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Every word an AI reads or writes is billed. At scale, small per-request costs add up quickly. Three engineering habits keep AI capable while cost stays predictable.
AI models are billed by the token: the small chunks of text, and for some models images, that they read and write. In a pilot with a handful of users the bill barely registers. Roll the same tool out to thousands of people and every unnecessary word is paid for thousands of times a day.
Cost problems are rarely about the price of one request. They come from design choices: sending a whole document when one paragraph would do, using the largest model for every task, or generating the same answer again for everyone who asks the same question.
| Technique | What it does | Where it fits |
|---|---|---|
| Model routing | Sends simple tasks to smaller models and keeps large models for complex reasoning | Sorting, extraction, short replies |
| Semantic caching | Reuses a stored answer when a new question means the same as an earlier one | Policy questions, FAQs, repeated lookups |
| Lean context | Finds and sends only the passages a question needs, instead of whole documents | Assistants over large document collections |
Sorting an email into the right queue doesn't need the model you would use to summarize a contract. A router decides per request, so the expensive model only runs when the task calls for it.
When a hundred customers ask about your return policy, the answer doesn't need to be written a hundred times. A semantic cache recognizes questions that mean the same thing and serves the stored answer. It needs clear rules for when cached answers expire, and it must never reuse answers that depend on who is asking.
Instead of placing a 50-page document in every prompt, the system searches it and sends only the few passages that answer the question. That costs less, and it keeps the model focused on the text that matters.
The best AI system is not the biggest one. It is the one that delivers the most business value per token.
Track cost per request and cost per completed task from the first pilot, next to the business result the tool is meant to deliver. You can then see when a change makes the system cheaper, when it makes it better, and when it does neither. We treat this as part of the engineering work from day one, not a clean-up after launch.
A small unit of text, roughly a short word or part of a word, that a model counts when it reads input and writes output. Providers bill per token, usually at different prices for input and output.
Use smaller models for simple tasks, reuse answers to repeated questions, and send the model only the context it needs. Measure answer quality before and after each change.
From the first pilot. Cost per request and per completed task show early whether the design will stay affordable at full scale.
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