Module 9 - Cost & FinOps for AI
"The most expensive model is the one nobody can afford to run."
AI systems fail in production for two reasons: they don't work, or they cost too much to keep running. Most engineering education focuses on the first problem. This module focuses on the second - and the second kills more ML projects than the first.
What You'll Learn
This module gives you the economic framework to build AI systems that are sustainable - not just technically impressive. You'll learn how to model costs before they surprise you, optimize at every layer, and make defensible build-vs-buy decisions backed by real numbers.
Module Map
Lessons in This Module
| # | Lesson | Core Skill |
|---|---|---|
| 01 | ML Cost Models | Build a complete cost visibility dashboard |
| 02 | Training Cost Optimization | Reduce a 12K |
| 03 | Inference Cost Optimization | Cut LLM API spend by 75% |
| 04 | Build vs Buy Analysis | Financial framework for platform decisions |
| 05 | Cloud Cost Management | Reserved instances, tagging, alert systems |
| 06 | Model Efficiency Economics | Accuracy-cost Pareto analysis |
| 07 | ML ROI & Business Cases | Iron-clad ROI cases for stakeholders |
Key Concepts
- Cost per prediction - the fundamental unit of ML economics
- TCO - total cost of ownership beyond the obvious cloud bill
- Compute-optimal training - Chinchilla scaling for cost-efficient training
- Spot instance strategy - how to use cheap preemptible hardware safely
- Accuracy-cost Pareto frontier - knowing when "more accurate" isn't worth the cost
- FinOps maturity - from zero visibility to full cost attribution
Why This Module Matters
Cloud bills are the #1 reason ML projects get cancelled after launch. A model that works in the lab but costs $2 per API call will never reach production scale. A training run that blows the quarterly budget once will never be approved again. Understanding ML economics is not optional - it is a core engineering competency for anyone building AI systems in 2024 and beyond.