kubernetes
EKS vs GKE vs AKS in 2026 — an honest field comparison
We run all three for customers. Here's where each one quietly wins, where it loses, and the decision framework we actually use.
24 de mayo de 2026 · 10 min · por Sudhanshu K.
EKS vs GKE vs AKS in 2026 — an honest field comparison
Every Kubernetes vendor blog comparing the three managed offerings has the same problem: the author has only really used one of them, and the comparison is shaped by what their employer sells.
We don't sell a cloud — we manage Kubernetes on whichever cloud the customer chose. So we run all three (plus DOKS, for completeness) at the same time, side by side, every day. This is the honest comparison, with the caveats and the gotchas, as of mid-2026.
The headline takeaway
If you want the TL;DR before the long-form:
- GKE is the most polished Kubernetes experience. Best autoscaler, best upgrade story, best release cadence. Autopilot is genuinely useful for teams that don't want to manage nodes.
- EKS is the most flexible, has the widest ecosystem of integrations, and has the deepest IAM model. Also the most operational overhead and the most expensive control plane.
- AKS is the dark horse. Has caught up enormously since 2023; the integration with Entra ID (Azure AD) is unmatched if you're already an Entra shop.
If you have a free choice, the order we'd pick for a greenfield workload is: GKE > AKS > EKS. If your business is already in AWS, EKS — the lock-in cost of switching outweighs the operational delta.
Control plane: cost, upgrade cadence, configuration
| EKS | GKE Standard | GKE Autopilot | AKS | |
|---|---|---|---|---|
| Control plane $/month | $73 | $73 | $0 (cluster fee included in pod cost) | $0 (with Uptime SLA: $0.10/hr) |
| New K8s version timing | 3-4 months after upstream | 1-2 months | 1-2 months | 2-3 months |
| Auto-upgrade option | Yes | Yes (default) | Yes (forced) | Yes |
| Min supported version skew | 2 minor versions back | 2 minor versions back | Latest only | 2 minor versions back |
| Multi-AZ control plane | Yes (default) | Yes (regional cluster) | Yes (forced) | Yes (default in production tier) |
Hidden cost trap for EKS: the $73/month is per cluster. Five EKS clusters = $365/month before a single workload runs. GKE and AKS don't have this problem at the same scale because their free / per-hour pricing models scale better with non-prod cluster counts.
Upgrade story winner: GKE. Release channels (Rapid, Regular, Stable) plus auto-upgrade plus an excellent surge upgrade strategy means you can effectively "set and forget" your cluster version for years. EKS auto-upgrade is newer and less mature; AKS auto-upgrade is good but the release cadence trails.
Networking: where the architectures diverge most
This is where the three clouds differ most, and the differences shape what kinds of workloads run well on each.
EKS uses the AWS VPC CNI. Pod IPs are real VPC IPs. This is a huge feature if you need pods to integrate with non-Kubernetes services in the same VPC (RDS, ElastiCache, EC2 instances). It also means you can run out of IP addresses on a subnet much faster than you'd expect, because every pod consumes a real IP.
GKE uses VPC-native networking with alias IP ranges. Pods get IPs from a separate alias range, so they don't compete with VM IPs. This is the right architecture for most use cases. Network Policy enforcement via Calico or Cilium is first-class.
AKS offers two CNIs: kubenet (NAT-based, simple, limited) and Azure CNI (pod IPs from VNet, similar to EKS). Azure CNI has the same IP-pressure issue as EKS. As of mid-2026, Azure CNI Overlay (similar to GKE's alias IPs) is GA and is what we deploy by default.
Practical impact: if you're integrating heavily with managed services (RDS, ElastiCache, Aurora, etc.) directly from pods, EKS's VPC-native CNI is the most ergonomic. For pure container-on-container workloads, GKE's design is cleaner. We architect this on a per-workload basis as part of our managed AWS engagements.
Identity and IAM integration
| EKS (IRSA) | GKE (Workload Identity) | AKS (Workload Identity for Entra) | |
|---|---|---|---|
| Cluster-to-cloud auth | OIDC + IAM roles for SA | OIDC + GCP SA binding | OIDC + Federated Identity |
| User-to-cluster auth | aws-auth ConfigMap (legacy) or Access Entries (new) | Google identity → Kubernetes RBAC | Entra ID → Kubernetes RBAC |
| Local auth fallback | Static kubeconfig (discouraged) | Discouraged | Local accounts can be disabled |
IRSA on EKS (IAM Roles for Service Accounts) is the granddaddy of the workload identity pattern and is still the most flexible. Each ServiceAccount can assume an IAM role with arbitrary AWS permissions, scoped to that SA only.
GKE Workload Identity is functionally equivalent and feels slightly more polished. The binding syntax is cleaner.
AKS Workload Identity (the newer one, not the deprecated Pod Identity) reached parity in 2024 and is now solid. The Entra integration is the killer feature — your existing Entra groups can map to Kubernetes RBAC roles natively. For organizations already on Azure / Entra, this alone is worth choosing AKS for.
Autoscaling: where GKE quietly dominates
Cluster Autoscaler works on all three. But the experience of cluster autoscaling differs substantially.
GKE Autopilot is the standout. You declare your pod's resource requests; GCP provisions nodes invisibly. No node groups, no instance type choice. You're billed per-pod for vCPU/memory/ephemeral-storage. For workloads that don't need specific instance characteristics (GPU, ARM, particular SSD types), this is genuinely the future. It also handles bin-packing and spot/preemptible scheduling better than any hand-rolled autoscaler I've configured.
EKS Karpenter is the second-best story. Karpenter (originally an AWS project, now CNCF) groups workloads by their constraints and provisions nodes that fit, ignoring node group abstractions entirely. It's faster and more efficient than Cluster Autoscaler. Not as zero-configuration as Autopilot, but very flexible.
AKS uses the standard Cluster Autoscaler. Works, but feels dated next to Karpenter and Autopilot. Microsoft is reportedly working on a Karpenter-equivalent; until it ships, this is AKS's weakest area.
Cost reality check
Per identical workload (10 nodes, 8 vCPU, 32GB RAM, public LB, basic monitoring), the bills roughly land:
| List price | Common discounted price | |
|---|---|---|
| EKS (m5.2xlarge × 10) | $2,920/mo | ~$1,750/mo with 3yr RI |
| GKE Standard (n2-standard-8 × 10) | $2,310/mo | ~$1,400/mo with CUD |
| GKE Autopilot (equivalent) | $2,700/mo | $2,700/mo (no commit) |
| AKS (D8s v5 × 10) | $2,560/mo | ~$1,550/mo with reserved instances |
| DOKS (s-8vcpu-32gb × 10) | $1,200/mo | $1,200/mo (no commit) |
DigitalOcean wins on raw price by a long way, with the caveat that managed databases, advanced networking, and the IAM model are simpler-and-less-capable than the big three. For many workloads, simpler is better — which is why we've been deploying more workloads onto DOKS in 2025-26 than in any year prior.
Day-2 operations: what actually wears you down
The differences that matter after a year of running the cluster:
- EKS has the most "moving parts" — the aws-auth ConfigMap drift, IRSA configuration tedium, AMIs needing periodic rotation, and the EBS-CSI driver lifecycle. None of it is hard; all of it is constant.
- GKE has the fewest sharp edges. The killer feature is that Google upgrades the control plane and you usually don't notice. Node pools follow with surge upgrades. The rest of the cluster mostly self-tends.
- AKS sits in the middle. Less operational overhead than EKS, slightly more than GKE. The big sharp edge historically was the Calico CNI; the move to Cilium-based Azure CNI Overlay has fixed most of this.
Our decision framework
When a customer asks us "which one should I run on," we ask three questions:
-
Where does the rest of your data live? If it's in S3/Aurora/Redshift, run on EKS — the data gravity is real and cross-cloud egress is expensive. Same logic for BigQuery → GKE, and Cosmos DB / Synapse → AKS.
-
What does your identity system look like? Entra-everywhere shops should default to AKS. Google Workspace + IAM should default to GKE. AWS IAM Identity Center shops should default to EKS.
-
How much operational capacity do you have? If the answer is "we want to think about Kubernetes as little as possible," lean GKE Autopilot. If the answer is "we want maximum control and we have engineers to run it," lean EKS with Karpenter.
For most customers we end up with a mix — not by accident but by design. Different workloads have different cost / availability / latency profiles, and the cloud that's best for the data-heavy analytics platform is rarely the same cloud that's best for the customer-facing edge service. We're cloud-agnostic by design precisely because the one-cloud-everywhere story is increasingly hard to defend in 2026.
Sudhanshu K. is Principal Engineer at EdgeServers (RemotIQ Pty Ltd, ABN 91 682 628 128). He runs Kubernetes for customers across all four major clouds and politely declines to have a favourite.