AWS Cost Optimization for Enterprise Infrastructure

Overview

Our team conducted a thorough AWS cost optimization initiative for an existing client with a mature, multi-service infrastructure spanning production and non-production environments. The goal was to reduce expenses across key services without compromising performance, compliance, or availability, achieving an estimated 35-50% overall cost savings through targeted rightsizing, lifecycle management, and commitment-based pricing.

We began with:

Amazon RDS (Relational Database Service):

  • Reduced snapshot retention periods in non-prod environments, leveraging complimentary storage limits to minimize on-demand charges for excess usage.
  • Implemented a mix of 1-year and 3-year Reserved Instances (RIs) with convertible options for flexibility in instance sizing, accommodating user normalization factors and yielding significant discounts (up to 60% off on-demand

Amazon CloudWatch optimizations:

  • Shortened log group retention for non-prod applications.
  • Standardized logging practices and reduced log volume pushed to CloudWatch, cutting ingestion and storage costs by ~40%.

Amazon EC2 saw extensive improvements:

  • Migrated compatible workloads to Arm-based Graviton instances for up to 40% better price-performance.
  • Upgraded all GP2 EBS volumes to GP3, delivering ~20% savings on storage.
  • Deleted unused EBS volumes, AMIs, and snapshots; released idle Elastic IPs.
  • Used EC2 Compute Optimizer to right-size EC2 instances.
  • Adopted a hybrid model: Convertible RIs for predictable long-term instances, Compute Savings Plans for overages (analyzed via multi-week usage monitoring), and Spot Instances for non-critical ECS workloads.

Amazon S3:

  • Segregated buckets by environment (prod/non-prod).
  • Applied lifecycle policies to transition objects to Glacier (and Deep Archive for compliance retention), expiring non-essential data after defined periods.
    This addressed bloated storage from old RDS snapshots, VPC flow logs, CloudTrail, and load balancer logs.
Additional services optimized:
  • Redshift and OpenSearch: All-upfront 1-year RIs for ~50% savings over on-demand.
  • Secrets Management: Migrated non-critical secrets from Secrets Manager to SSM Parameter Store (free tier eligible).
  • AWS PrivateLink/NAT Gateway: Reduced data transfer volumes by routing internal VPC traffic privately, minimizing processed data charges (gateway fixed costs unchanged).
  • Amazon ECS/Fargate: Spot Instances for non-critical microservices; On-Demand/Graviton for critical ones; rigorously tested CPU/memory limits to prevent over-provisioning.[1][2]

Methodology:

Used AWS Cost Explorer, Compute Optimizer, and CloudWatch for baseline analysis. Implemented no-regret quick wins (e.g., idle resource cleanup), followed by commitment modeling. Post- established monthly reviews and tagging for ongoing accountability. Total character count: 2,847.

Results:

Delivered predictable savings via Savings Plans/RIs, modernized architecture (e.g., Graviton), and eliminated waste. Client reported sustained monthly reductions, with flexibility for scaling.

Skills

  • AWS Services:
    EC2, RDS, S3, CloudWatch, ECS/Fargate, EBS, Redshift, OpenSearch, NAT Gateway, Secrets Manager, SSM Parameter Store
  • Cost Management:
    Reserved Instances, Savings Plans, Spot Instances, Cost Explorer, Compute Optimizer
  • Infrastructure as Code:
    Terraform, CloudFormation
  • Optimization Best Practices:
    Rightsizing, Graviton Migration, Storage Tiering (Glacier/Deep Archive), Logging Standardization
  • Monitoring & Analysis:
    CloudWatch, Usage Monitoring, Tagging Strategies
  • Programming/DevOps:
    Python (for automation scripts), Docker (ECS microservices)