Adding AI to your product without wrecking your margins
Adding an AI feature is easy; knowing what it costs every month is not. Four decisions that keep it predictable.
The technical side of adding AI is easier than most teams expect. The hard part is knowing how the bill grows with your user count. In classic software an extra user is nearly free; in a product that calls a model, every use is a direct cost.
1. Not every request needs the model
A large share of requests repeat. Cache the result instead of regenerating it — in a typical product this removes 30–60% of model cost and improves latency too.
2. Match the model to the job
Classification, tagging and short summaries run fine on small, cheap models. Escalate to the large model only where real reasoning is required. Using two models in one product is good engineering, not an exception.
3. Shorten the context
Most of the cost comes from the text you send. Select the relevant slice instead of shipping the user's whole history — it is cheaper and more accurate.
4. Set ceilings before launch
Never ship without per-user limits, a queue and a fallback path. A bug or a bad actor multiplying your bill overnight is a practical risk, not a theoretical one.
Measure three numbers
Cost per request, cost per user per month, and actual usage rate of the feature. The third is usually the most surprising.
AI is a great feature where it earns its place; added for show, it is just a line item.
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