Managing cloud spending is one of the biggest challenges for modern enterprises. As applications scale, costs silently grow through unused resources, over-provisioned workloads, and inefficient storage patterns. AWS provides numerous tools and best practices to control and optimize spend—yet most organizations use only a small fraction of them.
In this blog, I’m sharing the most effective AWS cost optimization techniques that I have personally implemented across real-world environments. These strategies are simple, practical, and deliver immediate results without compromising performance.
🚀 1. Migrate to Graviton Instances
AWS Graviton2 and Graviton3 processors offer 20–40% better price-performance compared to traditional x86 instances. They are energy-efficient and ideal for application servers, microservices, and container workloads. Migrating to Graviton is one of the easiest ways to cut EC2 compute costs significantly.
💰 2. Purchase Reserved Instances for Long-Running Workloads
If you have workloads running 24/7 (e.g., production servers, databases), Reserved Instances (RI) can cut costs by up to 72%. By committing to a 1-year or 3-year term, you get predictable and deeply discounted pricing compared to On-Demand.
📦 3. Apply S3 Lifecycle Policies
Without lifecycle policies, data sits forever in expensive S3 Standard storage. Using lifecycle rules, cold or unused data can automatically shift to cheaper tiers like Glacier, Glacier Deep Archive, or S3 Infrequent Access. This reduces storage costs dramatically for logs, backups, and infrequently accessed datasets.
🐳 4. Apply ECR Lifecycle Policies
Amazon ECR often stores hundreds of old container images that are no longer required. Implementing ECR lifecycle rules helps delete unused tags and old image versions, keeping repositories clean and reducing unnecessary storage costs.
📊 5. Set Retention Policies for CloudWatch Logs
CloudWatch Logs grow quickly—and storing logs forever gets expensive. Setting a retention period (7, 30, or 90 days) ensures logs are automatically deleted based on relevance. This is essential for cost control in environments with high log volume.
💾 6. Remove Unused AMIs and Snapshots
Unused AMIs and outdated snapshots accumulate over time, consuming EBS storage. Regular audits and deletion of stale snapshots help lower costs and maintain a clutter-free environment. I have used custom script to delete unused AMIs and associated snapshots.
🌐 7. Release Unused Elastic IPs
AWS charges for Elastic IPs that are allocated but not attached to a running instance. Releasing unused Elastic IPs prevents silent billing and keeps your network resources optimized.
🔍 8. Rightsize EC2 Instances
Over-provisioned EC2, RDS, or Auto Scaling Groups lead to unnecessary spending. Use AWS Compute Optimizer or CloudWatch metrics to identify resources that can be downsized. Rightsizing is often the quickest win with immediate cost reduction.
🎯 9. Use Spot Instances for Non-Critical Workloads
For flexible and fault-tolerant workloads, Spot Instances provide up to 90% cost savings. They are ideal for CI/CD pipelines, batch jobs, analytics workloads, and large-scale distributed tasks.
📂 10. Enable S3 Intelligent-Tiering
S3 Intelligent-Tiering automatically moves data between access tiers based on usage. This provides cost savings without needing manual lifecycle rules—perfect for unpredictable access patterns.
💤 11. Shut Down Non-Prod Resources During Off-Hours
DEV/QA environments typically run only during business hours. Automate shutdown using AWS Instance Scheduler or Lambda scripts. This alone can save 30–50% of EC2 costs for non-production environments.
🧾 12. Use AWS Savings Plans
Savings Plans offer flexible, commitment-based pricing across EC2, Fargate, Lambda, and SageMaker, delivering up to 66% savings. Unlike RIs, Savings Plans automatically apply across instance families, regions, and OS types.
⚖️ 13. Optimize Load Balancers
Delete unused ALBs/NLBs, idle target groups, and low-traffic load balancers. ALBs can also be more cost-effective than NLBs for HTTP workloads.
🗃️ 14. Use Aurora Serverless or DynamoDB On-Demand
Not all workloads need permanent, provisioned databases. Serverless and on-demand modes allow you to pay only when data is actually accessed, making them perfect for variable or unpredictable loads.
🔗 15. Reduce NAT Gateway Costs with VPC Endpoints
NAT Gateways charge per GB of data processed. Use VPC endpoints for S3 and DynamoDB to bypass NAT and significantly reduce data transfer charges—especially in data-intensive architectures.
📀 16. Optimize EBS Volumes
Convert GP2 to GP3 volumes to reduce cost and improve performance
Delete unattached EBS volumes
Use EBS Snapshot Lifecycle Manager to automate cleaning
These small changes collectively make a big impact on long-term cost savings.
📝 Conclusion
AWS offers a huge toolbox for cost optimization—but without active monitoring and periodic cleanup, cloud costs quickly spiral out of control. By implementing the techniques above—Graviton migration, lifecycle policies, RI/Savings Plans, rightsizing, and storage optimization—you can achieve substantial savings while keeping your cloud environment efficient and future-ready.
Cost optimization is not a one-time task; it’s a continuous FinOps practice. Start with small improvements and build a culture where teams regularly review and optimize their cloud usage.
Cost comparison post applying all FinOps practice:
Top comments (0)