EKS vs GKE vs AKS: A FinOps Cost Comparison in 2026
Choosing a managed Kubernetes platform is not just an engineering decision. It is a financial one. The three major providers each have different pricing models, different cost traps, and different optimization levers. Pick the wrong mental model, and you will spend 40% more than you need to.
This post walks through every major cost category: control plane fees, compute, egress, load balancers, storage, and observability. It closes with three real team archetypes and what each platform actually costs them per month.
The Control Plane Is Not Free (Except on AKS)
This is the most underestimated line item in managed Kubernetes. EKS and GKE both charge $0.10 per cluster per hour for the control plane. That is $73 per cluster per month, before a single pod runs.
AKS charges nothing for the control plane. Microsoft absorbs that cost entirely.
At one cluster, the difference is easy to dismiss. At five clusters, the math becomes harder to ignore.
| Platform | Control Plane / Cluster / Month | 5 Clusters / Month | 10 Clusters / Month |
|---|---|---|---|
| EKS | $73 | $365 | $730 |
| GKE | $73 | $365 | $730 |
| AKS | $0 | $0 | $0 |
Teams that follow the "one cluster per environment" pattern end up with dev, staging, QA, and production clusters per team. A 10-team engineering org can easily reach 40 clusters. On EKS or GKE, that is $2,920/month in control plane fees alone. On AKS, that is $0.
This does not mean AKS is cheaper overall. But if you are running many small clusters at low utilization, AKS's pricing model has a structural advantage that compounds at scale.
The counter-argument: EKS and GKE provide more mature networking primitives and more granular RBAC out of the box. You get what you pay for in operational capability. The question is whether your team actually uses those capabilities at the scale you are paying for.
Compute Costs: Node Pools, Spot, and the Autopilot Question
Node compute is the largest line item for most teams. Each provider offers on-demand, reserved, and spot/preemptible pricing, plus at least one serverless Kubernetes model.
Standard Node Pricing
For an m5.xlarge equivalent (4 vCPU, 16GB RAM), here is what each provider charges:
| Instance Type | On-Demand / Hour | Reserved 1yr No-Upfront / Hour | Spot / Hour (approx) |
|---|---|---|---|
| AWS m5.xlarge | $0.192 | $0.122 (36% off) | $0.058 (70% off) |
| GCP n2-standard-4 | $0.194 | $0.130 (33% off, 1yr CUD) | $0.019-0.058 (up to 91% off) |
| Azure D4s v5 | $0.192 | $0.115 (40% off, 1yr) | $0.058 (70% off) |
On-demand pricing is nearly identical across all three platforms for general-purpose instances. The gap opens in discount depth and discount mechanics.
AWS Reserved Instances require commitment upfront or monthly. GCP Committed Use Discounts (CUDs) apply automatically after purchase with no instance-type lock-in at the resource level. Azure Reserved VMs are straightforward but require matching the right VM series.
GCP Spot VMs offer the deepest discounts, up to 91% for some instance types. They are also the most aggressive about preemption. For CI/CD runners and batch jobs that tolerate interruption, this is a significant advantage. For stateful workloads, it breaks things in ways that take hours to debug.
GKE Autopilot: Per-Pod Billing
GKE Autopilot removes node management entirely. You declare pods; GKE provisions and manages the underlying nodes. You are billed for the CPU and memory your pods request, not the node capacity provisioned.
Autopilot pricing: $0.0445/vCPU-hour and $0.00498/GB-hour for pod requests.
For bursty workloads where average utilization is below 60%, Autopilot wins because you stop paying for headroom that sits idle. For workloads running at 80%+ utilization consistently, Standard mode with CUDs beats Autopilot by 20-35%.
EKS Fargate is the AWS equivalent. It charges $0.04048/vCPU-hour and $0.004445/GB-hour. That is roughly similar to GKE Autopilot but without Autopilot's node scheduling intelligence. Fargate works well for isolated jobs. For full cluster workloads, it is expensive.
AKS has no equivalent serverless mode. You manage node pools. You can use Azure Container Instances via virtual nodes for burst capacity, but this is not the same as a fully managed scheduling layer.
The Egress Tax: Where Multi-Cloud Architectures Bleed
Egress pricing is where cloud cost models diverge most sharply from engineering intuition. Outbound data is not free, and microservice architectures generate a lot of it.
| Traffic Type | AWS | GCP | Azure |
|---|---|---|---|
| Internet egress (first 10TB/mo) | $0.09/GB | $0.08/GB | $0.087/GB |
| Inter-region egress | $0.02/GB | $0.08/GB | $0.02/GB |
| Inter-AZ/zone egress | $0.01/GB | $0.01/GB | Free |
| Same-AZ traffic | Free | Free | Free |
Azure's free inter-availability-zone traffic is a genuine advantage for multi-AZ Kubernetes deployments. On AWS, spreading pods across three AZs for redundancy generates $0.01/GB per cross-AZ call. For a service mesh with 1TB/month of inter-pod traffic, that is $10/month. At 10TB, it is $100/month. This appears nowhere in initial capacity planning.
GCP inter-region egress is the most expensive at $0.08/GB. If your architecture spans multiple GCP regions (for latency or compliance), inter-region service calls add up fast. AWS and Azure inter-region egress at $0.02/GB is substantially cheaper for cross-region patterns.
The practical implication: if your workload is heavily cross-zone or cross-region, model egress costs explicitly before choosing a provider. For primarily same-zone traffic with internet egress, all three providers are within 10% of each other.
Hidden Multipliers: Load Balancers, Storage, and Observability
Load Balancers
Every production Kubernetes cluster needs at least one load balancer. The costs differ materially.
| Load Balancer Type | Base Cost / Hour | Variable Cost |
|---|---|---|
| AWS ALB (EKS) | $0.0225 | $0.008/LCU-hour (connections + bandwidth) |
| AWS NLB (EKS) | $0.0225 | $0.006/LCU-hour |
| GKE Regional Load Balancer | $0.025 | $0.008/GB processed |
| AKS Standard Load Balancer | $0.025 | $0.005/GB processed, free rules |
AWS ALB LCU pricing is opaque. LCUs are calculated from new connections per second, active connections, processed bytes, and rule evaluations. A medium-traffic API gateway can accumulate 3-5 LCUs, adding $17-35/month on top of the base $16/month. Teams often underestimate this by 2x.
AKS Standard Load Balancer has no per-rule fee, which benefits teams with complex routing rules. GKE Regional Load Balancers charge per GB processed, which is predictable but punishes high-throughput workloads.
Storage
Persistent volume costs are roughly equivalent across platforms. All three providers have SSD-backed block storage at around $0.08-0.10/GB-month.
| Storage Type | Cost / GB / Month |
|---|---|
| AWS EBS gp3 | $0.08 |
| Azure Premium SSD (P10, 128GB) | $0.10 |
| GCP SSD Persistent Disk | $0.17 |
GCP SSD persistent disk is notably more expensive. A 100GB volume costs $17/month on GCP vs $8/month on AWS. For stateful workloads with large persistent volumes, this adds up to a meaningful difference across a cluster.
Observability
This is the category most teams discover after their first bill shock.
| Platform | Free Tier | Cost After Free Tier |
|---|---|---|
| AWS CloudWatch Logs | First 5GB/month free | $0.50/GB ingested |
| GCP Cloud Logging | First 50GiB/month free | $0.50/GiB ingested |
| Azure Monitor LA | First 5GB/workspace/month free | $2.76/GB ingested |
Azure Monitor Log Analytics is 5x more expensive per GB than AWS or GCP after the free tier. For verbose workloads, this makes Azure the most expensive observability platform by a wide margin. Teams commonly address this by shipping logs to a cheaper store (Loki, Elasticsearch) rather than using the native platform logging service.
Enabling full pod logging on a 50-node cluster with moderate verbosity generates 100-200GB/month of logs. At Azure Monitor rates, that is $276-552/month just for logs.
Total Cost Scenarios: Three Team Archetypes
Archetype 1: Early-Stage Startup (1 cluster, 10 nodes, moderate traffic)
| Cost Category | EKS | GKE | AKS |
|---|---|---|---|
| Control plane | $73 | $73 | $0 |
| Compute (10x m5.xl, on-demand) | $1,382 | $1,397 | $1,382 |
| Load balancer | $35 | $45 | $40 |
| Storage (500GB) | $40 | $85 | $50 |
| Observability (50GB) | $22 | $0 | $124 |
| Egress (5TB) | $450 | $400 | $435 |
| Total / Month | $2,002 | $2,000 | $2,031 |
At startup scale with a single cluster, costs are nearly identical. AKS saves $73 on control plane but pays more for observability.
Archetype 2: Mid-Scale Team (5 clusters, 50 nodes, high service mesh traffic)
| Cost Category | EKS | GKE | AKS |
|---|---|---|---|
| Control plane (5 clusters) | $365 | $365 | $0 |
| Compute (50x m5.xl, 1yr RI) | $4,392 | $4,680 | $4,140 |
| Load balancers (5) | $175 | $225 | $200 |
| Storage (2TB) | $160 | $340 | $200 |
| Observability (200GB) | $97 | $75 | $552 |
| Egress (20TB, 30% cross-zone) | $1,860 | $1,660 | $1,740 |
| Total / Month | $7,049 | $7,345 | $6,832 |
AKS leads at mid-scale because the $365 control plane saving and cheaper Azure Reserved VMs offset the higher observability cost. GKE falls behind due to more expensive persistent storage.
Archetype 3: Enterprise (20 clusters, 500 nodes, multi-region, full observability)
| Cost Category | EKS | GKE | AKS |
|---|---|---|---|
| Control plane (20 clusters) | $1,460 | $1,460 | $0 |
| Compute (500x m5.xl, 1yr RI) | $43,920 | $46,800 | $41,400 |
| Load balancers (20) | $700 | $900 | $800 |
| Storage (20TB) | $1,600 | $3,400 | $2,000 |
| Observability (2TB) | $972 | $750 | $5,522 |
| Egress (200TB, multi-region) | $18,600 | $18,400 | $17,400 |
| Total / Month | $67,252 | $71,710 | $67,122 |
At enterprise scale, EKS and AKS converge. AKS wins on control plane and compute but gets penalized heavily by Azure Monitor. Teams that replace Azure Monitor with a self-managed logging stack push AKS costs to ~$62,000/month, making it the clear winner.
GKE's persistent storage costs and deeper spot preemption complexity make it the most expensive option at enterprise scale for stateful workloads.
Eliminating the Idle Tax Across All Three
None of the analysis above accounts for idle time. Most non-production clusters run 24/7, but engineers typically use them only during working hours. This results in up to 16 hours per day of paid compute sitting idle.
For a 10-node development cluster:
AWS EKS: $1,382/month running full-time, $461/month running 8 hours on weekdays
GKE: $1,397/month full-time, $466/month scheduled
AKS: $1,382/month full-time, $461/month scheduled
This represents a 67% cost reduction across all three platforms. The approach is straightforward: suspend the cluster outside working hours and bring it back up on a defined schedule.
A scheduling solution can handle cluster shutdown, state persistence, and automatic resumption across all three providers. While the platforms differ in implementation details, the idle cost problem and its solution remain the same.
The 2026 Verdict: Choose Your Strategy, Not Just Your Cloud
Selecting a managed Kubernetes platform is ultimately a strategic decision. It should align with your architectural priorities and operating model. Based on the data, the decision framework for 2026 is clear:
Choose AKS if your strategy prioritizes high cluster density. With no control plane fees and zero-cost inter-AZ traffic, Azure offers a compelling advantage for multi-cluster environments. However, this benefit holds only if you proactively manage observability to avoid unnecessary logging costs.
Choose EKS if you are deeply AWS native and rely heavily on Spot Instances. AWS continues to lead in mature discount mechanisms, including Reserved Instances and Savings Plans, making it ideal for teams with predictable, large scale workloads.
Choose GKE if your workloads are highly variable. GKE Autopilot’s per-pod billing model minimizes wasted capacity, making it the most efficient option for bursty, developer-driven environments.
Regardless of platform, one principle applies universally: eliminate idle non-production spend first. Addressing this alone can deliver a 30–40% reduction in total cloud costs often without requiring any architectural changes.
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