Case study
Cutting Cloud Costs Without Sacrificing Performance
The Challenge
Cloud costs had grown out of control. What started as manageable infrastructure costs for 7-10 projects suddenly spiked-new projects were seeing 8x cost increases with no clear explanation. Multiple teams were deploying resources without governance, using expensive architectural patterns without understanding the cost implications. There was no cost visibility, no accountability, and no one who could explain why monthly AWS bills kept climbing.
The Approach
- →Conducted comprehensive infrastructure audit across all AWS accounts
- →Tagged all resources and implemented cost allocation tracking
- →Identified idle resources, over-provisioned instances, and inefficient architectural patterns
- →Audited and decommissioned resources from abandoned or completed projects
- →Created and documented cost-effective patterns to replace expensive approaches
- →Migrated appropriate workloads to serverless architectures
- →Implemented architecture review process: all new features required cost analysis before implementation
- →Established budgets, alerts, and approval processes for new resources
- →Provided training to engineering teams on cost-effective architectural patterns
Technical Approach
This wasn't just about turning off unused instances. It required deep architectural analysis to understand why resources were provisioned the way they were, identifying systemic cost drivers, then redesigning patterns for cost efficiency without sacrificing performance. The key was establishing architectural oversight so teams couldn't unknowingly deploy expensive solutions.
Key Technical Decisions
- →Resource Cleanup: Audited all AWS accounts to identify resources from abandoned or completed projects. Coordinated with teams to safely decommission unused EC2 instances, RDS databases, S3 buckets, and other orphaned infrastructure. Established resource lifecycle policies to prevent future accumulation
- →Scheduled Workloads: Migrated two ECS containers running scheduled processing jobs to EventBridge Scheduler + Lambda, eliminating always-on container costs. Right-sized remaining ECS containers based on actual resource utilization
- →Batch Processing: Migrated expensive architectural patterns to simple SQS-based processing, reducing compute costs while improving reliability
- →Serverless Migration: Moved appropriate workloads from always-on EC2 instances to Lambda for intermittent processing, paying only for actual execution time
- →Database: Right-sized RDS instances based on actual usage patterns and CloudWatch metrics rather than overly conservative estimates
- →Storage: Implemented S3 lifecycle policies to automatically move infrequently accessed data to cheaper storage tiers
- →Network: Consolidated VPCs and eliminated unnecessary data transfer between availability zones
- →Governance: Implemented architecture review and modification process-features couldn't proceed without cost analysis and pattern approval
- →Monitoring: Implemented cost allocation tags and AWS Cost Explorer dashboards to maintain ongoing visibility and accountability
The Results
- ✓43% reduction in monthly AWS spend
- ✓Full cost visibility with tagging and allocation reports by team and project
- ✓Sustainable governance framework and architectural patterns library
- ✓Zero performance degradation or service interruptions during migration
- ✓Cost-per-project reduced from 8x spike back to predictable baseline
Key Takeaway
The cost savings gave the company additional runway during a challenging fundraising environment. More importantly, the architectural oversight process and pattern library prevented costs from spiraling again-new projects now launch with cost-efficient patterns from day one rather than requiring expensive retrofits.
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