2026
GKE vs EKS vs AKS: Managed Kubernetes on the Three Major Clouds
All three services run Kubernetes and expose the same API. The differences are in how deeply they integrate with their cloud, where they put the operational burden and how much they abstract away. Here is how to think about each one.
Running Kubernetes yourself means operating etcd, keeping the API server healthy, managing certificate rotation, upgrading the control plane without taking the cluster down. Those are real responsibilities that require real expertise. For most teams, the control plane is not where they want to spend their operational energy.
Managed Kubernetes services hand that responsibility to the cloud provider. You get a fully functioning cluster, a stable API endpoint and an SLA-backed control plane. The worker nodes where your workloads actually run are still virtual machines you pay for, but the machinery that orchestrates them is someone else's problem.
The three services that dominate this space are GKE on Google Cloud, EKS on AWS and AKS on Azure. They all run Kubernetes. They all expose the same API. The differences are in how deeply they integrate with their respective cloud ecosystems, where they put the operational burden and how much they abstract away from you when you want them to.
What all three have in common
Before getting into the differences, the shared foundation is worth being explicit about. If you know how to run workloads on one of these, the core skills transfer to the other two.
Control plane management
All three services manage the control plane for you. The API server, etcd, scheduler and controller manager are operated by the cloud provider. You interact with the cluster through kubectl as normal, but you have no access to the underlying control plane nodes and no responsibility for keeping them running.
Node groups and pools
Worker nodes run in groups — node groups on EKS, node pools on GKE and AKS. Each group is a collection of identically sized virtual machines. You can have multiple groups in a single cluster with different machine types: a pool of CPU-optimised nodes for compute-heavy workloads alongside a pool of general-purpose nodes for everything else.
Cluster autoscaling
All three providers support automatic scaling of node counts in response to pending pods. When the scheduler cannot place a pod because no node has enough free resource, the autoscaler adds a node. When nodes are underutilised for long enough, they are drained and removed. The implementation details vary but the behaviour is consistent.
Managed upgrades
Kubernetes releases a new minor version roughly every four months. Managed services handle the upgrade process and typically support two to three versions at a time before a version reaches end of life. Upgrading is not fully automatic in most configurations, but the providers handle the mechanics of rolling the control plane and nodes to the new version.
Cloud IAM integration
Each provider has a mechanism to give pods access to cloud services without storing credentials inside the cluster. GKE uses Workload Identity, EKS uses IAM Roles for Service Accounts, AKS uses Workload Identity too, built on Azure AD federation. The pattern is the same in all three: a Kubernetes service account is bound to a cloud IAM identity and the kubelet exchanges a signed token for short-lived cloud credentials.
The three providers
Each service has a distinct character shaped by the cloud platform it lives in. Understanding that character helps when choosing between them and when debugging the behaviour that surprises you.
GKE — Google Kubernetes Engine
Google Cloud
GKE is the oldest of the three and has historically been the most polished. Google invented Kubernetes, so it is reasonable to expect their managed offering to stay closest to upstream. Autopilot mode takes abstraction further than any other provider: you describe workloads rather than nodes and Google manages the compute layer entirely. Standard mode gives you node pools you configure yourself, which is closer to what EKS and AKS offer.
The control plane is fully managed and covered by a strong SLA. Node auto-provisioning in Standard mode will create node pools with the right machine types to fit pending pods rather than requiring you to plan capacity in advance. GKE also has some of the fastest version upgrade cycles and the tightest integration with Google Cloud IAM through Workload Identity, which replaces service account key files with short-lived token exchange.
EKS — Elastic Kubernetes Service
Amazon Web Services
EKS is the most widely deployed managed Kubernetes service by pure volume of usage. AWS's breadth of services means integrations are extensive: IAM roles for service accounts, EBS and EFS for storage, ALB and NLB for ingress, CloudWatch for observability. Most teams running workloads on AWS end up on EKS because the path of least resistance into the AWS ecosystem runs through it.
The tradeoff is that EKS is historically less opinionated than GKE. You provision node groups yourself, choose your own CNI plugin, configure your own cluster autoscaler. Fargate mode removes the node management burden but has meaningful constraints around storage and networking that make it a poor fit for many stateful workloads. EKS charges separately for the control plane, which is a minor but notable difference from GKE's pricing model.
AKS — Azure Kubernetes Service
Microsoft Azure
AKS sits between GKE and EKS in terms of operational abstraction. It integrates deeply with Azure Active Directory, which matters a great deal for organisations already running their identity on Microsoft's stack. Managed node pools handle OS patching and Kubernetes version upgrades through a controlled rolling process. The Azure CNI plugin integrates pods directly into the virtual network, giving each pod a real VNet IP address rather than an overlay.
The control plane on AKS is free, which makes it attractive for smaller clusters where control plane cost is a consideration. Virtual nodes using Azure Container Instances allow sudden burst workloads to spill onto serverless compute without pre-provisioning capacity. AKS has improved significantly in reliability over the past few years, having had a rougher period in its earlier releases.
Side by side
Some of the most common comparison points in condensed form. The details shift with new releases, so treat this as directional rather than definitive.
| Dimension | GKE | EKS | AKS |
|---|---|---|---|
| Control plane cost | Free (Standard) | ~$70/month per cluster | Free |
| Default networking | VPC-native (alias IPs) | AWS VPC CNI | Azure CNI or kubenet |
| Node-free mode | Autopilot | Fargate | Virtual nodes (ACI) |
| IAM integration | Workload Identity | IRSA | Workload Identity (AAD) |
| Upgrade model | Release channels | Manual or managed node groups | Cluster upgrade profiles |
| Kubernetes closeness | Very close to upstream | Close, some AWS-specific additions | Close, some Azure-specific additions |
Networking is where the differences bite
Kubernetes networking is already complex before the cloud provider adds its own layer on top. All three services use their own CNI plugin by default, which determines how pods get IP addresses, how they route traffic between nodes and how network policies are enforced.
GKE's VPC-native mode assigns each pod an IP from the VPC's address space using alias IP ranges. Traffic between pods on different nodes goes through the VPC routing layer directly without encapsulation. EKS with the AWS VPC CNI works similarly: every pod gets a real VPC IP address assigned to a secondary IP on the node's network interface. AKS with Azure CNI also puts pods directly into the VNet, though the IP allocation model has been through several versions and the right choice depends on the cluster size and subnet planning.
All three support network policies for pod-level traffic control, but the enforcement is handled differently. GKE has Dataplane V2 based on eBPF. EKS pairs VPC CNI with a separate network policy agent, also eBPF-based. AKS supports Azure Network Policy Manager as well as Calico. The surface area varies enough that networking is often the first place where switching providers requires real rework rather than just configuration changes.
Node management
Worker nodes are virtual machines. You are responsible for the cost of those machines regardless of which provider you use. The question is how much the provider does for you in terms of keeping them healthy.
What managed node groups handle
OS patching
Provider updates the node OS on a schedule. Nodes are replaced rather than patched in place.
Kubernetes version upgrades
When you upgrade the control plane, managed node groups can roll nodes to the matching kubelet version automatically.
Node replacement on failure
Unhealthy nodes are detected and replaced. The underlying instance group handles this rather than requiring manual intervention.
Managed node groups do not make capacity planning irrelevant. You still choose the instance type, set the minimum and maximum node counts for the autoscaler and decide how to spread nodes across availability zones. What they remove is the operational work of keeping the nodes themselves in a good state once they exist.
Observability integration
Each provider has a native path for getting logs and metrics out of the cluster. GKE feeds into Cloud Logging and Cloud Monitoring. EKS ships logs to CloudWatch and metrics through the CloudWatch Container Insights agent. AKS integrates with Azure Monitor and Container Insights. All three work and all three have meaningful costs at volume.
Many teams run their own observability stack — Prometheus for metrics, Loki for logs, Grafana for dashboards — rather than the cloud-native alternative. This gives consistent tooling regardless of which provider the cluster runs on, which matters for multi-cloud setups. The tradeoff is an additional system to operate inside the cluster. The cloud-native integrations require no cluster-side installation but tie you to the provider's query interface and pricing model.
How to choose
The honest answer for most teams is that the cloud they are already on determines the answer. Migrating a Kubernetes workload between providers is achievable but carries enough friction in networking, storage, IAM and load balancer configuration to make a purely technical comparison somewhat academic. If you are starting fresh, the following factors are worth thinking through.
You are already on AWS
EKS. The IAM, networking and storage integrations are too useful to give up. Fighting to use GKE from within an AWS-centric organisation is rarely worth the effort unless there is a specific technical reason.
You want the most polished Kubernetes experience
GKE. Years of investment and direct authorship of Kubernetes itself show. Autopilot mode is genuinely well thought out for teams that want to focus on workloads rather than nodes.
Your organisation runs on Microsoft identity
AKS. Azure AD integration for cluster authentication is significantly easier than bolting an external identity provider onto EKS. If your developers already log into everything with their Azure AD credentials, AKS makes the Kubernetes access model consistent with everything else.
Cost at small scale
AKS or GKE Autopilot. Neither charges for the control plane. EKS at roughly seventy dollars per cluster per month becomes noticeable when running many small clusters for development environments.
Multi-cloud strategy
The differences between the three narrow considerably when you abstract them behind tools like Terraform, Crossplane, or a service mesh. If portability is a goal, standardise on tools that work across all three rather than leaning into provider-specific features.
The tooling layer above the cluster
One of the underappreciated aspects of using a managed Kubernetes service is that the ecosystem of tools sitting above the cluster is largely provider-agnostic. Helm works the same on all three. Argo CD treats them the same as long as it can reach the API server. Prometheus scrapes pods the same way regardless of what cloud the node is running on.
The provider-specific behaviour surfaces at the edges: when aLoadBalancer service provisions a cloud load balancer, when a PersistentVolumeClaim creates a disk, when pod annotations trigger cloud IAM token exchange. These are real differences that need handling, but they are narrow enough that a team experienced on one provider can become productive on another in days rather than months.
Tools like the AWS Load Balancer Controller, GKE's Config Connector and Azure Service Operator represent a broader trend: managing cloud resources through Kubernetes custom resources rather than through separate Terraform or CLI workflows. This is worth knowing about because it changes where the operational boundary sits. The cluster becomes the control plane for cloud infrastructure, not just for containerised workloads.
Where this leaves you
GKE, EKS and AKS are all production-grade Kubernetes services. The variance in what they offer has narrowed as each provider has responded to competitive pressure and user feedback. The days when one was clearly ahead on a particular capability for years at a time are mostly behind us.
The more useful question than which is best is which fits the context you are actually in. The cloud you are already invested in, the identity system your organisation already runs, the team's existing operational knowledge — these factors will shape the experience of running Kubernetes day to day far more than the differences in the managed service itself.
Kubernetes is hard. Managed control planes remove a real operational burden. But a managed control plane does not make pod scheduling, resource requests, network policies, persistent storage or upgrade planning easier. Those stay with you on any provider. Understanding which parts the service handles for you and which parts remain your responsibility is the foundation of running it well.