Developers advised to model LLM inference costs before production launch

Developers often underestimate LLM inference costs after prototypes with short prompts expand to production-scale traffic with longer conversations. Modeling costs requires estimating token counts for realistic input prompts and expected output responses. Many API providers charge separate rates per million tokens for input and output, requiring calculations based on projected daily request volumes. Using tokenization libraries like tiktoken helps create representative test requests that mirror actual application usage. Accurate forecasting must account for production variables like retries, prompt changes, and traffic growth.