Inference Cost

Inference Cost is the primary variable expense associated with running generative AI in production. In machine learning, 'training' is the highly expensive, one-time process of building the model. 'Inference' is the ongoing process of actually using the model to generate answers. Every time an employee types a question into a chat window, the AI performs inference, and the enterprise incurs a cost.

For Large Language Models (LLMs), inference costs are typically calculated per 'token' (roughly 3/4 of a word). Providers like OpenAI or Google charge a specific rate for input tokens (the prompt you send) and a different, usually higher rate for output tokens (the answer the model generates). Because enterprise workflows often rely on Retrieval-Augmented Generation (RAG)—which involves stuffing thousands of words of background documents into every single prompt—inference costs can scale exponentially and unpredictably. A single complex query to a frontier model can cost several cents, which quickly turns into millions of dollars when scaled across 10,000 employees.

Managing inference cost is the core function of AI FinOps. Organizations must implement governance platforms that provide visibility into token consumption. Advanced strategies to lower inference costs include 'prompt caching' (reusing identical prompts without re-computing them), utilizing smaller, task-specific open-source models for routine tasks, and enforcing hard Department Budgets to prevent uncontrolled API spending.