llm-cost-optimization

name	llm-cost-optimization
description	Reduce LLM API and infrastructure costs through model selection, prompt caching, batching, caching, quantization, and self-hosting strategies. Track spend by team and model, set budgets, and implement cost-aware routing.
license	MIT
metadata	{"author":"devops-skills","version":"1.0"}

LLM Cost Optimization

Cut LLM costs by 50–90% with the right combination of caching, model selection, prompt optimization, and self-hosting.

When to Use This Skill

Use this skill when:

• LLM API spend is growing faster than revenue
• You need to attribute AI costs to teams, products, or customers
• Implementing caching to avoid redundant LLM calls
• Deciding when to switch from API providers to self-hosted models
• Optimizing prompt length without sacrificing quality

Cost Levers by Impact

Strategy	Typical Savings	Effort
Semantic caching	20–50%	Low
Model right-sizing	30–70%	Low
Prompt compression	10–30%	Medium
Provider caching (prompt cache)	10–25%	Low
Batching offline workloads