AI features live or die on cost-per-task — and routing, caching, and prompt discipline routinely cut LLM spend 60–90% without touching
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Prices across model tiers span two orders of magnitude. Route easy calls to small models, reserve frontier models for hard ones, and re-benchmark quarterly — the router is architecture, not an optimization afterthought.
Identical and near-identical requests are common in production; response caching, embedding reuse, and prompt-prefix caching convert repeat work into near-zero cost.
Tokens are the meter: trim boilerplate, retrieve only relevant context, and cap output lengths. Bloated prompts are the most common silent budget leak.
Cost-per-task by feature, visible from day one, with alerts on drift — because the bill that surprises you in month three was visible in week one.
Skipping the discipline this article describes until an incident, audit, or stalled project forces it — every practice above is cheaper adopted early than retrofitted under pressure.
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