The Economics of AI: Cost, Latency, and Infrastructure Tradeoffs pdf

The missing guide to making AI systems economically viable.

AI prototypes are cheap. AI production systems are expensive. A feature that costs $50/month during testing can cost $50,000/month at production scale. Most teams discover this too late. This book teaches you the economics before the bill arrives.

The Economics of AI is a comprehensive guide to understanding, managing, and optimizing the costs of AI systems. Written by Alex Merced, Head of Developer Relations at Dremio, it covers the full cost stack from token-level economics to organizational FinOps practices.

What you will learn:

• Understand token economics: why output tokens cost 3-5x more than input tokens and how to manage that asymmetry
• Compare pricing across GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, GPT-5-nano, and open-weight alternatives
• Make GPU infrastructure decisions: NVIDIA B200 vs. H100, cloud vs. on-premises, reserved vs. spot instances
• Implement caching strategies that reduce costs by 40-60% (exact match, semantic, KV cache, prompt caching)
• Design model routing that directs 60% of queries to cheap models, saving up to 83% of costs
• Optimize latency-cost tradeoffs with speculative decoding, batch APIs, and provider-level pricing tiers
• Manage retrieval-generation cost balance with optimal chunk sizing and re-ranking ROI
• Build batch processing architectures that cut API costs by 50% for non-interactive workloads
• Implement AI FinOps with cost allocation, monitoring, circuit breakers, and agent cost tracking
• Evaluate build vs. buy decisions with TCO models, platform cost estimation, and vendor lock-in mitigation

All pricing data reflects March 2026 API rates with verified specifications for current models.

For AI engineers: The cost optimization playbook for every stage of the AI pipeline.

For engineering managers: Decision frameworks for budgeting, vendor selection, and infrastructure investment.

For finance teams: The economics vocabulary to evaluate and govern AI spending.