Optimizing AKS Autoscaling for Cost Efficiency

A well-architected autoscaling strategy can reduce Azure Kubernetes Service (AKS) operational costs by up to 40% without compromising availability or performance. This deep dive explains how to optimize Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler (CA), along with Azure and Kubernetes best practices, real-world implementation examples, performance tuning, and security considerations.

1. Introduction

Autoscaling is essential for maintaining efficient, cost-effective Kubernetes workloads. In AKS, two primary components manage scaling:

• Horizontal Pod Autoscaler (HPA): Scales pods based on application load.
• Cluster Autoscaler (CA): Scales node pools based on cluster capacity.

When configured together correctly, these systems create a highly optimized and cost-effective environment.

2. Azure Configuration Best Practices

2.1 Node Pool Strategy

A cost-optimized AKS cluster typically includes:

System Node Pool

• For system-level components (CoreDNS, metrics-server, CNI)
• VM size: Small (Standard_D2s_v5)
• Stable capacity, minimal autoscaling

User Node Pools

Multiple pools based on workload type:

| Node Pool | Purpose | VM Type | Notes | |-----------|---------|---------|-------| | General Compute | Stateless workloads | D-series | Good default choice | | Memory Optimized | ML, analytics | E-series | Assign via labels | | Spot Node Pool | Non-critical workloads | Spot VMs | Up to 90% cheaper |

2.2 VMSS-Based Autoscaling Configuration

AKS uses VM Scale Sets (VMSS) under the hood.

Recommended settings:

minCount = 1
maxCount = <based on workload>
enableAutoScaling = true

Useful node pool tags:

• kubernetes.azure.com/scalesetpriority: spot
• kubernetes.azure.com/mode: user

3. Kubernetes Configuration Best Practices

3.1 Horizontal Pod Autoscaler (HPA)

Example HPA Configuration

apiVersion: autoscaling/v2
kind: HorizontalPodAutoscaler
metadata:
  name: api-service-hpa
spec:
  scaleTargetRef:
    apiVersion: apps/v1
    kind: Deployment
    name: api-service
  minReplicas: 2
  maxReplicas: 20
  metrics:
    - type: Resource
      resource:
        name: cpu
        target:
          type: Utilization
          averageUtilization: 65

HPA Best Practices

• Use CPU + Memory metrics
• Add custom metrics (e.g., queue length, latency)
• Avoid extremely aggressive scaling
• Configure cooldown periods
• Tune utilization targets based on real workloads

3.2 Cluster Autoscaler (CA)

Recommended configuration flags:

--balance-similar-node-groups=true
--expander=least-waste
--scale-down-enabled=true
--scale-down-delay-after-add=15m
--scale-down-unneeded-time=10m

Best Practices

• Define accurate resource requests and limits
• Implement Pod Disruption Budgets (PDBs)
• Prefer smaller nodes to improve bin packing
• For bursty workloads, enable overprovisioning

4. Cost Optimization Strategies

4.1 Rightsizing Workloads

Avoid over-provisioning by setting accurate resource Requests/Limits. Use real metrics from Azure Monitor or Prometheus.

Run Vertical Pod Autoscaler (VPA) in recommendation mode for guidance.

4.2 Utilizing Spot Nodes

Spot VMs provide massive savings for non-critical tasks.

Use Spot nodes for:

• Batch jobs
• CI/CD pipelines
• Stateless background services

Avoid them for:

• Databases
• Stateful systems

4.3 Schedule-Based Autoscaling

Not all workloads require 24/7 capacity.

You can reduce node count after hours using:

• Azure Automation
• Scheduled KEDA triggers
• CronJobs or GitHub Actions

4.4 Overprovisioning Pods

These low-priority "buffer pods" ensure:

• Zero cold-start delays
• Immediate pod placement
• More efficient scaling decisions

5. Real-World Implementation Scenarios

Scenario 1: E-commerce Platform

• Sudden traffic spikes handled using CPU + RPS metrics
• Overprovisioning enabled
• Workloads isolated using node pools
• Outcome: ~35% lower operational cost

Scenario 2: Machine Learning Workloads

• Batch workloads shifted to Spot nodes
• GPU autoscaling tuned via HPA
• Outcome: 50–70% cost reduction

Scenario 3: Microservices Architecture

• Resource Requests/Limits enforced
• VPA in recommend mode
• PDBs for safe rollouts
• Outcome: 28–33% reduction in waste

6. Performance Optimization Techniques

• Use Azure CNI Overlay for cost-efficient networking
• Implement Ingress + caching layers
• Optimize container images for quicker scaling
• Use KEDA for event-driven scaling
• Tune metrics-server for accurate autoscaling signals

7. Security & Compliance Considerations

Autoscaling interacts with security constraints.

Recommendations:

• Use Managed Identities for node pools
• RBAC for HPA/CA service accounts
• Enforce policies using Azure Policy
• Apply NetworkPolicies for isolation
• Use hardened OS images for nodes

8. Conclusion

Optimizing AKS autoscaling is a powerful way to enhance reliability, reduce cost, and maintain performance. By combining HPA, Cluster Autoscaler, Spot VMs, and node pool design, teams routinely achieve 20–40% savings.

If you'd like assistance implementing autoscaling or AKS optimization strategies, feel free to reach out.