Before launching an AI feature, token budgeting is often overlooked. A simple template helps teams put product assumptions, traffic expectations, and model pricing into one place instead of estimating by intuition.
Template Fields
Record scenario name, daily requests, average input tokens, average output tokens, cache hit ratio, model name, and safety margin.
Scenario name separates support bots, summarization, code generation, and other features. Daily requests come from product forecasts. Average input tokens should include system prompts, context, and user input. Average output tokens should come from real sample tests.
Example Calculation
For a summarization feature: daily requests 2,000, average input 4,000 tokens, average output 600 tokens, cache hit 20%, and safety margin 30%.
Estimate cost per request, multiply by 30 days, then apply safety margin.
budget = cost per request × 2,000 × 30 × 1.3Putting these numbers into the text model calculator makes it easy to compare monthly cost across models. For a broader planning workflow, continue with monthly AI API budget planning.
Why Add a Safety Margin
After launch, user behavior is usually more variable than testing: longer questions, longer outputs, retries after failures, concentrated traffic spikes, and new scenarios added by product teams.
A safety margin is not waste. It prevents the bill from surprising the team.
Update Weekly After Launch
During early launch, update the template weekly. Replace assumptions with real average tokens, request volume, and billing data.
If actual cost differs by more than 20%, review prompts, model choice, caching, and rate limits, then use the bill checking guide to separate usage changes from pricing misunderstandings.
Usage Tips
Create one row per feature, separate free and paid users, track staging and production separately, and add alerts for high-cost scenarios.
The goal of a token budget template is not cent-level precision. It is to make cost structure visible before launch.