llm-cost-optimization

Optimize costs for LLM applications. Covers token optimization (prompt compression, caching, context pruning), model routing (expensive vs cheap models), semantic caching, prompt caching, response caching, model selection strategy (GPT-4o vs GPT-4o-mini vs Claude vs open-source), batch API usage, fine-tuned small models vs large models, cost monitoring and budgeting, rate limit management, embedding cost optimization, and ROI analysis for LLM features. Use when reducing LLM API costs, implementing caching, choosing cost-effective models, or building cost-aware LLM architectures.

Version

1.0

License

Apache-2.0