An AI Token is the fundamental unit of text that large language models use to process and generate language. Tokens are not exactly words — they are pieces of text determined by the model's tokenizer, which breaks text into manageable chunks for processing.
Tokenization varies by model: GPT-4 uses roughly 1 token per 4 characters of English text (approximately 0.75 words per token), meaning a 1,000-word document is approximately 1,333 tokens. Different languages tokenize differently — some Asian languages may use more tokens per character. Special characters, code, and structured data may also tokenize differently than natural language.
Tokens are important for enterprise AI management for several reasons: cost management (AI API pricing is typically based on tokens consumed — both input and output), context window limits (models have maximum token limits per conversation, affecting what information can be processed), performance implications (longer token sequences increase latency and processing time), budget planning (estimating AI costs requires understanding token usage patterns), and security monitoring (token analysis can reveal data volume flowing through AI tools).
Organizations managing AI costs and governance should monitor token usage across departments and tools, set token budgets and alerts, optimize prompts for token efficiency, and understand how different use cases consume tokens to make informed decisions about AI tool allocation and spending.
