Controlling LLM Costs in Production

An AI feature that works in a demo can become expensive at scale, since every request costs money. Keeping costs sane is mostly about not doing unnecessary work, the same as any other system.

Match the model to the task

Use a smaller, cheaper model for simple jobs and reserve the large one for tasks that genuinely need it. Most requests do not need the biggest model available.

Cut redundant work

• Cache answers to repeated or similar questions.
• Trim the context you send to what the task needs.
• Set sensible limits so a runaway loop cannot drain the budget.

Measure cost per feature

Track spend by feature, not as one lump. When you know which feature costs what, you can decide where optimisation is worth the effort.