Cost and Latency Trade-offs as Product Constraints
Explore how to manage cost and latency as product constraints in AI applications by making informed call decisions, controlling context size, and implementing visible limits. Understand how to design logs and tests that monitor these constraints to maintain responsiveness and cost-effectiveness while delivering predictable AI behavior.
A text helper that calls the model on every keystroke feels responsive only until it starts queuing requests, flashing spinners, and returning suggestions that no longer match the current input. The same helper can feel faster and cheaper when it waits for deliberate moments, sends less context, and makes its limits visible when it declines to call the model.
This lesson treats latency as a responsiveness budget and token usage as a cost and time driver, then turns both into UI behavior and logs.
Turn constraints into call decisions
With the constraints anchored, the next step is deciding when a request is not worth making. A request is wasteful when the UI signal indicates the user will invalidate the result before it returns or when the input does not contain enough information to produce a useful output.
Common waste signals that map cleanly to product behavior include: