Skip to content

cost-optimization-strategies-aks.md

Latest commit

 

History

History
135 lines (111 loc) · 5.45 KB

cost-optimization-strategies-aks.md

File metadata and controls

135 lines (111 loc) · 5.45 KB
Raw

Cost Optimization Strategies for Azure Kubernetes Service

Author: Randy Bordeaux
Date: January 2026
Version: 1.0
Document Type: Architecture Brief
Scope: Azure Commercial
Azure Services: Azure Kubernetes Service (AKS), Azure Monitor, Azure Policy, Azure Cost Management

Deployable Terraform: examples/compute/aks-architecture-briefs/cost-optimization/

Architecture Brief — This document covers architectural principles and design decisions. For complete Terraform modules and KQL monitoring queries, see the full whitepaper template.

Executive Summary

This architecture brief provides a decision-oriented guide to cost optimization for Azure Kubernetes Service (AKS) in Azure Commercial environments.

Table of Contents

1. Scope and Assumptions

  • Azure Commercial only
  • Azure Kubernetes Service (AKS)
  • Terraform (AzureRM provider) required
  • Private AKS clusters only
  • Cost optimization enforced structurally, not manually
  • CI/CD-managed infrastructure

2. Cost Architecture Principles

  • Optimize architecture before tuning workloads
  • Align cost domains to ownership boundaries
  • Prefer elasticity over static capacity
  • Enforce limits to prevent runaway spend
  • Make cost visible at the team and workload level

3. Primary AKS Cost Drivers

graph TD
    AKS[AKS Cluster] --> Compute[Node Pools / VM SKUs]
    AKS --> Storage[Disks / Snapshots]
    AKS --> Network[Egress / Load Balancers]
    AKS --> Logs[Log Analytics Ingestion]
Loading
  • Node pool VM hours dominate baseline spend
  • Egress and NAT traffic are common blind spots
  • Log ingestion grows non-linearly with scale

4. Node Pool Right-Sizing and VM Selection

  • Use workload-specific node pools
  • Avoid over-provisioned general-purpose SKUs
  • Prefer memory-optimized SKUs only where justified
  • Right-size system node pools aggressively
  • Eliminate unused node pools

5. Autoscaling and Capacity Management

graph TD
    HPA[Horizontal Pod Autoscaler] --> CA[Cluster Autoscaler]
    CA --> NP[Node Pools]
Loading
  • Enable Cluster Autoscaler per node pool
  • Enforce pod resource requests and limits
  • Avoid fixed-size pools in non-production
  • Separate scale characteristics by workload

6. Spot Instances and Workload Segmentation

  • Use spot node pools for fault-tolerant workloads
  • Explicit taints and tolerations required
  • No stateful or control-plane-adjacent workloads
  • Expect and design for eviction

7. Networking and Egress Cost Control

  • Prefer internal load balancers
  • Centralize egress through firewall/NAT
  • Avoid unnecessary cross-zone traffic
  • Minimize public load balancer usage
  • Monitor SNAT and outbound data volume

8. Storage and Persistent Volume Optimization

  • Match disk SKU to IOPS requirements
  • Avoid over-sized managed disks
  • Clean up orphaned PVCs
  • Snapshot and backup retention limits enforced

9. Observability Cost Management

  • Table-level Log Analytics retention
  • Filter noisy container logs
  • Avoid per-pod alerting
  • Separate security logs from application telemetry

10. Governance, Budgets, and Policy Guardrails

  • Azure budgets at subscription and resource group scope
  • Cost anomaly alerts
  • Policy to deny unsupported VM SKUs
  • Enforce tagging for cost allocation

11. Terraform Implementation Patterns

resource "azurerm_kubernetes_cluster_node_pool" "spot" {
  name                  = "spotnp"
  kubernetes_cluster_id = azurerm_kubernetes_cluster.aks.id
  vm_size               = "Standard_D4s_v5"
  priority              = "Spot"
  eviction_policy       = "Delete"
  node_count            = 1
}

12. Tradeoffs and Limitations

  • Aggressive autoscaling can impact latency
  • Spot instances require workload resilience
  • Reduced log retention limits forensic depth

13. Conclusion

Cost optimization in AKS is an architectural discipline, not a post-facto exercise. By aligning node pools to workloads, enforcing autoscaling, controlling egress and telemetry, and codifying guardrails with Terraform and Azure Policy, teams can operate cost-efficient AKS platforms without sacrificing reliability or security.