How to reduce LLM API costs without hurting quality

The fastest way to overspend on AI is to send every request to your most capable (and most expensive) model. A large share of production traffic — classification, extraction, short chat, routine tool calls — is handled just as well by models that cost a fraction as much. The trick is cutting cost without guessing where quality would break.

Five levers that actually work

1. Route by cost with a quality floor

Instead of "cheapest model," optimize for "cheapest model above a quality threshold." That keeps easy work cheap and protects the requests that genuinely need a frontier model. This is the heart of LLM routing.

2. Cache repeated work

Identical or near-identical prompts (system prompts, RAG contexts, common questions) can be served from cache instead of re-billed to the provider.

3. Measure cost per successful task, not per token

A cheaper model that fails and forces a retry isn't cheaper. Optimize on the cost to get a correct result, which sometimes means the pricier model is the frugal choice.

4. Set budgets and degrade gracefully

Cap spend per workspace or key, and when a budget is hit, degrade to the lowest-cost path rather than surprising finance at month end.

5. Right-size context

Trim oversized prompts and pick models whose context window matches the job — you pay for tokens you send, not just tokens you need.