llm-cost-optimization

What problem does it solve?

This Skill addresses the rapidly escalating costs associated with using Large Language Models (LLMs) by providing strategies to significantly reduce API and infrastructure expenses.

Core Features & Use Cases

• Cost Reduction Strategies: Implements techniques like semantic caching, model right-sizing, prompt compression, batching, and self-hosting to cut LLM expenses.
• Cost Tracking & Attribution: Enables tracking of AI spend by team and model, and setting budgets.
• Use Case: A startup is spending $10,000/month on LLM APIs. By implementing semantic caching and routing simpler tasks to cheaper models, they reduce their spend to $2,000/month, freeing up budget for core product development.

Quick Start

Use the llm-cost-optimization skill to analyze your current LLM spend and identify the top 3 strategies for immediate cost savings.