AI coding costs are spiraling out of control. I spent months testing, measuring, and optimizing my workflows. Here's how I cut costs by 97% without sacrificing results.
Hi, my name is Tom Smykowski, I'm a staff full-stack engineer. I build and scale SaaS platforms to millions of users, working end-to-end from system architecture to frontend to mobile. On this blog I share practical insights on AI-assisted development and cost optimization strategies
The problem hit me when I noticed my Cursor bills climbing to $5 per request. After investigating, I found the IDE was wasting tokens on cache reads that provided zero benefit. But even after reporting the bug, I realized something bigger: most AI coding tools aren't designed for cost efficiency. They're designed for convenience.
That's fine when you're exploring or prototyping. But when you have production workflows that run hundreds or thousands of times per month, every dollar matters.
I managed to cut my costs from $5 per execution to $0.17. That's 97% savings. The techniques range from simple configuration changes to systematic workflow redesigns.
In the full article, I break down all 10 methods with specific numbers, code examples, and the experiments I ran to validate each approach. You'll learn which models actually perform best for coding tasks (spoiler: it's not the flagship models), how to structure prompts for minimum token usage, and how to build feedback loops that make your workflows smarter over time.