What Is AI Cost Optimization?

Complete guide to AI cost optimization: strategies, tools, and best practices.

AI cost optimization is the practice of minimizing AI and LLM API expenditure while maintaining output quality. It involves model selection, prompt engineering, caching, and spend enforcement.

The 4 Pillars of AI Cost Optimization

Model routing: matching model capability to task complexity
Prompt optimization: minimizing token count without losing quality
Caching: eliminating redundant API calls
Budget enforcement: hard limits that prevent overruns

Why It Matters

AI costs directly impact your gross margin. A SaaS product with 30% AI cost per request has fundamentally different unit economics than one with 5% AI cost. Optimization is not optional; it is a business viability question.

Frequently Asked Questions

What is model routing in AI cost optimization?

Model routing sends each request to the cheapest model that can handle it. Simple tasks use small, cheap models ($0.25/M tokens). Complex reasoning uses large models ($60/M tokens). This can reduce costs by 60-80%.

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