Token economics is the discipline of understanding and modeling the per-token costs and revenue of AI applications. Tokens (sub-word units of text) are the unit of pricing for most LLM APIs and the basis on which AI application unit economics must be modeled. The model includes input tokens (prompt + context + RAG content), output tokens (model response), cache savings, and the per-query economics that determine whether AI applications are profitable. It's the financial layer beneath every AI application.
What a token is:
Token: sub-word unit of text used by LLMs. Roughly 0.75 English words per token, or 4 characters.
Tokenization examples: