How We Saved $500K on Cloud Costs Without Sacrificing Performance

Cloud costs can spiral out of control fast. Here's how we brought ours back down to earth.

The Problem

Our AWS bill had grown 300% in 18 months. But revenue hadn't. Time to optimize.

What We Did

1. Automated Resource Tagging

• Built a system to tag all resources with owner, project, and environment
• Set up cost allocation reports
• Made spending visible to engineering teams

2. Right-Sized Everything

• Analyzed actual CPU/memory usage vs provisioned capacity
• Downsized over-provisioned instances (most were at 20-30% utilization)
• Moved appropriate workloads to spot instances

3. Implemented Auto-Scaling

• Set up proper auto-scaling policies based on business metrics
• Scaled down non-prod environments during off-hours
• Saved 40% on dev/staging infrastructure

4. Storage Optimization

• Automated S3 lifecycle policies
• Moved cold data to Glacier
• Cleaned up orphaned EBS volumes and snapshots

5. Reserved Capacity

• Purchased 1-year reserved instances for predictable workloads
• Used Savings Plans for compute flexibility

The Results

• $500K annual savings (42% reduction)
• Zero performance degradation
• Better visibility into spending patterns
• Engineering teams now care about costs

Key Takeaways

1. Make costs visible — What gets measured gets managed
2. Automate everything — Manual optimization doesn't scale
3. Involve engineering — They control most spending decisions
4. Monitor continuously — Cloud efficiency requires ongoing attention

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