Million Dollar Lines of Code: An Engineering Perspective on Cloud Cost Optimization

In a recent presentation at QCon, Erik Peterson, Founder and CTO of CloudZero, shared insights on cloud cost optimization, emphasizing the importance of understanding and managing cloud expenses.

Table of Contents

  1. Introduction
  2. Every Engineering Decision Is a Buying Decision
  3. The Engineer’s Role in Software Profitability
  4. Example Scenarios
  5. Lessons Learned
  6. Cloud Efficiency Rate
  7. Conclusion
  8. Appendix: Tools and Frameworks

Introduction

Erik Peterson emphasized that every engineering decision is a financial decision, and cloud cost management is crucial for modern software development. He highlighted the need for engineers to understand the financial implications of their code and make informed decisions to optimize cloud costs.

Every Engineering Decision Is a Buying Decision

Peterson explained that engineers make buying decisions with every line of code they write, impacting the company’s cloud spending. Understanding and managing these costs is essential to prevent unnecessary expenses and optimize resource utilization.

The Engineer’s Role in Software Profitability

Engineers play a critical role in ensuring software profitability by designing cost-effective systems. Peterson shared examples of costly mistakes and how small changes in code can lead to significant cost savings.

Example Scenarios

Death by Debug

An AWS Lambda function incurred $1.1 million in costs due to excessive CloudWatch logging. The solution was to remove unnecessary debug lines before deployment.

The API Costs Money

An MVP’s inefficient API calls to S3 led to $1.3 million in annual costs. The fix involved caching data outside the for loop and reducing redundant API calls.

How To 2x DynamoDB Write Costs

Adding a timestamp to DynamoDB records doubled write costs. Reducing attribute names and optimizing timestamp formats solved the issue.

Leaking Infra-as-Code

Terraform scripts with improper settings led to $1.1 million in costs due to unnecessary EBS volumes. The solution was to implement better resource management practices.

Cost Delivery Network

A single character bug in CDN code caused $648,000 in costs over six days. The fix involved rethinking the code design and ensuring thorough testing.

Lessons Learned

Peterson highlighted key lessons:

  • Storage is Cheap, API Calls Aren’t: Minimize API calls and optimize data storage.
  • Infinite Scale, Limited Wallet: Focus on efficiency and avoid unnecessary expenses.
  • Premature Optimization is Evil: Prioritize functionality first, then optimize for cost.

Cloud Efficiency Rate

Peterson introduced the Cloud Efficiency Rate, a simple metric to guide optimization efforts. It is calculated as (Revenue - Cloud Costs) / Revenue, helping companies understand their cloud spending efficiency.

Conclusion

Optimizing cloud costs requires understanding the financial impact of engineering decisions. By adopting best practices and focusing on efficiency, companies can achieve significant cost savings and improve software profitability.

For more detailed insights, you can watch the full presentation on InfoQ: Million Dollar Lines of Code: An Engineering Perspective on Cloud Cost Optimization.

Appendix: Tools and Frameworks

Tools and Frameworks Mentioned