The Economics of AI: Cost, Latency, and Infrastructure Tradeoffs
Published by Self-published · Tech
Analyzes the cost structures of AI systems and how to optimize spending across compute, latency, and infrastructure.
Who this book is for
Engineering and platform leaders, FinOps teams, AI engineers, CTOs and VPs of Engineering, and product managers who need to make AI systems financially sustainable.
Table of contents
- The Economics Problem
- Token Economics
- Model Selection Economics
- Inference Infrastructure
- Caching and Reuse Strategies
- Latency-Cost Tradeoffs
- Retrieval-Generation Cost Balance
- Batching and Throughput Optimization
- Managing Evaluation Costs
- AI FinOps
- Build vs. Buy Decisions
- Decision Frameworks for AI Economics
Excerpt
AI costs work differently from traditional software costs. A web application's marginal cost per user is close to zero once the infrastructure is in place. Serve one user or one million users: the database query cost barely changes. An AI application's marginal cost scales with every request: every query consumes tokens, every token costs money, and the cost varies dramatically based on model, context size, and output length.
