selects machine types, handles node upgrades, and manages DaemonSets internally. If you want to stop thinking about nodes entirely, Autopilot is the furthest any managed platform takes that abstraction.

EKS + Karpenter

EKS's answer to NAP is Karpenter — an open-source node provisioner that provisions individual EC2 instances (not node groups) in 30–60 seconds. Karpenter is more granular than GKE NAP, offers better spot/on-demand fallback, and has the largest production community of the three.

Karpenter is not installed by default. You configure it, manage it, and upgrade it. The operational overhead is real but tractable — most platform teams with EKS run Karpenter as a standard component. See How to Install Karpenter on EKS.

AKS Node Auto-Provisioning

AKS's Karpenter-equivalent (Node Auto Provisioning, GA in late 2024) is the newest of the three. It works, but has less community battle-testing than Karpenter on EKS or GKE NAP. For teams that can't invest in Karpenter operational knowledge, AKS NAP is sufficient. For teams that need Karpenter's full feature set, consider EKS.

Autoscaling verdict: GKE Autopilot > GKE Standard with NAP > EKS with Karpenter > AKS with NAP. GKE's advantage is that the best autoscaling story requires the least configuration.

Networking

GKE Networking

GKE uses VPC-native clusters (alias IP ranges) where pods get IP addresses from a secondary CIDR range within the VPC. This is roughly equivalent to EKS's VPC CNI — pods are first-class VPC citizens, routable without an overlay.

Dataplane V2 (default on new GKE clusters) is based on eBPF and Cilium, providing:

• Network policy enforcement via eBPF (faster than iptables)
• FQDN-based network policies
• Hubble integration for network flow observability

This is a meaningful differentiator — GKE has eBPF-based networking built in by default. EKS requires switching from VPC CNI to Cilium; AKS offers Cilium as an add-on.

Traffic Director and Cloud Service Mesh (Google's managed Istio) provide Layer 7 traffic management if needed.

EKS Networking

VPC CNI by default — pods get VPC ENI IPs. Network policy requires a separate plugin (Calico, Cilium, or AWS Network Policy Controller). IP exhaustion is a known issue on large clusters.

Cilium is available as an alternative CNI with eBPF networking, matching GKE's Dataplane V2 capabilities but requiring manual installation and management.

AKS Networking

Azure CNI Overlay (recommended for new clusters) solves the IP exhaustion problem by using a private overlay range for pods. Azure CNI gives pods VNet IPs but runs into the same exhaustion issues as EKS's VPC CNI.

Cilium is available as a managed add-on on AKS.

Networking verdict: GKE's Dataplane V2 (eBPF by default) is the most advanced out of the box. EKS and AKS can match it but require extra configuration.

Identity and Access

GKE: Workload Identity

GKE Workload Identity maps Kubernetes service accounts to Google Cloud service accounts (IAM). It's the most streamlined of the three platform identity systems — no OIDC provider to manage, no DaemonSet to install, just an annotation on the Kubernetes service account and an IAM binding.

1apiVersion: v1
2kind: ServiceAccount
3metadata:
4  name: my-app
5  namespace: production
6  annotations:
7    iam.gke.io/gcp-service-account: my-app@my-project.iam.gserviceaccount.com

gcloud iam service-accounts add-iam-policy-binding \
  my-app@my-project.iam.gserviceaccount.com \
  --role roles/iam.workloadIdentityUser \
  --member "serviceAccount:my-project.svc.id.goog[production/my-app]"

That's it. The pod automatically gets credentials for the GCP service account when it calls GCP APIs.

GKE cluster access uses Google Cloud IAM directly. No aws-auth ConfigMap, no Access Entries API — your existing Google Cloud IAM roles map to Kubernetes RBAC via IAM Kubernetes API.

EKS: Pod Identity (and IRSA)

EKS Pod Identity (GA November 2023) is the current recommended approach. See the EKS vs AKS comparison for details. Slightly more setup than GKE Workload Identity (requires the Pod Identity Agent DaemonSet), but clean once configured.

AKS: Workload Identity (Azure AD)

AKS Workload Identity (GA 2023) uses OIDC federation between Azure AD and Kubernetes service accounts. Excellent for organisations in the Microsoft ecosystem. More complex than GKE's approach for teams without Azure AD background.

Identity verdict: GKE Workload Identity is the simplest and cleanest implementation. All three work well; GKE requires the least configuration.

Upgrades

GKE Upgrade Channels

GKE offers four release channels: Rapid (latest features, including alpha), Regular (balanced, default), Stable (conservative), and Extended (longest support window, enterprise-focused).

Selecting a channel opts your cluster into automatic minor version upgrades on Google's schedule. You can set maintenance windows and exclusion windows to prevent upgrades during business hours or release freezes.

Node auto-upgrade is on by default on GKE — nodes are upgraded automatically with surge upgrades (new nodes provisioned before old ones are drained). The default behaviour is more automated than EKS or AKS, which require more manual intervention.

Node image upgrades (OS security patches) are decoupled from Kubernetes version upgrades on GKE, same as AKS.

EKS Upgrade Model

Manual by default. You initiate control plane upgrades, then node group upgrades separately, then add-on upgrades. Extended support available for $0.60/cluster/hour for versions past EOL.

No automatic upgrade channels — AWS has introduced "auto mode" features but auto-upgrades are not the default EKS experience.

AKS Upgrade Channels

AKS auto-upgrade channels (patch, stable, rapid, node-image) provide GKE-equivalent automation. Node image upgrades are independently scheduled from Kubernetes version upgrades. For compliance requirements mandating timely OS patching, AKS's explicit node-image channel is the cleanest implementation.

Upgrades verdict: GKE's automated upgrade channels with maintenance windows are the lowest-friction production upgrade experience. AKS matches it with explicit channels. EKS requires more manual process.

Observability

GKE

Google Cloud Managed Prometheus (GMP) is natively integrated — a single toggle enables cluster metrics collection into a managed Prometheus backend. Combined with Cloud Logging for container logs and Cloud Trace for distributed tracing, GKE has the most complete managed observability stack with the least setup.

Cloud Monitoring dashboards include pre-built GKE cluster, node, and workload dashboards that are usable immediately without configuration.

EKS

No built-in observability stack. Teams choose from: CloudWatch Container Insights (AWS-native, works, KQL not required), Amazon Managed Prometheus + Amazon Managed Grafana (fully managed Prometheus stack), or self-hosted Prometheus/Grafana.

AKS

Azure Monitor for Containers + Azure Managed Prometheus + Azure Managed Grafana. First-class managed observability, enabled with a flag. More opinionated about KQL (Kusto Query Language) for log queries — teams unfamiliar with KQL face a learning curve.

Observability verdict: GKE's integrated stack (GMP + Cloud Logging) has the smoothest getting-started experience. AKS and EKS both have strong managed options but require more initial configuration.

Cost Comparison

For a representative 3-node cluster (3x 4 vCPU / 16 GB, us-east-1 / us-central1 / eastus):

At scale (20 clusters, 10 nodes each):

AKS's free control plane advantage compounds significantly at scale. GKE Autopilot pricing (per-pod) can be cheaper or more expensive than Standard depending on workload density — benchmark for your specific workload mix before committing.

Decision Framework

Choose GKE if:

• You want the most automated, least-managed Kubernetes experience (especially Autopilot)
• You're building on Google Cloud (BigQuery, Cloud Storage, Pub/Sub, Vertex AI)
• eBPF networking out of the box matters
• You want the most mature managed autoscaling without installing Karpenter

Choose EKS if:

• You're already on AWS (RDS, S3, SQS, Lambda, Bedrock)
• Your team has deep AWS IAM and EC2 expertise
• You need Karpenter's mature spot/on-demand autoscaling
• You need the widest EC2 instance type variety (especially GPU workloads)

Choose AKS if:

• You're already on Azure (Entra ID, Azure DevOps, M365, Power Platform)
• You're running many clusters and the free control plane matters
• Your compliance team requires Entra ID-based cluster authentication
• Windows containers are a requirement

Don't choose based on: marketing claims about "Kubernetes compliance scores" or benchmark comparisons that don't reflect your workload. Choose based on which cloud ecosystem you're already embedded in and which operational model matches your team's capacity.

Frequently Asked Questions

Is GKE really that much better than EKS or AKS?

On pure Kubernetes-feature-per-unit-of-operational-effort, yes — GKE has the head start advantage of being the platform where Kubernetes was developed. But "better" depends on context. If your entire data platform is AWS (RDS, Redshift, S3, Lambda), the integration overhead of GKE makes it worse for you even if it's technically superior in isolation.

Can I run multi-cloud across EKS and GKE?

You can deploy Kubernetes workloads (Deployments, Services) to both, but cloud-specific resources (IAM, storage classes, load balancers, ingress annotations) are not portable. Multi-cloud Kubernetes requires an abstraction layer (Crossplane, Anthos Config Management, Terraform) to manage consistently. The operational cost is high — don't do it without a clear business reason.

Which has the best spot/preemptible instance support?

All three support spot instances. GKE Spot VMs are the cleanest implementation — spot node pools are a toggle in the node pool config, and GKE handles interruption gracefully with 30-second draining. EKS with Karpenter has the most sophisticated spot strategy (multi-instance-type fallback, spot interruption handling via SQS). AKS Spot node pools work well but have fewer instance type options in some regions.

What about GKE Enterprise vs Standard vs Autopilot?

GKE Standard = you manage nodes, GKE pays for control plane. GKE Autopilot = Google manages nodes and control plane; you pay per pod. GKE Enterprise = a licensing tier that adds fleet management, Policy Controller, Config Sync, and advanced security tooling on top of either Standard or Autopilot clusters (starts at significant per-cluster/per-vCPU cost). For most teams: Standard for full control, Autopilot for minimal operations, Enterprise only if you need fleet-scale policy management.

Does the choice matter for Kubernetes itself?

No — the Kubernetes API is identical across all three platforms. kubectl apply, manifests, Helm charts, Argo CD, Flux — all work the same. The choice is about what surrounds Kubernetes: node management, identity, networking, observability, cost, and cloud-native service integration.

For the two-way comparison, see EKS vs AKS: A Production Engineer's Comparison. For Karpenter on EKS, see How to Install Karpenter on EKS. For the zero-downtime upgrade strategy that applies across all three platforms, see Kubernetes Cluster Upgrades: Zero-Downtime Strategy. For EKS-specific upgrade procedures including add-on version management and blue/green node groups, see AWS EKS Upgrades: Zero-Downtime Guide.

Choosing a cloud platform for a new Kubernetes-based product? Talk to us at Coding Protocols — we've helped teams make this decision in both greenfield and migration contexts.