Optimizing Kubernetes Costs on AWS: A Practical Guide

Running Kubernetes clusters on AWS can be expensive if not managed properly. In this guide, I'll share practical strategies that helped me reduce cluster costs by 40% while maintaining 99.99% uptime.

The Challenge

Managing a multi-tenant Kubernetes cluster for healthcare applications, we faced escalating AWS costs. Our monthly bill was growing faster than our actual usage, and we needed to optimize without compromising reliability or performance.

Key Strategies

1. Right-Sizing Nodes and Pods

The first step was auditing our resource requests and limits. Many pods were over-provisioned, requesting more CPU and memory than they actually used.

Action taken:

Used metrics-server and Prometheus to analyze actual resource usage
Adjusted pod resource requests to match real usage patterns
Implemented Vertical Pod Autoscaler (VPA) for automatic optimization

Result: 25% reduction in node requirements

2. Cluster Autoscaling

We implemented Kubernetes Cluster Autoscaler to automatically adjust the number of nodes based on workload demand.

Configuration highlights:

apiVersion: autoscaling.k8s.io/v1
kind: ClusterAutoscaler
metadata:
  name: cluster-autoscaler
spec:
  scaleDown:
    enabled: true
    delayAfterAdd: 10m
    unneededTime: 10m

Result: 30% cost savings during off-peak hours

3. Spot Instances for Non-Critical Workloads

We migrated development and testing workloads to AWS Spot Instances, which offer up to 90% discount compared to On-Demand instances.

Strategy:

Used mixed instance types in node groups
Implemented pod disruption budgets (PDBs)
Set up proper handling for spot interruptions

Result: 60% cost reduction for dev/test environments

4. Storage Optimization

We optimized EBS volumes and implemented lifecycle policies for log and backup data.

Actions:

Migrated infrequently accessed data to S3
Implemented EBS volume snapshots with automated cleanup
Used gp3 volumes instead of gp2 for better price-performance

Result: 20% reduction in storage costs

5. Resource Quotas and Limits

Implemented namespace-level resource quotas to prevent resource wastage and ensure fair distribution across tenants.

apiVersion: v1
kind: ResourceQuota
metadata:
  name: compute-quota
spec:
  hard:
    requests.cpu: "10"
    requests.memory: 20Gi
    limits.cpu: "20"
    limits.memory: 40Gi

Monitoring and Continuous Optimization

Set up Grafana dashboards to monitor:

Cost per namespace
Resource utilization trends
Spot instance interruption rates
Pod rightsizing recommendations

Results

After implementing these strategies:

40% overall cost reduction
Maintained 99.99% uptime
Improved resource utilization from 45% to 75%
Reduced incident response time with better monitoring

Key Takeaways

Start with resource right-sizing - it has the biggest immediate impact
Use autoscaling to match resources with actual demand
Spot instances are great for non-critical workloads
Continuous monitoring is essential for sustained optimization
Document everything and automate where possible

Need Help with Your Kubernetes Infrastructure?

I offer DevOps consulting services to help optimize your cloud infrastructure. Get in touch to discuss your requirements.