Token Economics

Token economics refers to the cost structure and efficiency considerations associated with the consumption of tokens in large language model APIs. LLMs process text as sequences of tokens (roughly 0.75 words per token in English), and API pricing is based on the number of input and output tokens consumed. Token economics encompasses not just direct API costs but also the architectural and design tradeoffs driven by token consumption — including context window limits, prompt length, and the cost implications of different RAG and agentic patterns.

For enterprise AI programs, token economics is a first-order business concern. Unoptimized LLM applications can incur surprisingly high API costs at scale — a naive implementation of a product catalog enrichment pipeline or high-volume customer service chatbot can generate millions of tokens per day. Token economics drives decisions about when to use smaller, cheaper models versus frontier models, how to design prompts for efficiency, when to cache outputs, and how to architect RAG systems to minimize unnecessary context. Understanding and actively managing token consumption is essential for building AI applications that remain economically viable as they scale.