Shipping an app that calls an LLM API without a cost estimate is a good way to meet your first surprise invoice. Before you build the integration, it helps to understand the three variables that drive almost every LLM API bill.
1. Input tokens
Everything you send the model counts: the system prompt, the user's message, and any context you attach. Long system prompts and large retrieved document chunks add up fast, especially when they repeat on every single request.
2. Output tokens
Responses are billed separately, and usually at a higher rate than input. Long, detailed answers, reasoning output, and multi-step agent loops can quietly multiply your output volume.
3. Requests per day and model choice
Token counts mean little until you multiply them by traffic. A prototype making dozens of calls a day behaves very differently from a launch making thousands. And model choice changes the per-token rate, so the same workload can cost very differently depending on which model you pick.
Run the numbers up front
Take your expected input size, expected output size, requests per day, and the model you plan to use, then project a monthly total. If the number surprises you, you still have options before launch: trim your prompts, cache repeated context, limit output length, or route high-volume paths to a cheaper model.
A quick way to sanity-check your estimate is to plug your numbers into a free estimator like AI Calculators and see what the month looks like. Estimating first takes minutes; explaining an unexpected bill takes much longer.
Top comments (0)