KUBERNETES COST OPTIMIZATION 2026: EKS VS AKS VS GKE PRICING AND SAVINGS
Your Kubernetes bill is probably 30-50% higher than it needs to be. Not because you chose the wrong cloud provider, but because the cost levers most teams pull first — control plane fees and instance pricing — aren’t where the real money leaks.
The hard truth: control plane costs are table stakes. Compute optimization is where the savings live.
This is not a comparison of sticker prices. It’s a guide to what you’ll actually pay, what you can cut, and how EKS, AKS, and GKE differ when the bill arrives.
Who Is This Guide For?
Engineering leads, platform engineers, and FinOps practitioners managing Kubernetes clusters in production. If you’re spending more than $5,000/month on K8s infrastructure and want a framework for cutting that by 30-50%, keep reading.
By the End of This, You’ll Know
- The exact pricing formulas for EKS, AKS, and GKE — including the hidden costs
- Which optimization levers deliver the biggest savings on each cloud
- How Karpenter, spot instances, and commitment discounts interact
- A practical FinOps workflow for continuous cost reduction
Control Plane: The Easy Comparison, The Wrong Focus
EKS charges $0.10/hour ($73/month) per cluster, always. No free tier, no credit. If you don’t upgrade your cluster within 14 months, extended support kicks in at $0.60/hour ($438/month) — a 6x premium for the same control plane.
AKS offers a free tier for the basic SKU with no control plane charge. The Standard tier adds $0.10/hour. The Premium tier (for extended support and LTS) is $0.60/hour.
GKE charges $0.10/hour for all clusters but provides a $74.40 monthly credit that fully covers one zonal or Autopilot cluster. Effectively, your first cluster is free.
These differences look significant on a spreadsheet. In practice, control plane costs account for less than 10% of your total K8s bill once you have more than a handful of nodes. The compute layer dominates.
Compute: Where the Real Money Lives
On-demand instance pricing is similar across all three clouds for equivalent instance families. The real differentiation is in how each provider enables cost avoidance.
EKS has Karpenter, which launches just-in-time nodes that match pod requirements. Unlike the classic Cluster Autoscaler, Karpenter selects from across instance families rather than a fixed node group. It can automatically fall back to spot capacity when on-demand is needed. Benchmarks show Karpenter reduces compute spend by 10-20% versus the standard autoscaler. Karpenter is also available for GKE but less mature there.
AKS offers the lowest entry cost with free control plane and competitive spot VM pricing (up to 90% off on-demand). Azure Reservations (1 or 3 year) deliver up to 72% discounts. The tradeoff: AKS lacks a Karpenter equivalent, so you’re managing node pools manually or with Cluster Autoscaler.
GKE Autopilot shifts your billing model from node-hours to pod-resource-hours. You don’t manage nodes at all — Google handles capacity management and bin packing. For variable workloads with frequent scaling, Autopilot typically saves 15-30% over Standard mode because you only pay for what your pods actually request.
Spot Instances: The Biggest Single Lever
Spot (AWS), Spot (Azure), and Preemptible (GCP) VMs offer 70-90% discounts compared to on-demand pricing. If your workloads can tolerate interruption — batch processing, stateless web services, CI/CD runners — you should be running on spot capacity.
The catch: spot availability varies. AWS has the deepest and most liquid spot market. Azure spot is comparable. GCP preemptible instances are capped at 24 hours and have lower availability.
Modern Kubernetes deployments handle spot interruptions through:
- Pod disruption budgets that ensure minimum replicas remain available
- Karpenter’s spot fallback that automatically uses on-demand when spot is unavailable
- Workload separation via taints and tolerations that reserve on-demand nodes for critical stateful services
Most teams target 60-80% spot utilization with the remainder on on-demand or reserved instances. At that ratio, compute costs drop by roughly 50%.
Commitment Discounts: Lock In After You Stabilize
Reserved instances (AWS), Reserved VMs (Azure), and Committed Use Discounts (GCP) offer 57-72% off on-demand rates in exchange for 1 or 3 year commitments.
The right sequence: Start with on-demand and spot. Let your usage stabilize for 30-60 days. Analyze your baseline using Kubecost or native cost tools. Then purchase commitments against your predictable floor, not your peak.
AWS Savings Plans apply flexibly across instance families, making them the easiest commitment to manage. GCP CUDs apply automatically but require a 1-year minimum and only offer up to 57% — lower than AWS and Azure.
Hidden Costs That Add Up
Extended support surcharges are the most painful. EKS charges $0.60/hour after 14 months — that’s $438/month per cluster. The fix is simple: upgrade your cluster before the window expires. Komodor documented a fintech team saving $365/month per cluster by automating upgrades.
NAT gateway fees catch many teams off guard. A NAT gateway in a single AZ costs about $32/month plus $0.045/GB processed. For a three-AZ setup with moderate traffic, that’s $150-300/month. AKS’s outbound type configuration and GKE’s Cloud NAT have similar cost structures.
Load balancer proliferation is a silent cost driver. Each ALB or NLB adds $18-25/month plus data processing fees. Teams often spin up one load balancer per microservice, racking up $200-500/month in LB fees alone. Consolidation via a single ingress controller (like ingress-nginx or Contour) eliminates this.
Monitoring and logging at scale. CloudWatch Logs at $0.50/GB, Azure Monitor Log Analytics at $2.30/GB, and GCP Cloud Logging at $0.50/GB add up fast. A cluster producing 100GB of logs per day costs $1,500-7,000/month just for log ingestion. Most teams don’t need that much — set log retention to 7 days for debug logs and 90 days for audit logs.
Real-World Savings
A Sedai case study on EKS optimization: A production workload running on 10 m5.large nodes was cut to a mixed spot/on-demand fleet with Karpenter, reducing compute spend by 30% and eliminating idle pods via automated shutdown.
A ScaleOps migration to GKE Autopilot: A media company moved from 6 n2-standard-4 nodes ($600/month) to Autopilot with right-sized pod requests, achieving a 45% reduction ($330/month) while maintaining SLA.
An AWS Savings Plans adoption at a SaaS provider: 2,500 EC2 instances (mix of EKS workers and other workloads) consolidated under a 3-year Compute Savings Plan delivered an average 68% discount versus on-demand.
What You Can Actually Use Today
Start with Kubecost. Deploy it on your clusters today. The free tier shows per-namespace and per-deployment cost allocation. Run its right-sizing report to identify over-provisioned workloads — most teams find pods requesting 2-5x their actual usage.
Enable spot node pools for non-production environments first. Once you’ve validated interruption handling, extend to production stateless workloads. Run at least one on-demand node pool for critical services and cluster-critical add-ons.
Set a FinOps cadence. Review cost allocation weekly, not monthly. Configure budget alerts at 50%, 75%, and 90% of your target. Automate scaling policies based on cost data rather than manual approval.
Upgrade clusters proactively. Set a reminder 60 days before the 14-month extended support deadline. Use cluster API or renovate-style tooling to automate minor version upgrades. Cloudflare’s approach of always running N-2 within the support window is a good target.