Inference

Inference applies a trained model to new inputs to produce outputs such as predictions or text; it is distinct from further model training.

Each use is an operational event

When AI interprets a customer objection or generates a draft reply, a trained model is being executed. These events accumulate with regular use. One conversation may require several model calls for analysis, clarification and revision. The price of a single call therefore does not describe the cost of a completed task. A sales process also depends on receiving the answer in time. Low-cost processing may be unsuitable if it interrupts a conversation or delays preparation of a proposal.

Compare completed work rather than isolated calls

The token budget, number of calls and additional tools affect the overall effort. Measure latency across the complete workflow, including follow-up questions and review. Testing ordinary and difficult cases reveals how costs and response times vary. Economic measures should concern reliably usable outcomes. Whether inputs are retained or used for other purposes is a separate contractual and configuration question; the word inference does not answer it. Those conditions belong in the decision alongside measured performance and the responsibilities required to operate the application.

Sources

• What is LLM inference?