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AWS Cost Optimization is one of the highest-impact activities for DevOps and cloud engineering teams. As infrastructure scales, cloud spending often increases faster than expected. In this guide, we share seven AWS Cost Optimization techniques that helped us reduce our monthly AWS bill by 43% without affecting performance or reliability.
Cloud costs have a way of creeping up on teams. What starts as a few EC2 instances and a managed database can quickly become a monthly bill that surprises even experienced engineering leaders.
Recently, our team conducted a complete AWS cost optimization review and managed to reduce our monthly cloud spend by 43% without sacrificing performance, reliability, or developer productivity.
In this article, I’ll walk through the seven techniques that had the biggest impact.
1. AWS Cost Optimization Technique #1: Rightsize EC2 Instances
One of the most common cloud cost mistakes is running oversized instances.
Over time, applications evolve, traffic patterns change, and infrastructure gets upgraded. However, many teams continue paying for resources they no longer need.
We discovered several instances running at less than 20% CPU utilization for months.
What we did
- Analyzed CloudWatch metrics
- Reviewed CPU and memory utilization
- Downgraded overprovisioned instances
- Migrated workloads to newer generation instance types
Result
Savings: Approximately 15% of our monthly EC2 costs.
2. AWS Cost Optimization Technique #2: Adopt Spot Instances
Not every workload requires guaranteed compute capacity.
Batch jobs, CI/CD pipelines, testing environments, and background processing tasks can often tolerate interruptions.
What we moved to Spot
- Jenkins build agents
- Kubernetes worker nodes for batch jobs
- Data processing workloads
- Temporary development environments
Result
Spot Instances reduced compute costs by up to 70% for eligible workloads.
3. AWS Cost Optimization Technique #3: Improve Auto Scaling
Many teams enable Auto Scaling but never tune it.
As a result, resources stay online even when demand drops.
What we changed
- Reviewed scaling policies
- Added aggressive scale-in rules
- Scheduled scaling for predictable workloads
- Removed unnecessary baseline capacity
Result
Infrastructure scaled according to demand instead of peak traffic assumptions.
Savings: Around 8%.
4. AWS Cost Optimization Technique #4: Clean Up Unused Storage
Storage costs often go unnoticed because they grow gradually.
During our audit, we found:
- Unattached EBS volumes
- Old EBS snapshots
- Unused AMIs
- S3 buckets containing years of stale data
What we did
- Automated cleanup reports
- Applied lifecycle policies
- Archived infrequently accessed data
- Deleted orphaned resources
Result
Storage-related costs dropped significantly while improving operational hygiene.
5. AWS Cost Optimization Technique #5: Use Savings Plans
If certain workloads run continuously, on-demand pricing is usually the most expensive option.
After analyzing utilization trends, we identified stable workloads that were unlikely to change.
What we purchased
- Compute Savings Plans
- Reserved database capacity
- Long-term commitments for predictable workloads
Result
Savings ranged between 30% and 60% compared to on-demand pricing.
6. AWS Cost Optimization Technique #6: Optimize Kubernetes Resources
In Kubernetes environments, overallocated resources are extremely common.
Many applications request far more CPU and memory than they actually consume.
What we found
Several services requested:
- 2 vCPUs while consuming less than 0.5
- 4 GB memory while using less than 1 GB
What we did
- Monitored actual usage
- Adjusted requests and limits
- Improved node utilization
- Reduced cluster size
Result
Higher cluster efficiency and fewer worker nodes.
7. AWS Cost Optimization Technique #7: Improve Cost Visibility
The biggest optimization wasn’t technical.
It was cultural.
When nobody owns cloud spending, costs inevitably rise.
What we implemented
- Resource tagging standards
- Team-level cost dashboards
- Monthly cost reviews
- Budget alerts
- Cost accountability for engineering teams
Result
Teams became more conscious of infrastructure decisions and prevented future waste.
Final Results
After implementing these changes:
| Area | Estimated Savings |
|---|---|
| Rightsizing | 15% |
| Spot Instances | 8% |
| Auto Scaling | 8% |
| Storage Cleanup | 4% |
| Savings Plans | 5% |
| Kubernetes Optimization | 2% |
| Governance Improvements | 1%+ |
Total AWS Cost Reduction: 43%
The most important lesson wasn’t finding one magical optimization. It was consistently eliminating small inefficiencies across the entire infrastructure stack.
If your AWS bill keeps growing every month, start with visibility, measure actual usage, and optimize based on data—not assumptions.
You may be surprised by how much money is hiding in plain sight.
For more articles on topics check out Let’s Talk About DevOps.
