How We Reduced Cloud Costs by 60%: A Technical Deep-Dive
Executive Summary
Over six months, we reduced our AWS infrastructure costs from $120,000/month to $48,000/month,a 60% reduction,while simultaneously improving performance and reliability. This case study details the technical strategies, architectural changes, and measurable results that made this transformation possible.
Cost Breakdown by Service
Compute (EC2)
Database (RDS)
Data Transfer
Storage (S3/EBS)
Total Savings
Implementation Timeline
Analysis & Planning
Comprehensive audit of all AWS resources and usage patterns
Compute Right-Sizing
Optimized EC2 instances and implemented auto-scaling
Database Optimization
Query optimization, read replicas, and connection pooling
Data Transfer & CDN
CloudFront implementation and response compression
Storage Optimization
Lifecycle policies and volume optimization
Reserved Instances
Committed to 1-year RIs and Savings Plans
Optimization Strategies
Our optimization journey wasn't about cutting corners,it was about eliminating waste while improving performance. We discovered that most cloud overspending comes from three sources: over-provisioned resources, inefficient architectures, and lack of visibility into actual usage patterns. By addressing each systematically, we achieved dramatic cost reductions without sacrificing reliability or user experience. Here's how we did it:
Strategy 1: Compute Right-Sizing
The Problem
Analysis revealed that 60% of our EC2 instances were over-provisioned, running at less than 30% CPU utilization during peak hours. We were essentially paying for capacity we didn't need.
The Solution
- Deployed CloudWatch agents on all 247 instances
- Collected 30 days of detailed metrics (CPU, memory, network, disk I/O)
- Identified underutilized resources using custom scripts
- Migrated 89 web servers from m5.2xlarge → m5.large (75% cost reduction)
- Switched 34 API servers to compute-optimized c5 instances
- Moved 45 batch jobs to spot instances (90% cost reduction)
- Implemented predictive scaling based on historical patterns
- Reduced minimum instance count from 50 to 20 during off-peak hours
- Added scale-in protection for critical services
Results
Strategy 2: Database Optimization
The Problem
Our RDS costs were spiraling out of control. We were running 12 production databases, most of them over-provisioned "just in case." Slow queries were causing connection pool exhaustion, leading us to scale up instances rather than fix the root cause. We were also paying for high-availability features we didn't actually need for all databases.
The Solution
- Identified and optimized the top 50 slowest queries using Performance Insights
- Added missing indexes that reduced query time by 85%
- Implemented query result caching with Redis for frequently accessed data
- Reduced average query time from 847ms to 94ms
- Consolidated 4 low-traffic databases into a single multi-tenant instance
- Downgraded 6 databases from db.r5.2xlarge to db.r5.xlarge
- Moved development and staging databases to smaller instance types
- Implemented connection pooling to reduce connection overhead
- Implemented automated data archival for records older than 2 years
- Compressed large text fields, reducing storage by 40%
- Switched from Provisioned IOPS to GP3 volumes (30% cost reduction)
- Enabled automated backup retention policies
Results
Strategy 3: Data Transfer & CDN Optimization
Data transfer costs are often overlooked until they become a significant line item. We were paying $18K/month for data transfer, primarily because we were serving all static assets and API responses directly from our origin servers. Every image, CSS file, and JavaScript bundle was being downloaded from EC2 instances across the globe, racking up massive egress charges.
The solution was implementing a comprehensive CDN strategy with CloudFront. By caching static assets at edge locations and implementing smart caching policies for API responses, we reduced origin requests by 85%. We also implemented response compression, which reduced payload sizes by an average of 70% for text-based content.
Key Optimizations
- Configured aggressive caching for static assets (1 year TTL)
- Implemented cache invalidation on deployments
- Used Lambda@Edge for dynamic content optimization
- Reduced origin requests by 85%
- Enabled Brotli compression for all text content
- Implemented WebP images with fallbacks
- Minified and bundled JavaScript/CSS
- Reduced average payload size by 70%
The Hidden Costs We Discovered
During our optimization journey, we uncovered several "hidden" costs that weren't immediately obvious from our AWS bills. These costs were spread across multiple services and required deep analysis to identify. Here are the most surprising findings:
Zombie Resources
We found 47 EBS volumes that were no longer attached to any instances, costing us $2,800/month. These were snapshots and volumes from terminated instances that were never cleaned up. We also discovered 23 Elastic IPs that weren't associated with running instances, each costing $3.60/month.
Development Environment Waste
Our development and staging environments were running 24/7, even though they were only used during business hours (roughly 50 hours/week). By implementing automated start/stop schedules, we reduced these costs by 70% without impacting developer productivity.
Lessons Learned & Best Practices
After six months of intensive cost optimization work, we've learned valuable lessons that can help other teams avoid the same pitfalls. Here are our top recommendations for anyone embarking on a similar journey:
1. Make Cost Visibility a Priority
You can't optimize what you can't measure. We implemented comprehensive cost tagging across all resources, allowing us to track spending by team, project, and environment. We also set up daily cost anomaly alerts that notify us when spending deviates from expected patterns. This visibility was crucial for identifying optimization opportunities and preventing cost regressions.
We built custom dashboards that show cost trends, forecasts, and per-service breakdowns. These dashboards are reviewed weekly by engineering leads, making cost optimization a continuous process rather than a one-time project.
2. Automate Everything
Manual cost optimization doesn't scale. We built automation for resource tagging, right-sizing recommendations, and cleanup of unused resources. Our automated systems now handle 80% of cost optimization tasks that previously required manual intervention.
For example, we created Lambda functions that automatically stop development instances after hours, delete old snapshots, and send Slack notifications when resources are untagged or underutilized. These automations save us 10+ hours per week and prevent human error.
3. Balance Cost and Performance
The goal isn't to minimize costs at all costs,it's to maximize value. Some of our optimizations actually improved performance while reducing costs (like query optimization and CDN implementation). Others required careful trade-offs between cost and performance characteristics.
We established SLOs (Service Level Objectives) for all critical services before starting optimization work. This ensured that cost reductions never compromised user experience. In fact, our average API response time improved by 12% during the optimization process because we fixed underlying performance issues.
Key Learnings
Measure Everything
You can't optimize what you don't measure. Comprehensive monitoring was crucial to identifying opportunities.
Start with Quick Wins
Right-sizing compute resources provided immediate savings and built momentum for larger projects.
Automate Optimization
Manual optimization doesn't scale. We built automation for resource tagging, cost anomaly detection, and right-sizing.
Cultural Change
Cost optimization requires buy-in from engineering teams. We made cost visibility part of our dashboards.
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