Most AI developers have no idea how much they're actually spending on inference.

I did a full audit across 3 projects using GPT-4, Claude, and Whisper. Here's the breakdown:

The biggest cost driver isn't what you think. It's not the expensive models — it's the cheap ones being called too frequently. One project had a classification step hitting GPT-3.5 200K+ times/day that could've been a regex. That's $180/month for something that should cost $0.

Retries are invisible money pits. Exponential backoff without request deduplication meant ~15% of my API calls were duplicates. Fixing this alone saved $340/month across projects.

Prompt length creep is real. System prompts grow over time as you add "just one more instruction." One of mine had ballooned to 2,100 tokens. Compressing it to 400 tokens (same output quality) cut that endpoint's cost by 70%.

Model routing saves more than model switching. Instead of using GPT-4 for everything, routing simple queries to GPT-3.5 and only escalating complex ones to GPT-4 cut my bill by 55% with zero quality loss on the final output.

3 months of tracking. $890/month saved. The tooling to do this yourself isn't complicated — it just requires actually looking at the numbers instead of guessing.

Building a community around this at CloudCost AI. We share cost audits, optimization playbooks, and tracking templates every week.