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Your AI Feature Has a Hidden Price Tag
Why is my small AI app generating such a large bill? A worked guide to the hidden tradeoffs, practical checks, and implementation decisions.
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Why is my small AI app generating such a large bill? A worked guide to the hidden tradeoffs, practical checks, and implementation decisions.
How can I reduce costs without making answers worse?
Start with an instrumented sample of representative tasks, recording model, input size, output size, duration, retries, and outcome. Remove sensitive prompt contents from routine logs where possible. The first useful chart often shows a small number of unusually expensive paths rather than an evenly distributed problem across the whole application.
Reduce unnecessary context before changing the model. Send the relevant document sections, preserve a compact task state, and avoid appending repeated instructions or tool output indefinitely. Check that trimming does not remove the evidence needed for the answer; a smaller prompt that causes unsupported conclusions is an expensive quality regression.
Then compare alternatives on a fixed evaluation set. A simpler model may handle classification well while a more capable one remains useful for difficult synthesis. Route by task requirements and observed failures, not by a blanket assumption that every request needs the strongest available model or the cheapest available option.
Finally, add explicit limits for retries, tool iterations, and maximum task spend, with a clear user-facing outcome when a limit is reached. Review the cost per successful task again after the change. Keep the optimization only if it improves the overall result, including reliability and the amount of manual correction your users need.
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