Keeping AI Feature Costs Under Control

AI feature costs creep in a specific, predictable pattern: a feature ships cheap in the demo, gets popular, and three months later someone in finance is asking why the LLM line item tripled. The fix is architectural, not a spending freeze.

Prompt caching is the highest-leverage change most teams haven't made. System prompts, tool definitions and few-shot examples rarely change between requests cache them, and you're paying full price only for the genuinely new tokens in each call. On support and coding-assistant workloads we've cut input costs by 60–80% this way alone.

Model routing is the second lever: not every request needs your most capable model. Classify intent cheaply first, route simple lookups and formatting tasks to a small fast model, and reserve the frontier model for genuinely hard reasoning. Most production traffic is simpler than the team assumes.

Set hard budgets and alerting per feature, not just per account — a runaway loop in one feature shouldn't be discoverable only at month-end billing. We wire per-request cost tracking into the same dashboards product teams already watch for latency and error rate, so cost becomes a normal engineering signal instead of a quarterly surprise.

None of this trades quality for cost when done right. The teams that get burned are the ones treating the frontier model as the only tool in the box the teams that win treat cost as a design constraint from day one, the same way they'd treat latency or accuracy.