Cost-Aware LLM Pipeline Design
By cam j · Published · Updated
Designs a language-model pipeline that stays within budget: what to cache, how to route easy and hard requests, how many tokens each step gets, and which…
Prompt template
## Cost-Aware LLM Pipeline Design Design a model pipeline for this feature that I can afford to run. - The feature: [textarea: What the pipeline must do for users] - Request volume: [text: Requests per day] - Monthly budget: [text: Amount in dollars] - Quality bar: [textarea: What counts as a good answer and a failure] - Latency target: [text: e.g. under 3 seconds] - Levers to consider: [multi: Prompt caching, Response caching, Model routing, Token budgets, Batching, Smaller context, Fallbacks] Begin by sketching the steps of the pipeline and estimating tokens in and out for each, so cost per request is a number before any optimisation. Then apply the levers: cache stable prompt prefixes and repeated answers; route by difficulty, with a cheap model or rule for easy cases and a stronger model only when a confidence check or classifier says it is needed; set a hard token cap per step and trim retrieved context to what the answer needs. Add a fallback for timeouts and refusals. Define a small eval set built from real requests, and set gates: a release is blocked if quality drops by a stated margin or cost per request rises by more than an agreed amount. Return a diagram in text, a cost table comparing before and after, the routing rules, and the eval gate definition.