Cloud Cost Optimization: A CTO Playbook

Cloud costs have a way of creeping up. What started as a $10K monthly bill becomes $50K, then $100K. Suddenly cloud spend is a board-level conversation.

I learned this lesson the hard way early in my career. At one company, our AWS bill jumped 40% in a single month. The culprit? A well-meaning engineer had spun up a fleet of large instances for load testing and forgot to terminate them. We had no cost alerts, no tagging, and no visibility. That experience shaped how I approach cloud cost management today.

I have helped companies reduce cloud costs by 30-50% without sacrificing performance or reliability. Here is the playbook.

Start with Visibility

You cannot optimize what you cannot see. Before making any changes:

Enable detailed billing. Turn on cost allocation tags in AWS, labels in GCP, or tags in Azure. Tag every resource by team, environment, and service.

Set up cost anomaly detection. Configure alerts for unusual spending patterns. Catch runaway costs before they become disasters.

Create dashboards. Every team should see their cloud spend. Visibility drives accountability.

The 80/20 of Cloud Costs

In most organizations, 80% of cloud spend comes from:

1. 1Compute (40-50%): EC2, ECS, EKS, Lambda
2. 2Storage (15-25%): S3, EBS, RDS storage
3. 3Data transfer (10-20%): Inter-region, internet egress
4. 4Databases (10-15%): RDS, DynamoDB, ElastiCache

Focus your optimization efforts here first.

Compute Optimization

Right-Sizing

Most instances are over-provisioned. Use AWS Compute Optimizer, GCP Recommender, or Azure Advisor to identify right-sizing opportunities.

Common wins:

• Development environments running production-sized instances
• Memory-optimized instances for CPU-bound workloads (or vice versa)
• Instances running at < 20% average CPU utilization

Reserved Instances and Savings Plans

If you have predictable baseline capacity, commit to it:

Start with 1-year commitments for your baseline. Use on-demand for variable capacity.

Spot Instances

For fault-tolerant workloads (batch processing, CI/CD, stateless services), Spot instances offer 60-90% savings.

Requirements:

• Application can handle interruption with 2-minute warning
• Workload can be distributed across multiple instance types and AZs
• State is externalized (not on the instance)

Auto-Scaling

Properly configured auto-scaling can reduce compute costs by 30-40%:

• Scale down during off-hours (nights, weekends)
• Use target tracking policies based on actual utilization
• Set appropriate min/max bounds
• Test your scaling policies under load

Storage Optimization

S3 Lifecycle Policies

Most S3 data is accessed frequently for 30 days, then rarely:

• Standard: Active data (first 30 days)
• Intelligent-Tiering: Unknown access patterns
• Glacier Instant Retrieval: Archival with occasional access
• Glacier Deep Archive: Long-term retention (cheapest)

Implement lifecycle policies to automatically transition data.

EBS Optimization

• Delete unattached volumes (they still cost money)
• Snapshot old volumes and delete them
• Use gp3 instead of gp2 (20% cheaper, better performance)
• Right-size volumes (you can increase size, but not decrease)

Database Storage

• Enable storage auto-scaling with appropriate limits
• Use Aurora I/O-Optimized for I/O-heavy workloads
• Consider Aurora Serverless v2 for variable workloads

Data Transfer Optimization

Data transfer costs are often a surprise. Reduce them by:

Keep traffic in-region. Cross-region transfer is expensive. Deploy services in the same region as their data.

Use VPC endpoints. Traffic to AWS services through VPC endpoints is free. Traffic through NAT gateways is not.

Compress everything. Enable gzip/brotli compression. It reduces transfer costs and improves performance.

Consider CDN. CloudFront pricing is often cheaper than direct S3/EC2 egress, plus you get caching benefits.

Quick Wins Checklist

• [ ] Delete unused resources (idle EC2, unattached EBS, old snapshots)
• [ ] Stop development environments outside business hours
• [ ] Enable S3 Intelligent-Tiering for buckets with unknown access patterns
• [ ] Switch from gp2 to gp3 for EBS volumes
• [ ] Purchase Savings Plans for baseline compute capacity
• [ ] Enable data transfer compression
• [ ] Set up billing alerts and anomaly detection

Building a Cost-Conscious Culture

Optimization is not a one-time project. Build cost awareness into your engineering culture:

Make costs visible. Show teams their spending in weekly metrics reviews.

Incentivize efficiency. Celebrate teams that reduce costs. Make it part of performance reviews.

Architect for cost. Include cost estimates in design documents. Consider cost implications in architecture decisions.

Regular reviews. Monthly cost reviews with engineering leads. Quarterly deep-dives on major cost categories.

The Bottom Line

Cloud cost optimization is not about cutting corners. It is about eliminating waste and making intentional tradeoffs. The money you save on infrastructure is money you can invest in product development, talent, and growth.

Start with visibility, focus on the big categories, and build a culture of cost awareness. The savings compound over time.