Controlling Language Model API Costs: Caching, Model Selection, and Token Budgets

Products that build features on top of language model APIs are often startled by the second month's bill. The cause is rarely a sudden rise in the per-token price. It is a usage pattern that was never measured in the first place.

Understanding where the costs actually come from

Costs are calculated from input tokens and output tokens, and output rates are generally several times more expensive. In many applications, though, it is input tokens that dominate the bill.

The reason lies in the conversation pattern. Every turn resends the entire prior history, so a ten-turn conversation sends the first message ten times. Long system instructions go out with every request, and their cost multiplies by the number of requests.

Prompt caching and what it takes to benefit

Most providers now offer caching for repeated prompt prefixes, at a far lower rate for the cached portion. The savings are large, but they only apply if the prompt prefix is genuinely identical byte for byte.

The design consequence is concrete: put the stable parts — system instructions, examples, reference documents — at the start, and the changing parts at the end. Inserting a timestamp or a username at the front of the prompt wipes out the entire cache without anyone noticing.

Not every task needs the largest model

The biggest savings usually come from choosing the right model per task. Classification, structured field extraction, and format checks run fine on small models at a fraction of the cost.

A tiered pattern works well in practice: run the small model first, and escalate to the large model only when the small one signals uncertainty or its output fails validation. Most requests finish at the first tier.

Trimming context on purpose

Sending the entire conversation history is the easiest route and the most expensive. The alternative is a rolling summary: keep the last few turns intact and replace the older ones with a short summary.

Cap output length explicitly, too. A model given no limit tends to answer at length, and every output token is billed at the highest rate.

Measure per feature, not per month

A monthly bill tells you nothing you can act on. Log token counts and cost on every call, tagged with the feature that triggered it.

Almost always, one or two features consume most of the budget, and often those features are not the ones users value most. That number is what turns cost control from guesswork into a decision.