Token management is the discipline of controlling, optimizing, and tracking how Large Language Models (LLMs) consume input and output tokens—the fundamental units text is broken into for processing. With tokens driving both API costs (often $2–$15 per million output tokens in 2026) and context window limits (typically 128K–2M tokens), effective token management determines whether AI applications run efficiently or exhaust budgets and hit overflow errors. Key insight: output tokens typically cost 3–5× more than input tokens, and reasoning models can generate thousands of hidden tokens per request—making optimization a production necessity, not an afterthought.