Pick the wrong managed Kubernetes service and you pay for it twice: once in cloud fees, once in engineering hours spent working around limits nobody warned you about. In 2026 that decision has gotten more expensive to reverse, because Amazon EKS, Google GKE, and Azure AKS have each pushed further into their own lane rather than converging on one standard experience. This piece lines up eks vs aks vs gke on price, SLA terms, ecosystem depth, and real operational trade-offs, using current 2025-2026 figures rather than marketing copy.
The short version: GKE now holds close to 40% of the managed Kubernetes market, EKS sits around 30%, and AKS trails at roughly 20%, according to 2025 adoption data compiled by Atmosly. That gap has narrowed and widened at different points, and it says less about which product is “best” than about which cloud a team was already committed to before they ever ran kubectl. Below, we break down the control-plane pricing, the SLA fine print, node economics, and the migration friction that actually determines total cost of ownership.
Why this comparison matters more in 2026
Cloud infrastructure spending crossed $119 billion per quarter in 2025, and AWS, Azure, and Google Cloud together account for roughly 62-68% of it depending on which quarter you sample, per Synergy Research and Statista tracking cited through late 2025 and early 2026. Statista’s Q1 2026 snapshot, sourced from Synergy Research, puts AWS at 28%, Azure at 21%, and Google Cloud at 14% of global cloud infrastructure spend. Kubernetes runs underneath a large share of that spend, and the managed-service layer, EKS, AKS, and GKE, is where teams actually make buying decisions. For broader context on how these platforms fit into the rest of the cloud computing landscape, our ongoing coverage tracks pricing, outages, and architecture shifts across all three providers.
What changed heading into 2026 is pricing structure. All three providers now charge for high-SLA control planes rather than bundling everything into compute costs, which turns “which Kubernetes service is cheapest” into a more nuanced question than it was a few years back. A team running five clusters pays a very different bill than a team running one, and that math shifts again depending on whether you need the 99.95% SLA tier or can live with the default.
Control-plane pricing: the headline numbers
As of mid-2026, Amazon EKS charges $0.10 per cluster-hour for its standard control plane, which works out to roughly $73 per month per cluster, and that price includes a 99.95% uptime SLA by default. There is no free tier for EKS clusters, a cost structure AWS has kept stable since it introduced control-plane billing.
Google’s GKE flips that model. The Standard and Autopilot control planes are free for a team’s first cluster in a project, but reaching the 99.95% SLA requires the GKE Enterprise tier, also billed at $0.10 per cluster-hour, matching EKS almost dollar for dollar once that reliability guarantee is required. A separate November 2025 pricing breakdown confirms the same math: GKE Standard runs $0.10/hour, or about $73/month, once you’re past the free allotment.
Azure AKS keeps its Standard tier control plane free with no cluster-hour charge, which has long been its main pitch to cost-conscious teams. Getting the 99.95% SLA and long-term support (LTS) windows requires the AKS Premium tier, again priced at $0.10 per cluster-hour. By mid-2026, all three clouds converge on the same $0.10/hour figure once you actually need a guaranteed SLA. The difference is what you get for free below that line.
| Metric | Amazon EKS | Azure AKS | Google GKE |
|---|---|---|---|
| Standard control plane cost | $0.10/hr (~$73/mo) | Free | Free (Standard/Autopilot) |
| 99.95% SLA tier cost | Included by default | $0.10/hr (Premium) | $0.10/hr (Enterprise) |
| Default SLA without paid tier | 99.95% | Best-effort, no formal SLA | 99.5% (Standard) |
| Estimated 2026 market share | ~30% | ~20% | ~40% |
| Estimated customer base | ~2 million | ~1.5 million | ~3 million |
| Kubernetes version adoption speed | Moderate lag | Moderate lag | Generally first to new minor versions |
| Deepest native ecosystem tie-in | IAM, VPC, ALB | Entra ID, Microsoft 365 | Google AI/ML tooling |
| Fully-managed nodes option | EKS with Fargate profiles | AKS Automatic | GKE Autopilot |
| LTS support window | Standard support cycle | Available on Premium | Available on Enterprise |
| Native multi-cluster/fleet management | Via third-party tooling | Azure Arc | GKE Fleet |
Node and worker economics beyond the control plane
Control-plane pricing is the visible number, but it’s a small fraction of most bills. Worker nodes, load balancers, egress, and storage dwarf the $73/month control-plane charge once a cluster runs real workloads. This is where FinOps teams spend most of their time, and it’s also where the three clouds diverge on default behavior rather than list price. If your org already runs Kubecost or a similar cost-optimization tool, node-level waste, not control-plane fees, is almost always the bigger lever.
EKS node economics
EKS bills EC2 instances at standard on-demand or Spot rates, and Fargate profiles let teams skip node management entirely at a per-pod premium. Because EKS integrates directly with AWS IAM, VPC, and Application Load Balancer, teams already running EC2 or Lambda workloads tend to see lower integration overhead, even though the sticker price for the control plane itself is the only one of the three that isn’t free at the entry level. Teams weighing EKS against pure serverless containers should also read our ECS vs EKS breakdown, since ECS removes the Kubernetes learning curve entirely for simpler workloads.
AKS node economics
AKS Automatic, Azure’s newer fully-managed mode, handles node provisioning and scaling without a per-cluster markup beyond the Premium SLA tier. For teams already licensing Microsoft 365 and using Entra ID (formerly Azure AD) for identity, AKS often removes a layer of IAM plumbing that EKS and GKE both require native or third-party configuration to replicate.
GKE node economics
GKE Autopilot bills per pod resource request rather than per node, which tends to reduce waste from over-provisioned nodes but can cost more per unit of compute than manually sized node pools. GKE has also generally been first among the three to support new upstream Kubernetes minor versions, since Kubernetes’ release process is still stewarded heavily by Google engineers alongside the wider Cloud Native Computing Foundation community.
Autoscaling and day-two operations
Cluster autoscaling behavior is where the three services feel most different in daily operation, even though the underlying Kubernetes primitives, Horizontal Pod Autoscaler and Vertical Pod Autoscaler, are identical across all three. EKS relies on the open-source Cluster Autoscaler by default, though Karpenter, AWS’s own node provisioning project, has become the more common choice for teams that want faster bin-packing and support for mixed instance types without maintaining separate node groups per instance shape.
AKS ships its own cluster autoscaler add-on, tightly integrated with Azure Virtual Machine Scale Sets, and AKS Automatic layers node auto-provisioning on top so teams rarely touch node pool sizing directly. GKE’s node auto-provisioning goes furthest in this direction. Autopilot removes node pool management as a concept entirely, since Google handles bin-packing, right-sizing, and node lifecycle behind the scenes and simply bills for the pod resources requested. That trade-off cuts operational overhead substantially but also removes some of the low-level tuning that platform teams running dense, latency-sensitive workloads sometimes need.
For teams standardizing on GitOps, all three support Flux and Argo CD without modification, since both tools talk to the standard Kubernetes API rather than a provider-specific extension. Where things diverge again is in how each provider handles cluster upgrades. GKE offers automated release channels (Rapid, Regular, Stable) that handle minor version bumps with minimal manual intervention, a workflow AWS and Azure both support in some form but with less granular channel control historically.
SLA fine print: what “99.95%” actually covers
A 99.95% monthly SLA allows for about 21.6 minutes of downtime per month before a provider owes service credits. That sounds tight, but the SLA only covers the control plane, the API server and etcd layer, not your workloads, your node pools, or your ingress. A control-plane outage on any of the three clouds can still take applications down even while the SLA number stays clean, because node health and application-layer failures aren’t counted against it.
EKS includes its 99.95% SLA at the standard price with no upsell required. AKS and GKE both gate that same SLA behind a paid tier, which changes the real cost comparison for any team that treats SLA-backed uptime as a requirement rather than a nice-to-have. Run the numbers for your own cluster count before assuming AKS or GKE’s free tier is the cheaper option, because once you add the Premium or Enterprise tier, all three land at the same $0.10/hour rate. For teams that have already hit a real outage on the AWS side, our AWS vs GCP vs Azure cost comparison covers how that pricing convergence plays out across the full platform, not just Kubernetes.
It’s also worth reading the SLA credit structure, not just the percentage. All three providers issue service credits on a sliding scale tied to how far uptime fell below the target, and those credits apply against future billing rather than as cash refunds. A cluster that drops to 99.0% uptime in a given month typically earns a smaller credit than one that drops below 99.5%, and the credit request process is manual on every provider, teams have to file a claim, it isn’t automatic. Factor that administrative overhead into how much weight the SLA number should actually carry in the eks vs aks vs gke decision.
Market share and adoption in 2026
According to 2025 data compiled by Atmosly and cited across multiple 2026 cloud training resources, GKE holds an estimated 40% share of the managed Kubernetes market with roughly 3 million customers, EKS holds about 30% with roughly 2 million customers, and AKS holds about 20% with roughly 1.5 million customers. Those figures track the managed Kubernetes layer specifically, not overall cloud market share, and they come from third-party aggregation rather than a single authoritative source, so treat them as directional rather than exact.
Broader cloud infrastructure share tells a related story. A Q1 2025 Canalys/Omdia estimate put AWS at 32%, Azure at 23%, and Google Cloud at 10%. By Q3 2025, Google Cloud’s share had climbed toward 13-14% on multiple trackers, and Statista’s Q1 2026 figure (28% AWS, 21% Azure, 14% Google Cloud) confirms that climb held. Google Cloud’s revenue growth has consistently outpaced AWS and Azure on a percentage basis through this stretch, which lines up with GKE’s lead in managed Kubernetes share. A March 2026 DACH-region analysis put AWS Q4 2025 quarterly revenue at $33 billion (20% year-on-year growth) against Google Cloud’s $16 billion (33% growth), underscoring that Google is growing faster off a smaller base rather than closing the gap in absolute revenue.
Ecosystem and integration differences
Each service inherits the personality of its parent cloud. EKS is AWS-native, with the deepest integration into IAM, VPC networking, and Application Load Balancer, an obvious pick for teams already running EC2, Lambda, or RDS. AWS’s overall catalog, more than 240 services as of 2025, means EKS clusters rarely operate in isolation. They plug into whatever else the account already runs.
GKE leans on Google’s strength in AI and ML tooling and tends to ship new Kubernetes features and version support ahead of the other two, a natural byproduct of Google originating the Kubernetes project before donating it to the CNCF in 2015. For teams building GPU-heavy training or inference pipelines, GKE’s native integration with Google’s ML stack is usually the deciding factor over control-plane pricing.
AKS wins on Microsoft ecosystem depth. Entra ID integration, Azure Arc for hybrid and multi-cluster fleets, and tight coupling with Microsoft 365 and Azure DevOps make AKS the default choice for enterprises already standardized on Microsoft identity and compliance tooling. That default control plane also happens to be free, which keeps AKS attractive for cost-sensitive teams running a handful of clusters.
These ecosystem gravities also shape hiring and vendor support. Teams building on EKS find a deeper bench of AWS-certified engineers and third-party integrations built specifically for AWS networking and IAM. GKE shops benefit from Google’s own SRE-derived operational playbooks and the fact that upstream Kubernetes maintainers and Google’s internal platform teams overlap heavily, which shows up as faster triage on obscure scheduler or networking bugs. AKS teams lean on Microsoft’s enterprise support contracts, which many large organizations already hold for Windows Server, SQL Server, and Microsoft 365, turning AKS support into a line item that’s already budgeted rather than a new vendor relationship to negotiate.
Security and compliance defaults
Default security posture differs enough between the three that it changes onboarding time for a security team, not just the eventual configuration. EKS defaults to private API server endpoints being an opt-in setting, and IAM Roles for Service Accounts (IRSA) or the newer EKS Pod Identity require explicit setup before pods get scoped AWS permissions rather than inheriting node-level roles. Teams that skip this step often end up with pods holding broader IAM permissions than intended, a pattern flagged repeatedly in cloud IAM misconfiguration research across all three major providers, not just AWS.
AKS ties cluster security to Entra ID by default when workload identity federation is enabled, and Azure Policy for Kubernetes adds built-in compliance scanning that maps to standards like CIS benchmarks out of the box. That said, AKS has had its own run of disclosed vulnerabilities, including a CVE wave affecting multiple AKS versions that pushed Microsoft to ship broader Kubernetes security tooling to general availability in 2026. GKE, meanwhile, defaults newer clusters toward private nodes and Workload Identity Federation, and Google’s Autopilot mode enforces a hardened baseline (no privileged containers, restricted host access) that Standard mode leaves optional. GKE has not been immune either. Containerd-related CVEs surfaced on GKE nodes during 2026 and required patching outside the normal release cadence.
None of the three eliminates the need for a dedicated container security practice. Image scanning, admission control, and runtime detection all remain the operator’s responsibility regardless of which managed control plane sits underneath, a point covered in more depth in our container security hardening guide. What differs is how much of the identity and network layer the provider secures by default versus leaves to the operator to wire up.
Monitoring and observability tooling
Each provider ships a native observability stack, and each one nudges you toward staying inside its ecosystem rather than running fully open-source tooling. EKS integrates with CloudWatch Container Insights, which adds a per-metric cost on top of standard CloudWatch pricing and can get expensive fast on high-cardinality workloads with many short-lived pods. AKS pairs with Azure Monitor and Container Insights for AKS, billed by data ingestion volume, with similar cost dynamics to CloudWatch. GKE includes Google Cloud Operations (formerly Stackdriver) with a more generous free ingestion tier on Autopilot clusters specifically, which is one more reason Autopilot has gained traction with teams that don’t want to run and maintain a separate Prometheus and Grafana stack.
Most platform teams running any of the three at meaningful scale end up layering open-source Prometheus and Grafana on top anyway, since native tooling tends to be strong on infrastructure metrics but weaker on custom application-level tracing. All three clouds support the OpenTelemetry Collector, so instrumentation code stays portable even when the backend dashboard doesn’t. That portability matters more than it sounds. It’s usually the deciding factor in whether a later cloud migration takes weeks or months, since observability code that assumes a vendor-specific SDK has to be rewritten cluster by cluster.
Real-world usage patterns
Five patterns show up repeatedly in how platform teams actually choose between EKS, AKS, and GKE once they get past the pricing sheet and into real deployment planning.
- An e-commerce company already running its checkout flow on Lambda and DynamoDB adds EKS for a new microservices layer that needs finer-grained orchestration than Lambda alone provides, keeping IAM policy management in one place instead of splitting it across two AWS identity models.
- A machine learning startup training models on Google’s TPU and GPU infrastructure runs GKE Autopilot for inference serving, leaning on Google’s faster Kubernetes version cadence to pick up scheduler improvements early and avoid backporting fixes manually.
- A regulated financial services firm standardized on Microsoft 365 and Entra ID deploys AKS specifically because its compliance team already has audit tooling built around Azure AD, and the free Standard control plane keeps a dozen dev/test clusters cheap while Premium covers the three production clusters that need the SLA.
- A media company running multi-cloud for redundancy uses GKE Fleet to manage clusters across regions from one control surface, something EKS and AKS both require third-party tools like Rancher or a custom control plane to approximate.
- A mid-size SaaS vendor migrating off self-managed Kubernetes on bare metal chooses AKS Automatic specifically to avoid hiring a dedicated platform engineer, trading some low-level control for the free control plane and largely hands-off node management.
None of these five teams picked their platform by reading a feature matrix top to bottom. Each one mapped the eks vs aks vs gke decision to a constraint they already had, existing cloud spend, an identity provider, a data science stack, and let that constraint drive the choice rather than chasing whichever service scored higher on a generic checklist.
Migration guide: moving between EKS, AKS, and GKE
Kubernetes’ portability is real but not absolute. Workload manifests, Deployments, Services, ConfigMaps, generally move between EKS, AKS, and GKE with minimal changes. The friction shows up in three places: identity and access management, storage classes, and ingress controllers.
Start any migration by auditing IAM bindings. EKS uses IAM roles for service accounts (IRSA) or the newer EKS Pod Identity, AKS uses workload identity federation with Entra ID, and GKE uses Workload Identity Federation with Google service accounts. None of these map one-to-one, so budget real time for rewriting access policies rather than treating it as a find-and-replace exercise.
Next, check storage classes. Persistent volume claims backed by EBS, Azure Disk, or Google Persistent Disk all use different provisioner names and default reclaim policies. Re-point StorageClass objects before migrating stateful workloads, and test failover behavior in a staging cluster first, since default reclaim policies differ across providers and can silently delete volumes you meant to keep.
# Example: checking current context and switching between managed clusters
kubectl config get-contexts
# Point kubectl at an EKS cluster
aws eks update-kubeconfig --name my-cluster --region us-east-1
# Point kubectl at an AKS cluster
az aks get-credentials --resource-group my-rg --name my-cluster
# Point kubectl at a GKE cluster
gcloud container clusters get-credentials my-cluster --zone us-central1-a
# Compare node counts and versions across contexts before cutting over
kubectl --context=eks-cluster get nodes -o wide
kubectl --context=aks-cluster get nodes -o wide
kubectl --context=gke-cluster get nodes -o wideFinally, budget time for ingress controller differences. AWS Load Balancer Controller, Azure Application Gateway Ingress Controller, and GKE’s native Ingress-GCE all expose slightly different annotation sets. If you run NGINX Ingress instead to keep configuration portable, verify the version against known CVEs before cutover, since ingress-nginx has had multiple disclosed flaws in 2026.
Plan the cutover itself in stages rather than a single flag day. Stand up the target cluster alongside the source, mirror traffic to it with a weighted DNS or load balancer split, and watch error rates for at least a full business cycle, a week minimum, before decommissioning the old cluster. Database and stateful workloads need extra care: replicate data ahead of the cutover rather than relying on a live migration tool, and rehearse the rollback path before you need it, not after. Teams that skip the staged rollback plan are the ones who end up firefighting a stateful service migration in production instead of catching the problem in staging.
Pricing at scale: a worked example
Consider a team running 4 production clusters, all needing the 99.95% SLA tier, plus 6 smaller dev/test clusters that can tolerate best-effort uptime.
| Scenario | EKS monthly control-plane cost | AKS monthly control-plane cost | GKE monthly control-plane cost |
|---|---|---|---|
| 4 SLA-backed production clusters | ~$292 (included SLA) | ~$292 (Premium tier) | ~$292 (Enterprise tier) |
| 6 dev/test clusters, no SLA needed | ~$438 (no free tier) | $0 (Standard tier) | $0 (Standard/Autopilot) |
| Total monthly control-plane spend | ~$730 | ~$292 | ~$292 |
This is the scenario where AKS’s and GKE’s free Standard tier pays off directly: any team running a meaningful number of low-priority clusters saves real money by keeping them on the free tier and only paying for SLA where it’s actually needed. EKS’s all-or-nothing pricing removes that flexibility, which is a fair trade for teams that want SLA guarantees everywhere by default but a real cost for teams with a long tail of throwaway clusters.
Scale the same math to a larger organization running 20 or 30 clusters across business units, a common pattern at enterprises that let individual product teams provision their own environments, and the gap widens fast. A company standardized on EKS with 25 clusters, even if only 8 genuinely need the SLA tier, still pays the $0.10/hour rate on all 25 because there’s no free fallback. The identical fleet on AKS or GKE pays that rate only on the 8 that need it, keeping the other 17 free. Over a year, that’s the difference between roughly $21,900 (EKS, all 25 clusters billed at $73/month each) and around $7,008 (AKS or GKE, only 8 clusters billed at the paid tier), based on the $0.10/hour rate applied across a standard 730-hour month. Numbers like that are exactly why platform teams running many small clusters increasingly default to AKS or GKE for anything that isn’t customer-facing production traffic, and reserve EKS clusters for workloads where the AWS ecosystem integration outweighs the control-plane premium.
FinOps considerations for multi-cloud Kubernetes teams
Organizations running Kubernetes across more than one of these three clouds face a specific FinOps problem: cost allocation tooling doesn’t speak a common language by default. EKS cost data flows through AWS Cost Explorer and Cost and Usage Reports, AKS through Azure Cost Management, and GKE through Google’s Cloud Billing export to BigQuery. None of these natively tags spend the same way, which is why open-source tools like Kubecost or OpenCost have become common regardless of which cloud a team runs on, since they normalize cost data at the namespace and label level instead of the provider’s billing schema.
The FinOps Foundation’s own guidance, reflected in the broader shift toward AI-driven cost tracking that has swept the industry through 2026, points to the same conclusion across clouds: teams that tag workloads consistently at deploy time catch cost overruns weeks earlier than teams that try to reconstruct spend after the fact from raw billing exports. That’s true whether the underlying cluster is EKS, AKS, or GKE. The provider doesn’t matter nearly as much as whether cost visibility gets built into the CI/CD pipeline from day one.
One practical habit worth adopting regardless of provider: review Reserved Instance, Savings Plan, or Committed Use Discount coverage against actual node utilization monthly, not annually. Kubernetes workloads scale up and down constantly, and a commitment sized for last quarter’s baseline often overshoots or undershoots by enough to matter once a cluster’s traffic pattern shifts.
Pros and cons
Amazon EKS
- Pros: SLA included by default, deepest AWS ecosystem integration, largest managed Kubernetes install base by raw enterprise count in many AWS-heavy industries, mature IAM and networking tooling.
- Cons: No free tier for any cluster, control-plane cost adds up fast with many small clusters, slower to adopt new Kubernetes minor versions than GKE.
Azure AKS
- Pros: Free Standard tier control plane, tight Entra ID and Microsoft 365 integration, AKS Automatic reduces operational overhead for smaller teams, Azure Arc extends fleet management to hybrid environments.
- Cons: No SLA at all on the free tier, smallest market share of the three at roughly 20%, historically slower Kubernetes version rollout than GKE.
Google GKE
- Pros: Largest managed Kubernetes market share at roughly 40%, fastest new-version adoption, best native fleet management via GKE Fleet, strongest AI/ML tooling integration, free Standard and Autopilot control planes.
- Cons: SLA gated behind the Enterprise tier just like EKS and AKS Premium, Google Cloud’s overall market share still trails AWS and Azure, so third-party tooling support sometimes lags.
Use-case recommendations
- Already running EC2, Lambda, or RDS at scale: choose EKS. The IAM and networking integration outweighs the lack of a free tier.
- Heavy ML/AI training or inference workloads: choose GKE. Google’s tooling and faster Kubernetes version support matter more than a few dollars of control-plane cost.
- Regulated enterprise already standardized on Microsoft identity: choose AKS. Entra ID integration and Azure Arc reduce compliance overhead significantly.
- Running many small dev/test clusters on a tight budget: choose AKS or GKE Standard, both keep the control plane free where EKS charges regardless of SLA needs.
- Multi-region, multi-cluster fleet with centralized management: choose GKE for GKE Fleet’s native tooling, or budget for third-party fleet management like Rancher if staying on EKS or AKS.
- Migrating off self-managed Kubernetes with a small platform team: choose AKS Automatic or GKE Autopilot, both minimize node-level operational work compared to EKS’s more manual Fargate or EC2-based node management.
The verdict
There’s no universal winner in the eks vs aks vs gke comparison, and the data backs that up rather than contradicts it. GKE leads on market share (about 40%), version adoption speed, and free-tier generosity, which explains why it’s growing fastest among the three. EKS leads on included SLA and AWS ecosystem depth, which explains why it still commands roughly 30% share despite charging for every cluster. AKS trails in share at around 20% but wins decisively for Microsoft-standardized enterprises, where the free control plane and Entra ID integration cut real operational cost.
If forced to rank the three for a generic, cloud-agnostic team with no prior lock-in, the honest answer skews toward GKE on raw numbers: fastest growth, largest share, free Standard tier, and the shortest wait for new Kubernetes features. But “generic team with no lock-in” describes almost nobody. Most engineering orgs already have a dominant cloud, and that existing footprint, not this comparison, should settle the eks vs aks vs gke question in practice.
The practical takeaway: don’t pick a managed Kubernetes service based on control-plane pricing alone, since all three converge on $0.10/hour once SLA guarantees are in play. Pick based on which cloud already holds your identity provider, your data pipeline, and your existing compute footprint. Switching clouds later is possible but expensive in engineering hours, not just dollars, given the IAM and storage-class rework a real migration demands.
Frequently asked questions
Is EKS, AKS, or GKE cheapest?
It depends on cluster count and SLA needs. AKS and GKE are cheaper for teams running many low-priority clusters since their Standard tiers are free. EKS is cost-competitive, and arguably cheaper overall, for teams that need SLA guarantees on every cluster, since that guarantee is included rather than a separate $0.10/hour upsell.
Which managed Kubernetes service has the largest market share in 2026?
GKE, with an estimated 40% share of the managed Kubernetes market based on 2025 Atmosly adoption data, ahead of EKS at roughly 30% and AKS at roughly 20%.
Do EKS, AKS, and GKE all support the same Kubernetes API?
Yes, all three run upstream, conformant Kubernetes, so core manifests and the kubectl API surface are portable across them. Differences show up in IAM integration, storage provisioners, and ingress controllers, not the core API.
What does the 99.95% SLA actually cover?
Only the control plane, the API server and etcd. It does not cover node health, workload availability, or ingress uptime, so an SLA-backed cluster can still experience application downtime that isn’t covered by service credits.
Can I migrate workloads directly between EKS, AKS, and GKE?
Most Deployment and Service manifests move with minimal changes. IAM bindings, storage classes, and ingress controller configuration all need to be rewritten for the target provider, which is usually the bulk of migration effort.
Is GKE Autopilot cheaper than EKS Fargate?
Both bill per-pod resource requests rather than per-node, and both remove node management overhead. Exact cost depends on workload shape and region, so benchmark actual pod resource requests against both pricing calculators before committing, since neither is a flat percentage cheaper in every scenario.
Does AKS really have no SLA on the free tier?
Correct. The AKS Standard tier control plane is free but comes without a formal uptime SLA. Teams that need a contractual guarantee have to move to the Premium tier at $0.10 per cluster-hour, the same rate EKS and GKE charge for their SLA-backed tiers.
Why is Google Cloud growing faster than AWS and Azure in percentage terms?
Google Cloud is growing off a smaller base, roughly 14% of the global cloud infrastructure market as of Q1 2026 versus AWS’s 28% and Azure’s 21%, per Statista/Synergy Research data. A March 2026 regional analysis recorded 26% year-on-year growth for Google Cloud against 20% for AWS in the same period, though AWS’s absolute revenue remains far larger.
