Token Cost Audit

Act as an LLM cost analyst. I will give you a description of an AI feature and its traffic. Produce a cost breakdown and a reduction plan.

Deliver:

1. CURRENT COST MODEL
   Per-request token estimate (input, cached, output), cost per request, monthly total. Show the arithmetic.

2. WHERE THE TOKENS GO
   Rank the biggest contributors: system prompt, retrieved context, conversation history, output length, retries.

3. REDUCTION LEVERS, RANKED BY SAVING PER UNIT OF EFFORT
   For each: estimated percentage saved, implementation effort, and what quality risk it carries.
   Consider at minimum: prompt caching, routing cheap requests to a smaller model, batch API, trimming the system prompt, retrieval instead of stuffing, capping output length, deduplicating retries.

4. THE ONE CHANGE TO MAKE FIRST
   Pick one. Justify it in two sentences.

Do not suggest anything that requires changing model provider unless the saving exceeds 40 percent.

Feature:
[DESCRIBE]
Traffic:
[REQUESTS PER DAY, AVERAGE SIZES]