AI cost is a product decision, not a model setting

Cost follows the workflow

When an AI feature becomes expensive, the first reaction is often to switch to a cheaper model. That can help, but it ignores the larger question: what work is the system asking the model to do?

Repeated instructions, oversized document context, unnecessary conversation history, and unfiltered requests all create cost before model pricing enters the discussion.

Spend where reasoning matters

Many useful applications contain a mix of tasks. Classification, extraction, retrieval, drafting, and complex reasoning do not need the same model or the same amount of context.

Routing each step intentionally makes the economics clearer. So does caching stable outputs and moving deterministic work back into ordinary software.

Protect quality with evaluation

Every optimization should be checked against a representative set of user requests. Lower spend is not an improvement if it creates more review, more corrections, or less customer confidence.

Cost optimization is product design: deciding where intelligence creates value and where simpler systems are enough.