EKS vs GKE vs AKS in 2026: Which Managed Kubernetes Should You Pick?
By Deepak Verma · June 23, 2026 · 9 min read
TL;DR
- EKS is the right default if you’re already on AWS — deepest integration with IAM, VPC, and the AWS ecosystem, largest third-party tooling market, but you assemble more yourself.
- GKE is the most managed experience — Autopilot eliminates node management, Fleet handles multi-cluster, and the control plane is free. Best if you want fewer operational decisions.
- AKS is the natural pick if you’re an Azure shop — tight integration with Entra ID (Azure AD), Azure DevOps, and the Microsoft ecosystem, free control plane at standard tier.
- All three are production-ready. The decision is driven by which cloud you’re already on, not by Kubernetes feature gaps.
The question behind the question
Most teams asking “EKS or GKE?” have already decided on Kubernetes — what they’re actually deciding is which cloud to commit to, or whether their existing cloud provider’s Kubernetes offering is good enough. The Kubernetes API is the same across all three; the differences are in everything around it: networking, identity, autoscaling, observability integration, and the depth of the managed-service wrapper.
If you’re already running production workloads on AWS, the answer is almost always EKS. If you’re on Azure, it’s AKS. The only time the question gets interesting is when you’re starting fresh, migrating clouds, or evaluating whether GKE’s operational advantages justify a cloud switch.
Side-by-side comparison
Control plane pricing
| Provider | Control plane cost | SLA tier |
|---|---|---|
| EKS | $0.10/hr (~$73/mo) per cluster | Included — 99.95% uptime SLA |
| GKE | Free (Standard and Autopilot) | Enterprise tier: $0.10/hr for 99.95% SLA |
| AKS | Free (Standard tier) | Premium tier: $0.10/hr for 99.95% SLA with LTS |
GKE and AKS both offer free control planes at the base tier — a real advantage if you’re running multiple clusters (dev, staging, prod). EKS charges for every cluster regardless. At scale (5+ clusters), this adds up to $4,000+/year.
Compute and autoscaling
EKS uses Karpenter (or the legacy Cluster Autoscaler) for node provisioning. Karpenter is the most flexible autoscaler across all three clouds — it provisions right-sized nodes from a wide instance selection, consolidates aggressively, and supports spot and on-demand in the same node pool. Karpenter originated on AWS and has the deepest integration there.
GKE Autopilot eliminates node management entirely — you declare pods, Google provisions and manages the nodes. You pay per pod resource (CPU, memory, ephemeral storage), not per node. GKE Standard mode uses the built-in cluster autoscaler, which is mature but less flexible than Karpenter.
AKS uses the Kubernetes Cluster Autoscaler natively, with Karpenter support in preview (as of mid-2026). AKS also supports KEDA (Kubernetes Event-Driven Autoscaling) as a managed add-on for scaling based on queue depth, HTTP traffic, or custom metrics.
When this matters: If you want zero node management, GKE Autopilot is the most hands-off option. If you want maximum control over instance selection and bin-packing, Karpenter on EKS is the most mature. AKS is catching up with Karpenter preview but isn’t production-ready there yet.
Networking
EKS uses the VPC CNI by default — each pod gets a real VPC IP address, which means native VPC security groups and routing work at the pod level. The downside: IP address exhaustion in large clusters is a real operational concern (secondary CIDR blocks or prefix delegation help, but add complexity).
GKE uses VPC-native networking with alias IP ranges by default. IP exhaustion is less of a concern because GKE manages the ranges more efficiently. GKE also has native support for multi-cluster networking via Fleet and Anthos Service Mesh.
AKS supports both Azure CNI (pod gets a VNET IP) and kubenet (pods on an overlay, nodes get VNET IPs). Azure CNI Overlay mode (GA since 2024) gives the best of both — pods use an overlay but can still reach VNET resources natively, avoiding the IP exhaustion problem.
When this matters: If your workloads need to communicate with non-Kubernetes resources in the same VPC/VNET (RDS, Elasticache, Azure SQL), the native CNI approach of EKS and AKS makes firewall rules simpler. If you’re running large clusters (500+ nodes), GKE’s IP management is the least painful.
Identity and access
EKS uses IAM Roles for Service Accounts (IRSA) or the newer EKS Pod Identity — pods assume IAM roles without static credentials. Integrates with AWS SSO and any OIDC provider.
GKE uses Workload Identity Federation — pods impersonate Google Cloud service accounts. Tight integration with Google Cloud IAM and Google Workspace for user access.
AKS uses Microsoft Entra Workload ID (formerly Azure AD Workload Identity) — pods use federated tokens to access Azure resources. Integrates natively with Entra ID for both user RBAC and workload identity.
When this matters: All three approaches solve the same problem (no static credentials in pods). The difference is which identity provider your organisation already uses. If your team manages users in Azure AD / Entra ID, AKS is frictionless. If you’re in AWS IAM, EKS. If Google Workspace, GKE.
Add-on ecosystem
EKS has the largest third-party ecosystem — every CNCF tool, every observability vendor, and every CI/CD platform has first-class EKS support. AWS maintains EKS Add-ons (CoreDNS, kube-proxy, VPC CNI, EBS CSI) as managed lifecycle components, but most real-world clusters need 5-10 additional tools you install and manage yourself (ArgoCD, cert-manager, external-dns, Prometheus, etc.).
GKE is the most batteries-included — Config Sync (GitOps), Anthos Service Mesh (Istio), Binary Authorization, GKE Backup, and Cloud Logging/Monitoring are all first-party, managed add-ons. Less ecosystem assembly required, but less flexibility to swap components.
AKS sits in the middle — managed add-ons for monitoring (Container Insights), GitOps (Flux, as a managed extension), KEDA, Open Service Mesh, and Azure Policy for Kubernetes. Microsoft’s ecosystem is strong but narrower than AWS’s third-party breadth.
When this matters: Teams that want to assemble their own stack from best-of-breed tools prefer EKS. Teams that want a curated, first-party experience with less integration work prefer GKE. AKS works well for teams already committed to the Azure ecosystem (Azure DevOps, Azure Monitor, etc.).
Upgrades and long-term support
EKS supports each Kubernetes version for ~14 months after release, with extended support available for an additional fee ($0.60/hr per cluster — expensive). Auto-upgrade is opt-in.
GKE supports each version for ~14 months on the Regular channel, with extended support on the Stable channel. Auto-upgrade is on by default, with maintenance windows. GKE tends to have new Kubernetes versions available first among the three providers.
AKS offers Long-Term Support (LTS) on the Premium tier — up to 2 years per version. Standard support is ~12 months. Auto-upgrade is available with configurable maintenance windows.
When this matters: Teams that can’t upgrade Kubernetes frequently (compliance, legacy dependencies) benefit from AKS Premium’s 2-year LTS. Teams that want the latest versions fastest prefer GKE. EKS is in the middle but charges heavily for extended support.
Decision matrix
| Criterion | Choose EKS | Choose GKE | Choose AKS |
|---|---|---|---|
| Already on this cloud | AWS | GCP | Azure |
| Node management | Karpenter (best autoscaler) | Autopilot (zero node ops) | Cluster Autoscaler (Karpenter in preview) |
| Control plane cost | $73/mo per cluster | Free | Free |
| Most managed experience | ✓ (Autopilot + Fleet) | ||
| Largest tooling ecosystem | ✓ | ||
| Best for large enterprises | ✓ (most Fortune 500 adoption) | ✓ (Entra ID + M365 integration) | |
| Long-term version support | Paid extended support | ~14 months | LTS: 2 years (Premium) |
| Multi-cluster native | Manual (ArgoCD/Flux) | Fleet (built-in) | Azure Arc (built-in) |
Real-world example
A payments infrastructure startup asked us to set up their first production cluster. They were AWS-native (RDS, S3, SQS already in use), so EKS was the obvious choice. The Kubernetes Readiness Playbook delivered a 3-node EKS cluster with Karpenter, ArgoCD, Prometheus/Grafana, and IRSA-based pod identity in three weeks. Six months later they’re running 12 services across two environments, and the Karpenter configuration we set up has kept their node count 30% lower than it would be with the default Cluster Autoscaler.
A different client — iCardio.ai, a medical-AI platform running deep learning models against a 200M+ image echocardiogram dataset — chose GKE for the opposite reason. They were migrating off ECS and needed direct control over node pools for continuous ML training workloads, plus tight integration with BigQuery and Kubecost for real-time cost visibility. Standard GKE (not Autopilot) gave them that control while still being meaningfully less node-ops overhead than the ECS setup they were leaving behind — 132 TB of imaging data moved from AWS to GCP with zero downtime during the transition.
Trade-offs and what we’d avoid
- Don’t switch clouds for Kubernetes. If you’re running production on AWS and considering GKE because Autopilot sounds better, the cost of migrating your entire ecosystem (IAM, networking, CI/CD, monitoring) dwarfs any operational savings from a slightly better autoscaler.
- Don’t run multi-cloud Kubernetes unless you genuinely need it. The complexity is real, the tooling is immature, and “avoiding vendor lock-in” is not a business requirement — it’s a fear. Pick one cloud, run it well.
- Don’t ignore the control plane cost at scale. Five EKS clusters (dev, staging, prod, data, platform) cost $4,400/year in control plane fees alone. GKE and AKS standard tier: $0. Factor this in early.
- Don’t assume Autopilot is always cheaper. GKE Autopilot charges a premium per pod-vCPU-hour compared to Standard mode. For large, predictable workloads, Standard mode with committed use discounts is often cheaper.
What to do next
- If you’re choosing a cloud for the first time — the free Infrastructure Assessment evaluates your workload profile and recommends the right provider and cluster architecture before you commit.
- If you know your cloud and need Kubernetes set up — the Kubernetes Readiness Playbook ($2,500, ~3 weeks) works on EKS, GKE, or AKS — same scope, same price.
- If you’re evaluating a cloud migration that includes Kubernetes — see Cloud Migration or book a 30-minute call to scope the migration before committing.
- If cost is the deciding factor — see Cloud Cost Optimization for control-plane fee modelling, right-sizing, and Spot/Reserved mix across EKS, GKE, and AKS.
Related reading: ArgoCD vs Flux in 2026 — once you’ve picked a cloud, the next decision is which GitOps tool to wire up.
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