EKS vs GKE

Amazon EKS vs Google GKE: choosing the right managed Kubernetes service in 2026. Deep comparison of provisioning, networking, autoscaling, and cost.

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Feature Matrix	Amazon EKS AWS managed Kubernetes with the deepest cloud-native AWS service integrations and Karpenter.	Google GKE Google's managed Kubernetes — the most operationally polished, fastest to provision, and easiest to upgrade.
Control Plane Cost EKS charges $0.60/hr for clusters running Kubernetes versions on extended support (after 14-month standard window). GKE charges the same base rate.	$0.10/hr (standard support); $0.60/hr on extended support versions	$0.10/hr per cluster (Autopilot differs)
Provisioning Speed GKE consistently provisions clusters and node pools faster than EKS.	5–12 min (new cluster)	2–5 min (fastest in class)
Upgrade UX GKE Release Channels handle control plane and node upgrades with zero manual steps.	Manual / Managed Node Groups	Release Channels (fully automated)
Node Autoscaling Karpenter on EKS is the gold standard for reactive bin-packing. GKE Autopilot abstracts nodes entirely.	Karpenter (best-in-class) or MNG	GKE Autopilot / Node Auto Provisioner
Networking (CNI) EKS VPC CNI exhausts subnet IPs quickly without prefix delegation. GKE's networking is tighter out of the box.	AWS VPC CNI — IP-per-pod, IP-hungry	VPC-native with Alias IP — efficient
IAM / Workload Identity Both map K8s service accounts to cloud IAM roles. IRSA requires an OIDC provider; GKE WI is simpler to configure.	IRSA (IAM Roles for Service Accounts)	Workload Identity Federation
Ecosystem Depth If you use heavy AWS services, EKS wins on native integration depth.	Deepest AWS service integrations (ALB, RDS, Secrets Manager, etc.)	Best-in-class K8s conformance and GCP integrations
Observability GKE's native observability is more K8s-aware. EKS users often prefer Prometheus + Grafana stack.	CloudWatch Container Insights (costly)	Cloud Logging / Cloud Monitoring (tighter K8s integration)
GPU / AI Workloads Both are solid; EKS has a wider variety of GPU instance families via AWS.	Strong (p4d, p5, Inf2 instances)	Strong (A100, H100 via GKE Standard)
Spot / Preemptible Nodes Karpenter's native spot handling with bin-packing and rebalancing is arguably superior.	Spot Instances + Karpenter disruption handling	Spot VMs + Autopilot spot pods

Overview

EKS and GKE are the two most production-proven managed Kubernetes offerings in 2026. EKS wins on AWS ecosystem depth — if you're running RDS, SQS, Secrets Manager, and ALB natively, EKS's IRSA and VPC integration are hard to replicate elsewhere. GKE wins on operational polish: faster provisioning, Release Channels for hands-off upgrades, and a networking model that doesn't exhaust your subnet IPs. Both charge $0.10/hr for the control plane, but real cost differences emerge in egress, node pricing, and tooling choices.

Where EKS Wins

EKS is the right choice when your stack is heavily AWS-native. IRSA gives every pod fine-grained IAM access without credential rotation. Karpenter — born at AWS and now CNCF-owned — is the best node autoscaler on any cloud: it provisions nodes directly (bypassing ASGs), bin-packs aggressively, and handles spot interruptions natively. If you run GPU workloads on p4d, p5, or Inf2 instances, or if your team already operates in AWS and uses services like PrivateLink, RDS Proxy, or AWS Config, EKS wins on integration depth alone.

Where GKE Wins

GKE wins on day-2 operations. Release Channels mean you never manually patch a control plane again — Google rolls upgrades automatically, with surge upgrades for node pools. Provisioning a new cluster takes 2–5 minutes vs EKS's 5–12. GKE's VPC-native networking with Alias IPs is more IP-efficient than EKS's VPC CNI without prefix delegation. For teams that want Kubernetes to feel like a managed service — not an ops project — GKE Autopilot takes this further by abstracting away node management entirely.

Migration & Switching Cost

Migrating between EKS and GKE is a meaningful undertaking. Your IAM model changes completely (IRSA → Workload Identity), your networking changes (VPC CNI → VPC-native), your ingress changes (ALB Ingress Controller → GKE Ingress or Gateway API), and your storage classes change (EBS → Persistent Disk). Plan for 4–8 weeks of infra work for a mid-size cluster. Use Velero for stateful workload backup before migration. The Kubernetes manifests themselves are portable — the cloud-specific integrations are not.

Final Verdict

For most platform teams, we recommend GKE if you're on GCP or if you want the lowest operational overhead. Choose EKS if your stack is AWS-native — the IRSA + Karpenter combination is genuinely best-in-class and the depth of AWS service integration is unmatched. Avoid EKS if your team is small and doesn't want to own networking and upgrade complexity.

The K8s Cloud Wars: EKS vs GKE vs AKS (2026 Edition)

A hands-on comparison of the three major managed Kubernetes services across provisioning, networking, cost, and day-2 ops.

Read the Blog Post

EKS vs GKE: how to choose

EKS and GKE are both managed Kubernetes, and the honest summary is that GKE does more for you and EKS gives you more to assemble. Google shipped Kubernetes and its managed service reflects that: node auto-provisioning, upgrade orchestration and a working default networking model arrive without much decision-making. EKS starts closer to bare Kubernetes and expects you to choose the CNI behaviour, the autoscaler, the ingress path and the add-on lifecycle.

That difference matters less than where the rest of your infrastructure lives. A cluster does not exist in isolation — it talks to a database, assumes a role, reads a secret and writes to object storage. Running Kubernetes in the cloud your data is not in means paying for egress and latency on every one of those calls, and running an identity bridge for the privilege.

So the real question is rarely "which cluster is better". It is whether the workload's dependencies are in AWS or in Google Cloud, and whether your team has more experience with IAM or with Google's project and IAM model.

Where the day-to-day difference shows up

Concern	EKS	GKE
Node management	Managed node groups, or Karpenter for provisioning. Auto Mode reduces this if you adopt it.	Node auto-provisioning is mature and default-adjacent. Autopilot removes nodes as a concept entirely.
Upgrades	You drive control plane and node group upgrades, and you own add-on compatibility.	Release channels handle sequencing; you pick a channel and a maintenance window.
Pod networking	VPC CNI assigns real VPC IPs to pods — excellent for integration, and a subnet-sizing problem you must plan for.	Alias IP ranges, sized when the cluster is created. Fewer surprises, less direct VPC addressability.
Workload identity	IRSA or EKS Pod Identity maps a service account to an IAM role.	Workload Identity Federation binds IAM roles to the Kubernetes service account principal directly, with Google service account impersonation as the fallback.
Cost shape	Per-cluster control plane charge plus nodes you size and own.	Per-cluster charge plus nodes, or Autopilot's per-pod resource pricing.

Autopilot is the real differentiator

GKE Autopilot is the one option with no close EKS equivalent in spirit: you stop managing nodes, Google sizes and patches them, and you are billed for what your pods request rather than for instances. For a team without a platform engineer it removes an entire category of work — node upgrades, capacity headroom, autoscaler tuning.

The trade is control. Privileged workloads, arbitrary DaemonSets, custom kernel settings and some observability agents are restricted or unavailable, and per-pod pricing can exceed well-packed self-managed nodes at scale. Autopilot suits application workloads with ordinary requirements; it is a poor fit for infrastructure-flavoured workloads that expect node access.

Decision matrix: which one fits your situation

Your situation	Use	Why
Data and services already in AWS	EKS	Egress, latency and IAM all argue against crossing clouds for the compute layer alone.
Data and services already in Google Cloud	GKE	Same argument in the other direction, plus the better managed experience.
Small team, no dedicated platform engineer	GKE Autopilot	Removes node operations entirely, which is most of the ongoing work.
Need pods addressable directly on the VPC	EKS	VPC CNI gives pods real VPC IPs, which simplifies integration with non-Kubernetes services.
Heavy GPU or ML workloads	Either, decided by hardware availability	Accelerator supply and quota in your region matters more than the control plane.
Regulated workload requiring node-level control	EKS	Full node access, custom AMIs and kernel tuning without platform restrictions.

Frequently asked questions

Is GKE genuinely easier to operate than EKS?

Yes, out of the box, and the gap is narrowing. GKE's upgrade orchestration, node auto-provisioning and defaults mean fewer decisions before a cluster is production-ready. EKS has closed distance with Pod Identity, managed add-ons and Auto Mode, but it still assumes you will assemble more of the stack yourself. If ease of operation is the deciding factor and neither cloud is required, GKE wins it.

Can I run the same manifests on both?

Mostly yes — the workload API is identical. What does not port is the surrounding integration: IAM annotations on service accounts, ingress and load balancer annotations, storage classes, and anything referencing cloud-specific resources. Budget for the glue, not the Deployments. Keeping that glue in overlays rather than in base manifests makes the difference manageable.

Which is cheaper?

Control plane charges are close enough not to decide anything; node cost dominates and depends on how well you pack and what commitments you hold. The larger cost usually hides elsewhere — cross-cloud egress if your data is in the other provider, and idle capacity from over-provisioned node groups. Model those two before comparing sticker prices.

What about multi-cloud across both?

Running the same workload on both is a real strategy for a small number of organisations and an expensive mistake for most. You take on two identity models, two networking models, two upgrade cadences and a lowest-common-denominator platform. Choose it when a contract or a genuine availability requirement demands it, not as insurance against a lock-in that mostly lives in the data layer anyway.