EKS vs GKE vs AKS 2026: Pricing and GPU Comparison

All three major cloud providers offer managed Kubernetes. All three are production-ready. The right choice depends on your existing cloud, your workload profile, and what you actually need from the control plane.

This comparison is based on running production workloads across all three, not on marketing feature lists.

Feature comparison

Pricing deep dive

Control plane

Compute (3-node cluster, 2 vCPU / 8 GB each)

GKE is cheapest for small clusters because of the free control plane and lower compute pricing. At scale, the compute cost differences narrow and the ecosystem matters more.

GPU pricing (single H100 80GB instance, on-demand)

GPU pricing is roughly comparable. Availability is the real differentiator — check instance quotas in your target region before committing.

GPU and AI workloads

This is where the providers diverge the most in 2026:

EKS for AI

• Karpenter auto-provisions GPU nodes in seconds (no Cluster Autoscaler delays)
• Inferentia/Trainium custom chips for cost-efficient inference and training
• SageMaker integration for managed training and model endpoints
• EFA (Elastic Fabric Adapter) for multi-node GPU communication
• P5e instances with H200 GPUs (latest)

# Karpenter NodePool for GPU workloads
apiVersion: karpenter.sh/v1
kind: NodePool
metadata:
  name: gpu-inference
spec:
  template:
    spec:
      requirements:
        - key: node.kubernetes.io/instance-type
          operator: In
          values: ["p5.48xlarge", "g5.xlarge", "g5.2xlarge"]
        - key: karpenter.sh/capacity-type
          operator: In
          values: ["on-demand", "spot"]
      nodeClassRef:
        name: default
  limits:
    nvidia.com/gpu: "64"

GKE for AI

• TPU v5p — Google’s custom AI accelerators, not available elsewhere
• GKE Autopilot with GPU — just request GPU pods, Google manages the nodes
• Vertex AI integration — managed training, serving, and pipelines
• GCS FUSE for high-throughput model storage access
• Multi-host TPU slices for large model training

# GKE Autopilot GPU request
apiVersion: apps/v1
kind: Deployment
spec:
  template:
    spec:
      nodeSelector:
        cloud.google.com/gke-accelerator: nvidia-l4
      containers:
        - name: inference
          resources:
            limits:
              nvidia.com/gpu: 1

AKS for AI

• KAITO (Kubernetes AI Toolchain Operator) — deploy LLMs with a single CRD
• Azure OpenAI integration for GPT models alongside your cluster
• ND-series H100/A100 instances with InfiniBand
• Azure Machine Learning managed endpoints

# KAITO — deploy an LLM with one resource
apiVersion: kaito.sh/v1alpha1
kind: Workspace
metadata:
  name: llama3-inference
spec:
  resource:
    instanceType: "Standard_NC24ads_A100_v4"
    count: 1
  inference:
    preset:
      name: "llama-3-70b-instruct"

My take: GKE leads for AI if you want TPUs or the tightest Kubernetes-native experience. EKS leads if you want Karpenter’s GPU scheduling speed. AKS leads if you want the simplest LLM deployment path (KAITO).

Networking

GKE has the best networking out of the box — Dataplane V2 (Cilium) provides network policy, observability, and eBPF-based datapath without add-ons. EKS and AKS require additional components for equivalent functionality.

Security

All three meet enterprise security requirements. AKS has an edge in government/defense workloads with IL5 certification. GKE has the most integrated security posture management.

Decision framework

Choose EKS if:

• Your organization is already on AWS
• You need Karpenter for fast, intelligent node provisioning
• You want Inferentia/Trainium for cost-efficient AI inference
• You need EKS Anywhere for on-premises Kubernetes with AWS APIs
• You want Graviton (ARM) instances for compute savings

Choose GKE if:

• You want the least operational overhead (Autopilot)
• You need TPUs for large-scale AI training
• You value the best pure Kubernetes experience (Google invented it)
• You want built-in Cilium networking without add-ons
• You need 15,000-node clusters

Choose AKS if:

• Your organization is a Microsoft/Azure shop
• You need Windows container support alongside Linux
• You want KAITO for one-click LLM deployment
• You need Azure AD/Entra ID integration
• You want the cheapest entry point (free control plane, no premium required)

Multi-cloud

If you are genuinely multi-cloud, standardize on open-source tooling (Crossplane, ArgoCD, Cilium) and treat each provider as a compute substrate. The managed Kubernetes differences become less relevant when your platform layer abstracts them.

Related

• Kustomize vs Helm 2026
• Internal Developer Platforms Compared
• Service Mesh: Istio vs Cilium vs Linkerd
• Kong vs Envoy vs Traefik: API Gateways
• Enterprise Kubernetes Security Checklist

Frequently Asked Questions

Which managed Kubernetes is cheapest in 2026?

EKS charges $0.10/hour for control plane. GKE Autopilot has no cluster fee. AKS has a free control plane tier. For GPU workloads, GKE typically offers best spot pricing.

Which cloud Kubernetes is best for AI/ML workloads?

GKE leads with TPU support and GPU auto-provisioning. EKS integrates with SageMaker. AKS integrates with Azure ML. All support NVIDIA GPU Operator.