How to Plan AI API Costs Before Launch
A simple planning checklist for estimating AI API costs before a product reaches production traffic.
AI features can feel inexpensive during prototyping because early usage is small. Production traffic changes the shape of the bill quickly.
Before launch, estimate the cost of a real user session instead of only checking the price of one model call. A session can include routing, retrieval, retries, summaries, and background jobs.
Start with the workload
Write down the parts of the product that call an AI model. For each part, estimate:
- Requests per active user
- Average input tokens
- Average output tokens
- Retry or tool-call frequency
- Expected monthly active users
These numbers are rarely perfect, but they make cost risk visible before pricing decisions are locked.
Add a buffer
Real usage is uneven. Long prompts, failed retries, and power users can all push costs above the average case. Add a practical buffer to your estimate and revisit it after launch data arrives.
Review pricing regularly
Provider pricing and model options change often. Recheck assumptions before major launches, paid plan changes, and high-volume campaigns.