AKS Cost Optimization: Cut Azure Kubernetes Costs (2026)

Azure Kubernetes Service (AKS) hides its costs well — there's no cluster management fee (unlike EKS and GKE), but the underlying VM node costs, managed disk costs, and load balancer charges add up fast. Here's how to optimize an AKS environment end-to-end.

AKS Cost Components

Component	Cost	Optimization
AKS cluster management	Free ✅	N/A
VM nodes (system + user pools)	VM pricing (largest cost)	High
Managed OS disks	$0.04–$0.17/GB/mo	Medium
Load balancers	~$18/mo each	Consolidate
Egress bandwidth	$0.087/GB (first 10TB)	Architecture
Azure Monitor / Log Analytics	$2.30/GB ingested	Filter logs

The free cluster management fee is AKS's biggest pricing advantage over EKS ($73/month) and GKE ($73/month). For organizations running many small clusters, this saves thousands per month.

Spot Node Pools: 60–90% Savings

# Add Spot node pool to existing AKS cluster
az aks nodepool add   --resource-group myRG   --cluster-name myAKS   --name spotpool   --priority Spot   --eviction-policy Delete   --spot-max-price -1   --node-vm-size Standard_D4s_v5   --node-count 3   --enable-cluster-autoscaler   --min-count 0   --max-count 10

AKS automatically adds the kubernetes.azure.com/scalesetpriority=spot:NoSchedule taint to Spot nodes. Add the corresponding toleration to workloads that should run on Spot, and keep system-critical workloads on the regular node pool.

# Pod toleration for Spot nodes
tolerations:
  - key: "kubernetes.azure.com/scalesetpriority"
    operator: "Equal"
    value: "spot"
    effect: "NoSchedule"

Node and Pod Right-Sizing

Use Azure Advisor's AKS recommendations for node pool right-sizing. For pod-level right-sizing, deploy the Vertical Pod Autoscaler (VPA) in Recommendation mode first — it shows suggested resource requests without changing anything. After validating recommendations in staging, enable Auto mode for non-critical workloads.

Node pool sizing strategy: prefer fewer, larger nodes over many small ones. A pool of 4x Standard_D8s_v5 (8 vCPU each) runs more efficiently (better bin packing, less per-node overhead) than 8x Standard_D4s_v5 at similar cost.

KEDA: Scale to Zero for Event-Driven Workloads

KEDA (Kubernetes Event-Driven Autoscaling) enables scaling deployments to zero replicas when there's no work to do — and scaling up instantly when events arrive. For queue-processing workloads, this eliminates idle compute entirely:

# KEDA ScaledObject: scale based on Azure Service Bus queue length
apiVersion: keda.sh/v1alpha1
kind: ScaledObject
metadata:
  name: my-queue-processor
spec:
  scaleTargetRef:
    name: queue-processor-deployment
  minReplicaCount: 0   # scale to zero when idle
  maxReplicaCount: 20
  triggers:
    - type: azure-servicebus
      metadata:
        queueName: my-queue
        messageCount: "5"  # scale up when >5 messages

Cluster Autoscaler Configuration

Enable the cluster autoscaler with aggressive scale-down settings for non-production clusters. Key settings to tune: scale-down-delay-after-add=5m (don't wait 10 minutes before scaling down), scale-down-unneeded-time=5m, and skip-nodes-with-system-pods=false for user node pools.

Cost Visibility for AKS

Azure's native AKS cost analysis is available in the Azure portal under the AKS cluster → Cost analysis (preview). It shows cost by namespace, service, and pod. For more depth: deploy Kubecost (free for single cluster) or use Azure Monitor with a custom cost allocation workbook.

// FAQ

AKS vs EKS vs GKE — which is cheapest?

AKS wins on cluster management fee (free vs $73/month for EKS and GKE). EKS wins on ecosystem maturity and Spot tooling (Karpenter). GKE wins on Spot VM discounts (up to 91%) and Autopilot pricing model. Total cost depends heavily on workload mix and existing cloud commitment.