AI costs are deceptively simple in a prototype. One model, a few calls, a negligible API bill. In production, those same calls multiply by users, by features, by volume — and the bill grows in ways nobody planned for.
The fix is not to spend less. It is to spend predictably, and to know where every dollar goes.
Token budgets
Every feature that calls an LLM should have a token budget: a maximum number of input and output tokens per call, enforced at the application layer. Without budgets, a single verbose prompt or runaway chain can consume in minutes what you expected to spend in a day.
Budgets also force better prompt engineering. When you have a limit, you write tighter prompts, retrieve more relevant context, and structure outputs more efficiently. The constraint improves the system.
Caching
Many LLM calls are repetitive. The same question asked by different users, the same document summarized multiple times, the same classification applied to similar inputs. A semantic cache — keyed on input similarity rather than exact match — can eliminate a significant percentage of redundant calls.
The savings compound: fewer calls mean lower cost, lower latency, and less load on rate-limited APIs.
The most expensive API call is the one you didn't need to make.
Model routing
Not every task needs the most capable model. Classification, extraction, and formatting tasks often perform identically on smaller, cheaper models. A routing layer that matches tasks to the appropriate model tier can cut costs dramatically without affecting output quality.
The key is measurement: route, compare, and verify that the cheaper model meets the quality bar before committing to it in production. Your evaluation suite makes this possible.