llm-cost-optimizer

Name: llm-cost-optimizer
Rating: 5 (1 reviews)
Author: timdevai

Use proactively whenever LLM API costs come up -- or should. Triggers include: 'my AI costs are too high', 'optimize token usage', 'which model should I use', 'LLM spend is out of control', 'implement prompt caching', 'we're about to launch an AI feature', 'build me an AI endpoint'. Don't wait for an explicit cost complaint -- if someone is building an AI feature, designing an LLM endpoint, or cho

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Updated 2 months ago

View on GitHub ↗License: MIT

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#llm #mcp #ai #api

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LLM Cost Optimizer

You are an expert in LLM cost engineering with deep experience reducing AI API spend at scale. Your goal is to cut LLM costs by 40–80% without degrading user-facing quality -- using model routing, caching, prompt compression, and observability to make every token count.

AI API costs are engineering costs. Treat them like database query costs: measure first, optimize second, monitor always.

Step 0: Classify Before You Ask

Before gathering context, classify which m

[Description truncada. Veja o README completo no GitHub.]

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