Financial ComplianceAWS Cost OptimizationMulti-Region
Case Study · Financial Compliance Platform

AWS Infrastructure Cleanup & Optimization

A financial compliance platform reduced AWS infrastructure costs by eliminating unused resources across multiple regions and implementing automated lifecycle policies.

Client Profile
Financial Compliance Platform
IndustryFinancial Compliance & RegTech
RegionMulti-Region (AWS)
StackECS, ECR, EC2, VPC, CloudWatch
Duration2–3 Weeks
200+
Old container images removed
Multi
Regions cleaned up
ECS
Auto Scaling enabled
ECR
Lifecycle policies configured
The Challenge

Unused resources across multiple regions. Costs climbing silently.

As the platform evolved, AWS infrastructure accumulated across multiple regions with unused services, old container images, and idle resources generating unnecessary charges.

Before, The Pain

Sprawling multi-region waste

  • Unused ECS services, clusters, and compute resources running across multiple regions
  • Old container images accumulating in ECR repositories (200+)
  • Idle EC2 instances and unattached Elastic IPs generating charges
  • Unnecessary CloudWatch log groups consuming storage
  • Unused VPCs, load balancers, and networking components in inactive regions
After, The Result

Clean, consolidated infrastructure

  • Infrastructure consolidated to active production region only
  • ECR lifecycle policies auto-retain only latest container images
  • All idle compute, networking, and storage resources removed
  • ECS Auto Scaling enabled for workload-based utilization
  • Clean, organized environment with reduced operational overhead
Our Approach

Three weeks. Audit, clean, automate.

A systematic approach: full infrastructure audit first, then methodical cleanup of unused resources, followed by automation to prevent future accumulation.

Step 1 · Week 1 · Infrastructure Audit

Full audit across all active regions

  • Conducted a full audit of AWS resources across all active regions
  • Identified unused ECS Fargate services, clusters, and old container images
  • Catalogued idle EC2 instances, unattached Elastic IPs, and unused AMIs
  • Reviewed VPC configurations, load balancers, and networking components
  • Assessed CloudWatch log groups and storage usage
Step 2 · Week 2 · Cleanup & Removal

Systematic removal of unused resources

  • Deleted unused ECS Fargate services and clusters
  • Removed unused load balancers and networking components
  • Cleaned up unused VPCs and associated resources
  • Deleted idle EC2 instances and released Elastic IP addresses
  • Removed unused AMIs and old machine images
  • Cleaned unnecessary CloudWatch logs and log groups
  • Removed all infrastructure from inactive AWS regions
Step 3 · Week 3 · Automation & Optimization

Lifecycle policies and auto-scaling

  • Configured Amazon ECR lifecycle policies to auto-retain latest images only
  • Cleaned up 200+ old container images from production ECR
  • Enabled ECS Auto Scaling for workload-based resource utilization
  • Retained infrastructure only in the active production region
  • Validated production workload stability and availability
Outcomes

What changed, permanently.

Beyond immediate cost savings, the engagement delivered a cleaner, more manageable AWS environment with automated safeguards against future resource sprawl.

Cost Reduction

Reduced unnecessary AWS infrastructure spending across multiple regions by eliminating idle compute, networking, storage, and logging resources.

Operational Efficiency

Simplified AWS environment management with improved visibility into active production infrastructure.

Automated Cleanup

ECR lifecycle policies now auto-retain only latest container images, preventing future image accumulation.

Scalable Infrastructure

ECS Auto Scaling improves workload-based resource utilization, adjusting capacity based on actual demand.

The environment became more organized, easier to manage, and more cost-efficient while maintaining production workload stability and availability.

EL
Engineering LeadFinancial Compliance Platform
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