EKS vs GKE vs AKS Ireland: $438 vs $73 Cost [2026]

Every platform team evaluating managed Kubernetes in 2026 eventually asks the same blunt question: does the control plane bill actually matter, or is it a rounding error next to compute, egress, and the hours an engineer spends babysitting upgrades? Amazon EKS, Google GKE, and Azure AKS all answer that question differently, and the gap between the cheapest and most expensive path through a real-world cluster can run into hundreds of euros a month before a single pod ships traffic. This comparison breaks down the actual 2026 pricing, release cadence, and operational trade-offs across all three services, using published rate cards rather than marketing copy, so Irish engineering teams evaluating their cloud computing strategy can pick a platform based on data instead of brand loyalty.

The three services look similar on a feature checklist, but the billing models diverge sharply. AKS still ships its Kubernetes control plane for free on the base tier, EKS charges a flat hourly rate that becomes punishing under extended support, and GKE sits in between with a small monthly credit that only covers specific cluster shapes. None of that tells you which platform costs less once node pools, load balancers, storage, and egress are added, which is exactly the gap this article closes.

Google · Preferred Sources

Don't miss new tech stories on Google

Add TrendinTech Ireland once in the Google app and our stories appear in your news suggestions.

Add Now

EKS vs GKE vs AKS: the 2026 pricing picture at a glance

Amazon EKS charges $0.10 per cluster-hour for standard support, which works out to roughly $73 a month for a cluster that runs continuously. That number alone makes EKS look competitive with GKE’s own $0.10 per cluster-hour Standard tier. The difference shows up once a cluster drifts out of supported Kubernetes minor versions: EKS extended support jumps to $0.60 per cluster-hour, close to $438 a month, which is about six times the standard rate. Teams that let clusters age past the supported window pay a steep premium just to stay patched.

GKE’s Standard mode matches EKS at $0.10 per cluster-hour, but Google offers a $74.40 monthly credit per billing account that effectively zeroes out the control-plane fee for one eligible zonal cluster. The catch is that the credit does not extend to every configuration; regional Standard clusters, which span multiple zones for higher availability, fall outside that free-credit coverage. A team running a single zonal dev cluster might pay nothing for the control plane, while a production team running regional clusters for resilience pays the full rate.

AKS takes the simplest approach on paper: the Kubernetes control plane itself carries no separate charge on the free tier, though Azure still bills for the virtual machines, networking, storage, and other infrastructure that sit underneath it. In one 2026 cost comparison, a three-node on-demand AKS deployment landed around $501 a month with the control plane remaining free, while equivalent three-year committed-use estimates came out to roughly $72 for EKS, $60 for GKE, and $65 for AKS. Those committed numbers are illustrative, not universal, because region, VM family, storage class, network traffic, and the length of any committed-use agreement all move the final bill.

Full specifications table: EKS vs GKE vs AKS 2026

Attribute	Amazon EKS	Google GKE	Azure AKS
Control plane cost (standard)	$0.10/cluster-hour (~$73/month)	$0.10/cluster-hour (~$73/month)	Free on base tier
Extended/legacy support cost	$0.60/cluster-hour (~$438/month)	Varies by extended channel	Paid tier for long-term support
Free credit offered	None on control plane	$74.40/month per billing account (one eligible zonal cluster)	None needed (free tier)
Serverless compute option	Fargate: ~$0.04048/vCPU-hour + $0.004445/GB-hour	Autopilot: per-pod resource pricing	Virtual nodes via ACI integration
New minor version availability	~4-8 weeks after upstream release	~2 weeks (Rapid channel)	~3-6 weeks after upstream release
Supported Kubernetes minor versions	Approximately latest 3 minor versions	Approximately latest 3 minor versions	Approximately latest 3 minor versions
Three-node on-demand estimate (illustrative)	Varies by region and instance type	Varies by region and machine type	~$501/month in one 2026 comparison
Three-year committed estimate (illustrative)	~$72/month	~$60/month	~$65/month
Native cloud ecosystem	Deep AWS service integration (IAM, VPC, ALB)	Deep GCP integration (IAM, VPC, Cloud Load Balancing)	Deep Azure integration (Entra ID, VNet, App Gateway)
Spot/preemptible node support	EC2 Spot integration via node groups	Spot VMs and Preemptible VMs	Azure Spot VM scale sets
Autoscaling approach	Cluster Autoscaler / Karpenter	Cluster Autoscaler, node auto-provisioning	Cluster Autoscaler, node auto-provisioning
FinOps cost-allocation tooling	Cost Explorer tagging, Kubecost compatible	Cost breakdown reports, Kubecost compatible	Cost Management + Kubecost compatible

A caveat that applies to every row above: these figures come from published rate cards and 2026 cost-modelling comparisons, most of which use US-dollar examples and generic VM assumptions rather than Ireland-specific price cards. Irish teams need to re-validate against the eu-west-1 (AWS Ireland), europe-west region (GCP), and North Europe (Azure) price lists, because regional pricing, VAT treatment, and data-transfer charges differ from the US figures that dominate most public comparisons.

Benchmarks: release speed and version support compared

Pricing is only half the story. The speed at which each provider ships a new Kubernetes minor version, and how long they support the version you are already running, shapes both security posture and the extended-support bill. Across three separate 2026 comparisons, GKE’s Rapid release channel consistently comes out fastest, typically delivering a new minor version to customers within about two weeks of the upstream Kubernetes release. AKS follows at an estimated three to six weeks, and EKS trails at four to eight weeks for general availability.

That gap matters more than it looks on a spec sheet. Kubernetes ships a new minor version roughly every four months, and each provider only keeps a rolling window of supported versions open before charging extended-support fees or forcing an upgrade. All three services describe their standard support window as covering approximately the latest three minor versions, though the exact cutoff dates and enforcement policies differ by provider and release channel. A team on EKS that takes two months longer than a GKE team to even receive a new version has correspondingly less runway before that version ages out of standard support.

Why release cadence changes the real cost of a cluster

Release speed is not just an operational nicety, it is a direct input into the total cost model. A cluster that cannot get onto a new minor version quickly spends more of its life inside the window where the next upgrade is overdue, and overdue upgrades are precisely what push EKS clusters into the $0.60-per-hour extended-support tier. The same logic applies in reverse on GKE: because a new version lands roughly four to six weeks sooner than on EKS, a GKE team has a correspondingly longer runway to test, stage, and roll out that version before its predecessor ages out of standard support. AKS sits in the middle of that spectrum, fast enough that most teams on a disciplined quarterly upgrade cadence never need to think about extended support at all.

For platform teams running security-sensitive workloads, this translates into a concrete operational tempo. GKE’s faster release cycle suits teams that want to patch Kubernetes-level CVEs quickly and are comfortable with a more aggressive upgrade cadence. EKS’s slower cadence buys more stability between upgrades but raises the risk of drifting into extended support territory if upgrade work gets deprioritised, given that the extended-support rate on EKS is roughly six times the standard hourly fee.

Pricing deep dive: compute, serverless, and the hidden costs

The control-plane fee is almost never the biggest line item on a Kubernetes bill. Worker-node compute dominates in nearly every real deployment. Across the three providers, a comparable four-vCPU, 16GB worker node lands somewhere between roughly $0.04 and $0.10 per hour depending on instance family, region, and whether the team buys on-demand, spot, or a committed-use discount. That range alone can swing a ten-node cluster’s monthly bill by hundreds of dollars, dwarfing any difference in the control-plane fee.

Serverless Kubernetes options complicate direct comparisons further. AWS Fargate for EKS prices at approximately $0.04048 per vCPU-hour plus $0.004445 per GB-hour, a straightforward per-resource rate that scales linearly with whatever a pod requests. GKE Autopilot takes a different approach, billing per-pod based on requested resources rather than charging for the underlying node capacity, which can be cheaper for bursty or spiky workloads but harder to forecast for steady-state traffic. AKS offers virtual-node style serverless compute through integration with Azure Container Instances, giving teams a third billing shape to model against their actual traffic patterns.

The costs that catch teams off guard tend to sit outside the Kubernetes-specific line items entirely: load balancers provisioned per Ingress object, persistent volume storage billed at block-storage rates, observability and logging pipelines that scale with pod count, and cross-zone or cross-region network egress. A FinOps-minded comparison published in 2026 described these as the real multipliers behind most Kubernetes overspend, arguing that egress charges in particular can erode any savings a team thought it had locked in by choosing the cheaper control plane.

Full pricing comparison table: control plane, compute, and extended support

Cost component	Amazon EKS	Google GKE	Azure AKS
Control plane (standard)	$0.10/hr (~$73/mo)	$0.10/hr (~$73/mo), offset by $74.40/mo credit on one eligible zonal cluster	$0/hr on free tier
Control plane (extended support)	$0.60/hr (~$438/mo)	Separate extended-channel pricing applies	Paid long-term-support tier applies
Serverless pod compute	Fargate: $0.04048/vCPU-hr + $0.004445/GB-hr	Autopilot: per-pod resource pricing	Virtual nodes via ACI
Standard worker node (4 vCPU/16GB)	~$0.04-$0.10/hr (on-demand, varies by instance)	~$0.04-$0.10/hr (on-demand, varies by machine type)	~$0.04-$0.10/hr (on-demand, varies by VM size)
Three-year committed estimate	~$72/month (illustrative)	~$60/month (illustrative)	~$65/month (illustrative)
Three-node on-demand estimate	Not directly published in comparison	Not directly published in comparison	~$501/month (illustrative, 2026 comparison)
Spot/preemptible discount availability	Yes, EC2 Spot via managed node groups	Yes, Spot VMs and Preemptible VMs	Yes, Azure Spot VM scale sets

Every figure in that table is drawn from published provider rate cards or 2026 cost-modelling articles rather than extrapolated. Treat the on-demand and committed estimates as directional rather than exact, since none of the underlying comparisons published a full Ireland-specific bill of materials. Before signing a committed-use agreement, run the actual workload’s CPU, memory, storage, and egress numbers through each provider’s own pricing calculator for the eu-west-1, europe-west1, or North Europe region specifically.

Real-world example 1: a fintech startup choosing between EKS and AKS

A Dublin-based fintech running a ten-service microservices stack on AWS already has deep investment in IAM roles, VPC peering, and ALB-based ingress. For this team, EKS is the default choice not because it is the cheapest control plane but because the integration cost of moving to a different cloud for Kubernetes would outweigh the $73-a-month control-plane fee many times over. The real decision point for a team like this is whether to let clusters drift toward the four-to-eight-week version lag EKS carries, since a compliance-driven fintech cannot afford to sit in extended support at six times the hourly rate while waiting on a regulatory audit.

Real-world example 2: a data analytics team on GKE Autopilot

A data engineering team running bursty batch-processing jobs, where workloads spike for a few hours during nightly ETL runs and sit nearly idle the rest of the day, is a textbook case for GKE Autopilot’s per-pod billing model. Rather than paying for provisioned node capacity that sits unused for eighteen hours a day, the team pays only for the resources each pod actually requests while it runs. Combined with GKE’s roughly two-week lag for new Kubernetes minor versions, this team also gets security patches faster than an equivalent EKS or AKS deployment, which matters when batch jobs touch customer financial data.

Real-world example 3: an Azure-native enterprise standardising on AKS

A large enterprise already running its identity stack through Microsoft Entra ID and its networking through Azure Virtual WAN has an obvious default: AKS, where the control plane is free and the surrounding governance tooling (Azure Policy, Microsoft Defender for Cloud) is already licensed. The illustrative $501-a-month three-node on-demand figure from the 2026 comparison data is a reasonable starting estimate for this kind of team, though a committed-use discount on the underlying VM scale sets would push that closer to the $65-a-month-equivalent range seen in the three-year committed estimates.

Real-world example 4: a multi-cloud SaaS vendor comparing egress exposure

A SaaS vendor serving customers across both AWS and Azure environments, for contractual data-residency reasons, has to run workloads on both EKS and AKS simultaneously. For this team, the control-plane fee on EKS and the free tier on AKS both become secondary to the cross-cloud egress tax: every byte of data that crosses from an EKS cluster to an AKS cluster, or back, bills as network egress on top of whatever compute and storage each cluster already costs. A 2026 FinOps-focused comparison specifically called out egress as the cost center most likely to erode savings in exactly this kind of multi-cloud architecture.

Real-world example 5: an early-stage startup testing all three before committing

An early-stage startup with no existing cloud commitment and a small, GPU-light workload is in the best position to actually test all three before picking one. Spinning up a single zonal GKE Standard cluster inside the $74.40 monthly credit window effectively removes the control-plane cost from the decision entirely while the team evaluates node pricing, Autopilot’s per-pod billing, and the roughly two-week version lag against EKS’s and AKS’s equivalent free tiers (AKS) or $73-a-month control plane (EKS standard). Running the same proof-of-concept workload across all three for a single billing cycle is the only way to see which provider’s hidden costs (load balancers, storage classes, logging) actually dominate for that specific application.

Use-case recommendations: which platform fits which team

AWS-native organisations with existing IAM and VPC investment: EKS is the lower-friction choice despite the slower four-to-eight-week version cadence, because re-platforming identity and networking elsewhere costs more than the control-plane premium.
Teams prioritising the fastest security patch cycle: GKE’s roughly two-week Rapid channel delivery makes it the strongest fit for workloads handling regulated or sensitive data where CVE response time matters.
Cost-conscious teams running steady, predictable workloads: AKS’s free control plane on the base tier removes one fixed cost entirely, which compounds across dozens of clusters in a large organisation.
Bursty or spiky batch and ETL workloads: GKE Autopilot’s per-pod billing avoids paying for idle provisioned capacity, a meaningful saving for workloads that are not busy around the clock.
Multi-cloud or hybrid-cloud SaaS vendors: all three platforms are viable, but egress cost modelling between clouds should come before control-plane cost in the decision, since cross-cloud data transfer tends to dominate the bill.
Teams that have let clusters age past supported versions: migrating off EKS extended support (roughly six times the standard hourly rate) toward a disciplined upgrade cadence on any of the three platforms will save more than switching providers.
Early-stage startups with no cloud commitment yet: GKE’s free monthly credit on one eligible zonal cluster offers the cheapest real-world sandbox to test Kubernetes operations before scaling.

Migration guide: moving a production workload between EKS, GKE, and AKS

Migrating a Kubernetes workload between managed providers is rarely a lift of the cluster itself, since the Kubernetes API surface is largely portable. The real work is in everything that sits around the API: identity and access bindings, load balancer and ingress configuration, persistent storage classes, and CI/CD pipeline targets. The following sequence reflects how most 2026 migration playbooks approach a cross-provider move.

Audit cloud-specific dependencies first. Catalogue every resource that is not plain Kubernetes: IAM roles for service accounts, cloud load balancer annotations, managed DNS records, and any provider-specific storage classes (EBS, Persistent Disk, Azure Disk).
Containerise configuration with Helm or Kustomize. Parameterise anything cloud-specific (storage class names, load balancer annotations, node selectors) so the same manifest set can target a different provider with a different values file.
Stand up the target cluster in parallel. Provision the destination EKS, GKE, or AKS cluster alongside the existing one rather than replacing it in place, so both environments can run simultaneously during validation.
Re-map identity and secrets. Translate IAM roles for service accounts (EKS), Workload Identity (GKE), or Managed Identity bindings (AKS) into the destination provider’s equivalent, and re-provision secrets through the new provider’s secret manager or an external tool like Vault.
Migrate persistent data with a dual-write or snapshot window. For stateful workloads, either run a brief dual-write period across both clusters’ storage backends or take a snapshot during a planned maintenance window, depending on how much downtime the application can tolerate.
Shift traffic gradually with DNS weighting. Use weighted DNS or a global load balancer to move a small percentage of production traffic to the new cluster first, watching error rates and latency before increasing the split.
Validate cost assumptions against the actual bill. Compare the first full billing cycle on the new provider against the illustrative estimates used during planning, since real egress and storage costs often diverge from pre-migration modelling.
Decommission the source cluster only after a full billing cycle of stability. Keep the original EKS, GKE, or AKS cluster running in a cold-standby state for at least one complete month before tearing it down, in case a rollback becomes necessary.

A minimal example of parameterising a load balancer annotation across providers in a Helm values file looks like this:

# values-eks.yaml
service:
  annotations:
    service.beta.kubernetes.io/aws-load-balancer-type: "nlb"

# values-gke.yaml
service:
  annotations:
    cloud.google.com/load-balancer-type: "Internal"

# values-aks.yaml
service:
  annotations:
    service.beta.kubernetes.io/azure-load-balancer-internal: "true"

Common migration pitfalls to avoid

The most common failure mode in a cross-provider Kubernetes migration is treating the manifest layer as the whole job and discovering the identity layer is broken only after cutover. A service account that worked fine under EKS’s IAM roles for service accounts will not automatically work under GKE’s Workload Identity or AKS’s Managed Identity, and teams that assume otherwise tend to spend their first week on the new platform firefighting authentication failures instead of validating the migration. A second common pitfall is underestimating storage migration time: moving a large persistent volume between cloud block-storage backends is rarely instant, and teams that schedule only a few hours for this step frequently blow through their maintenance window. Finally, teams consistently underestimate the egress bill generated during the migration itself, since copying terabytes of data between two different clouds, even temporarily during a dual-write period, bills as cross-cloud network transfer on top of whatever the steady-state workload costs once settled.

Security and compliance: shared responsibility across EKS, GKE, and AKS

All three managed Kubernetes services operate on a shared-responsibility model: the provider secures and patches the control plane, while the customer remains responsible for workload configuration, network policy, image scanning, and identity bindings on the worker-node side. That split matters because most Kubernetes security incidents reported across the industry trace back to misconfiguration on the customer side of that line, such as an over-permissioned service account or an exposed dashboard, rather than a flaw in the managed control plane itself. The broader lesson from recent cybersecurity incidents, including large-scale data exposure events like the Veradigm breach affecting patient records, is that identity and access misconfiguration tends to be the common thread across unrelated platforms and industries, not a single vendor’s infrastructure failing outright.

Compliance certifications and audit readiness

EKS, GKE, and AKS all carry the standard set of enterprise compliance certifications that regulated Irish and EU organisations look for, including ISO 27001 and SOC 2 coverage at the platform level, with each major cloud provider publishing its own compliance documentation and audit reports on request. None of that removes the customer’s own obligation to configure network policies, pod security standards, and audit logging correctly, since a compliant control plane sitting underneath a misconfigured namespace does not make the resulting deployment compliant. Teams operating under GDPR in particular need to pay close attention to where persistent volumes and log data physically reside, since EKS, GKE, and AKS all offer EU-region deployments (Ireland, Frankfurt-equivalent, and North Europe respectively) specifically so that regulated workloads can keep data inside the EU.

Pros and cons: Amazon EKS

Pro: Deepest integration with the AWS ecosystem, including IAM, VPC, and Application Load Balancer ingress controllers.
Pro: Fargate offers predictable per-resource serverless pricing at $0.04048/vCPU-hour plus $0.004445/GB-hour.
Con: Slowest new-version rollout among the three, at an estimated four to eight weeks after upstream release.
Con: Extended support pricing at $0.60/cluster-hour is roughly six times the standard rate, penalising teams that fall behind on upgrades.

Pros and cons: Google GKE

Pro: Fastest version availability via the Rapid channel, at roughly two weeks after upstream release.
Pro: $74.40 monthly credit per billing account can fully offset the control-plane fee for one eligible zonal cluster.
Pro: Autopilot’s per-pod billing suits bursty or unpredictable workloads better than fixed node provisioning.
Con: The free credit explicitly does not cover regional Standard clusters, so production-grade high-availability setups pay the full rate.

Pros and cons: Azure AKS

Pro: No separate charge for the Kubernetes control plane on the base tier, removing one fixed cost entirely.
Pro: Strong fit for organisations already standardised on Entra ID and Azure governance tooling.
Con: Underlying VM, networking, and storage charges still apply in full, so the “free” control plane does not mean a free cluster, as shown by the roughly $501-a-month three-node on-demand estimate.
Con: Mid-pack version availability at an estimated three to six weeks, slower than GKE but faster than EKS.

FinOps considerations: tagging, cost allocation, and avoiding surprise bills

Every one of the three platforms supports cost-allocation tagging at the cluster and node level, and all three are compatible with third-party FinOps tooling such as Kubecost for workload-level cost breakdowns. The practical difference is how much manual tagging discipline each platform demands. GKE Autopilot’s per-pod billing model gives relatively granular cost data out of the box because the provider already meters at the pod level. EKS and AKS, by contrast, rely more heavily on node-level tagging and namespace-level resource quotas to approximate per-team or per-service costs, since the underlying billing unit is the node rather than the pod.

The FinOps Foundation’s guidance on cloud cost management applies directly to Kubernetes: cost visibility has to exist before cost optimisation can happen, and a Kubernetes cluster without consistent tagging is close to impossible to attribute accurately after the fact. Teams running any of these three platforms should treat tagging policy as a day-one requirement, not a retrofit, because reconstructing historical cost attribution from node-level billing data months later is rarely accurate.

Building a monthly cost review cadence

A practical FinOps cadence for any of the three platforms starts with a monthly review that breaks the bill into four buckets: control-plane fees, compute (on-demand, spot, and committed-use blended), storage and networking, and observability or logging overhead. Teams that skip the observability bucket are often surprised by how much a logging pipeline costs once it ingests every container’s stdout at production volume, since log storage and processing fees scale with pod count and verbosity rather than with the Kubernetes-specific fees this article has focused on. Setting namespace-level resource quotas early, before a cluster sprawls across dozens of teams, also makes the monthly review dramatically easier to reconcile against the FinOps Foundation’s cost-allocation best practices.

GPU and AI workload support across EKS, GKE, and AKS

Running GPU-backed workloads, whether that is model fine-tuning, batch inference, or a RAG pipeline serving production traffic, adds a layer of cost and operational complexity on top of everything covered so far. All three providers support GPU node pools, but availability, instance variety, and bin-packing efficiency differ enough to matter for teams budgeting an AI workload on top of an existing Kubernetes estate.

Scheduling and sharing GPU capacity efficiently

EKS relies on the NVIDIA device plugin for Kubernetes to expose GPU resources to the scheduler, and teams typically pair it with Karpenter for just-in-time node provisioning so that expensive GPU instances only spin up when a pod actually requests one. GKE offers similar NVIDIA device-plugin support but adds GPU time-sharing and multi-instance GPU partitioning directly in its node pool configuration, letting several smaller workloads share a single physical GPU rather than each requiring a dedicated card. AKS supports GPU node pools through its standard VM SKU selection and integrates with the same NVIDIA tooling, though teams report slightly less first-party tooling for fractional GPU sharing compared with GKE’s native support. For any team running GPU workloads intermittently rather than continuously, the ability to scale GPU node pools to zero when idle is the single biggest lever on cost, since GPU instances carry some of the highest hourly rates on any of the three platforms by a wide margin over standard CPU nodes.

None of the three providers has published GPU-specific control-plane pricing separate from the standard cluster-hour fees already covered above, meaning the control-plane economics discussed earlier in this comparison apply identically whether a cluster runs CPU-only workloads or a mixed fleet of CPU and GPU node pools. The cost differentiation for AI workloads sits almost entirely in the GPU instance hourly rate and utilisation efficiency, not in any Kubernetes-specific surcharge.

Kubernetes version support and upgrade cadence compared

All three providers describe their standard support window as covering approximately the latest three Kubernetes minor versions, which lines up with the open-source project’s own roughly fourteen-month support window per minor release. AWS documents its EKS version support policy directly, as does Microsoft’s AKS supported-versions page and Google’s GKE release notes, and all three are worth bookmarking directly rather than relying on third-party summaries, since support windows shift periodically. The practical difference between the providers is less about how long a version stays supported and more about how quickly a new version becomes available to upgrade into. GKE’s roughly two-week Rapid-channel lag means teams on GKE have the most runway to plan and test upgrades before a version ages toward its support cutoff. EKS’s four-to-eight-week lag compresses that runway, which is part of why EKS’s extended-support fee structure exists: it is effectively a safety valve for teams that could not upgrade in time.

For platform teams in regulated industries, this cadence difference should factor directly into vendor selection. A fintech or healthtech team that needs to demonstrate patched infrastructure to an auditor on a fixed schedule benefits more from GKE’s faster release cycle than from any control-plane pricing advantage on the other two platforms.

The verdict: which managed Kubernetes service wins in 2026

There is no single winner across all three platforms, and the data above explains why. On raw control-plane economics, AKS wins outright with its free base tier, though the roughly $501-a-month three-node on-demand estimate shows that the surrounding infrastructure costs still add up quickly. On release speed, GKE wins clearly with its roughly two-week Rapid channel, nearly four times faster than EKS’s slowest estimate. On ecosystem depth for teams already committed to AWS, EKS wins by default, not because its pricing is most competitive but because the integration cost of switching clouds dwarfs any control-plane savings elsewhere.

The clearest actionable takeaway from the 2026 pricing and release data: pick the platform that matches the cloud ecosystem you are already standardised on, then spend the engineering effort you save on upgrade discipline rather than on chasing a marginally cheaper control plane. The real money sits in node compute, egress, and storage, not in the $73-a-month difference between a standard EKS and GKE control plane, and the real risk sits in version drift, where EKS’s six-times extended-support multiplier makes falling behind the most expensive mistake a team can make on any of the three platforms.

Before signing a multi-year commitment with any of the three providers, run the same proof-of-concept workload through all three for at least one billing cycle, request Ireland-specific or EU-region quotes rather than relying on the US-dollar figures that dominate most public comparisons, and build the tagging and cost-allocation discipline in from day one rather than retrofitting it once a cluster has already sprawled across a dozen teams. That combination of direct testing and FinOps discipline, more than any single pricing table, is what separates teams that stay inside budget on managed Kubernetes from teams that discover a surprise bill three months into production.

Frequently asked questions

Is AKS really free to run?
No. The Kubernetes control plane itself carries no separate charge on the AKS free tier, but the virtual machines, load balancers, storage, and networking underneath it still bill in full. A real three-node AKS deployment was estimated at roughly $501 a month on-demand in a 2026 comparison, control plane included at $0.

Why is EKS extended support so much more expensive?
EKS extended support costs $0.60 per cluster-hour versus $0.10 for standard support, roughly a six-times multiplier. It exists for clusters still running a Kubernetes minor version that has aged out of standard support, functioning as a penalty for delayed upgrades rather than a feature teams should plan to use long-term.

Does GKE’s free credit cover a production cluster?
Only in specific configurations. The $74.40 monthly credit applies to one eligible zonal Standard or Autopilot cluster per billing account, but it explicitly does not cover regional Standard clusters, which most production deployments use for multi-zone high availability.

Which provider ships new Kubernetes versions fastest?
GKE’s Rapid release channel, at an estimated two weeks after the upstream Kubernetes release. AKS follows at roughly three to six weeks, and EKS trails at an estimated four to eight weeks for general availability.

Should an Irish team worry about US dollar pricing in these comparisons?
Yes. Most published EKS, GKE, and AKS cost comparisons use US-dollar examples and generic VM assumptions. Irish teams should validate pricing against the specific eu-west-1 (AWS), europe-west1 (GCP), or North Europe (Azure) regional price lists before budgeting, since regional pricing and VAT treatment differ from the US figures most comparisons publish.

Is GKE Autopilot cheaper than standard node-based clusters?
It depends on workload shape. Autopilot bills per-pod based on requested resources, which tends to save money on bursty or idle-heavy workloads but can cost more than a well-utilised, fully-packed node pool running steady-state traffic around the clock.

Can a team migrate between EKS, GKE, and AKS without downtime?
Largely yes, if the migration runs both clusters in parallel with gradual DNS-weighted traffic shifting rather than a hard cutover. The harder part is re-mapping cloud-specific identity bindings, storage classes, and load balancer annotations, which is why most migration playbooks budget weeks, not days, for a production cutover.

Do all three platforms support the same FinOps cost-allocation tools?
Yes, all three are compatible with third-party tools like Kubecost, and all three support native tagging for cost allocation. GKE Autopilot’s per-pod billing model provides more granular cost data by default, while EKS and AKS rely more on manual namespace and node-level tagging discipline.

EKS vs GKE vs AKS: Ireland Cost Guide, $438 vs $73 [2026]