Comparison
EKS vs AKS vs GKE
A practical guide to choosing the right managed Kubernetes platform.
Comparison table
| Factor | EKS (AWS) | AKS (Azure) | GKE (Google) |
|---|---|---|---|
| Control plane cost | Per-hour fee per cluster; extended support for older versions costs more | Free tier (no SLA); Standard tier (uptime SLA, paid); Premium adds long-term support | Per-cluster management fee, partly offset by a monthly free-tier credit |
| Kubernetes version support | Standard and extended support periods per minor version | Community support window; long-term support available on Premium | Rapid, regular and stable release channels |
| Node autoscaling | Karpenter (recommended) or Cluster Autoscaler | Cluster Autoscaler; KEDA for workload-driven scaling | Cluster Autoscaler; Autopilot removes node management entirely |
| Networking model | AWS VPC CNI (pods get VPC IPs); custom CNI possible | Azure CNI (pods in VNet) or CNI Overlay | VPC-native; Dataplane V2 (eBPF) for NetworkPolicy |
| Pod identity / IAM | IRSA (IAM Roles for Service Accounts) | Workload Identity (replaces AAD Pod Identity) | Workload Identity (GCP service account binding) |
| Managed add-ons | EKS Add-ons (CoreDNS, kube-proxy, VPC CNI, EBS CSI) | Azure-managed add-ons (Azure Monitor, Defender, KEDA) | Google Cloud observability tooling; Dataplane V2 available |
| Upgrade experience | Manual trigger; managed node group rolling update | Manual or auto-upgrade; surge node pool update | Automatic via release channel |
| Best for | AWS-native workloads; broad AWS service integration | Azure-first orgs; .NET/Windows workloads; Entra ID integration | GCP workloads; reduced operational overhead (Autopilot) |
When to choose each
Choose EKS when:
- Your primary cloud is AWS and you need deep integration with AWS services (RDS, S3, IAM, SQS)
- You want Karpenter for flexible node autoscaling with Spot and bin-packing
- You need a broad selection of EC2 instance types including GPU, high-memory, and Graviton
- Your team has existing AWS expertise and tooling
Choose AKS when:
- You are an Azure-first organisation with existing Azure infrastructure
- You run Windows containers (AKS supports Windows node pools)
- You need deep Entra ID integration and Azure Policy for governance
- You want a no-cost control plane tier for development or non-critical clusters (the AKS Free tier has no uptime SLA)
Choose GKE when:
- You are on GCP or using Google Workspace/BigQuery heavily
- You want GKE Autopilot for reduced operational overhead (no node management)
- You need eBPF-based networking (Dataplane V2) without a third-party CNI
- You want automated cluster upgrades through release channels
Which managed Kubernetes service should you choose?
In most cases the right service is the one on the cloud where your data, identity and other workloads already live. Kubernetes itself is portable, but everything around it is not: networking, load balancers, storage classes, identity federation and logging all connect to the host cloud. Choosing a cluster on a different cloud from your databases usually adds latency, egress charges and a second set of security controls.
If you are genuinely free to choose, the useful questions concern your team and workloads rather than feature lists. Do you have engineers who already know one provider? Do you run Windows containers, GPU or machine learning jobs, or tightly regulated data? Do you want to manage nodes at all, or would you rather pay a premium for the provider to do so?
Managed Kubernetes also sits on a spectrum of responsibility. Standard modes give you control of node pools and require you to plan capacity and upgrades. Newer modes such as EKS Auto Mode, AKS Automatic and GKE Autopilot move more of that work to the provider in exchange for less control and a different billing model.
| Your situation | Leans towards | Reason |
|---|---|---|
| Workloads and data mainly on AWS | EKS | Closest integration with IAM, VPC, load balancers and AWS data services |
| Microsoft estate with Entra ID and Azure Policy | AKS | Identity, governance and billing already sit in Azure |
| Data and analytics on Google Cloud | GKE | Tight integration with Google data services and a mature autoscaling and upgrade model |
| Small team that wants minimal node management | Autopilot style modes on any cloud | Provider handles node lifecycle, at the cost of some flexibility |
| Multi-cloud by regulation or acquisition | A consistent layer across clusters | Standardise Terraform, GitOps and policy rather than the cluster product |
How does the cost model differ between the three?
Control plane fees are the smallest part of the bill for most clusters. The real spend is worker nodes, storage, load balancers, NAT or egress traffic and observability tooling. Comparing only the cluster fee can therefore mislead, and it is better to model a representative workload on each provider.
The fee structures do differ in ways worth knowing. EKS charges a per-cluster hourly fee, and versions that pass their standard support window move into extended support at a higher hourly rate, which turns delayed upgrades into a visible cost. AKS has a Free tier with no financially backed uptime SLA, a Standard tier with an uptime SLA suited to production, and a Premium tier that adds long term version support. GKE charges a cluster management fee that a monthly free-tier credit offsets for one zonal or Autopilot cluster per billing account, and Autopilot bills by the resources your pods request rather than by whole nodes.
Because exact rates change and vary by region, we recommend using each provider's current pricing calculator with your own node shapes, commitment discounts and traffic assumptions. Cross-zone traffic and idle capacity are the two items we most often see underestimated.
- Model compute, storage, load balancing, egress and observability together, not just the control plane.
- Check whether commitment discounts or savings plans apply to the node types you intend to use.
- Include the cost of staying on an older Kubernetes version, which may be higher than upgrading on time.
What does the choice mean for compliance and data residency?
All three providers publish compliance programmes covering common frameworks, so the practical question is usually not whether a service is certified but whether your chosen region, account structure and configuration meet your own obligations. Shared responsibility applies: the provider secures the control plane, and you secure node configuration, workload permissions, network policy and secrets.
Data residency often decides the region before it decides the provider. If you must keep personal data within the EU or within a specific country, check which regions offer the managed Kubernetes features you need, including the newer automatic modes, since feature availability can differ by region.
Whatever the platform, the hardening work is similar: private API endpoints where appropriate, workload identity instead of long-lived keys, network policies, pod security standards, encrypted secrets and audit logging to a separate account. We treat these as a baseline for every cluster and verify them against a recognised benchmark such as the CIS Kubernetes benchmark.
How hard is it to move between EKS, AKS and GKE?
Moving the workloads is the easier part when you have containerised applications and declarative manifests. Moving the dependencies is harder: managed databases, object storage, queues, container registries, DNS, certificates and identity bindings are all provider specific and need equivalents on the target cloud.
The most portable setups keep infrastructure in Terraform, application delivery in Helm or Kustomize with a GitOps controller, and avoid hard-coding provider-specific annotations in application charts. Ingress and storage class differences still require testing, and so do autoscaling behaviours, since the node autoscaling approach differs by provider.
A realistic migration runs both environments side by side, shifts traffic gradually and keeps the old cluster until the new one has handled a full release cycle. Data migration and cut-over planning usually dominate the timeline, not the cluster build itself.
What are the common pitfalls in managed Kubernetes?
The most frequent problem is treating upgrades as optional. Kubernetes minor versions have a limited support window on every provider, and falling behind forces rushed multi-version upgrades or extra cost for extended support. A regular upgrade routine with tested rollback steps is cheaper than an emergency.
Networking is the second source of surprises. On EKS and AKS with pod networking tied to the virtual network, a poorly sized subnet can exhaust IP addresses as the cluster grows, and the address space is hard to change later. Planning ranges before the first cluster is created avoids a painful rebuild.
Finally, many teams over-provision. Missing resource requests and limits, no autoscaling policy and idle node pools quietly inflate the bill. Setting sensible requests, enabling the right autoscaler for your provider and reviewing utilisation monthly is usually the largest saving available.
How would a neutral consultant approach the decision?
We begin with a short discovery of your applications, team skills, compliance obligations and existing cloud commitments. From that we produce a written recommendation with a fixed scope, delivered by a named senior engineer, and any resulting infrastructure code lives in your accounts and repositories.
Where two options are close, we suggest a time-boxed proof of concept using one real workload on each, comparing deployment effort, autoscaling behaviour, observability and running cost. This replaces opinion with evidence that your own stakeholders can review.
If you already run a cluster and are unhappy with it, the answer is often to fix configuration and operations rather than change provider. A production readiness review can show whether the problems come from the platform or from how it is set up.
Frequently asked questions
Is AKS really free?
What happens if we stay on an old Kubernetes version?
Should we use Autopilot or an equivalent automatic mode?
Can one team run clusters on more than one cloud?
Can you help if we have already chosen a provider?
Not sure which platform is right for you?
We can help you compare all three. A working session can help you reach a clear recommendation based on your cloud provider, team expertise, and workload requirements.