Lesson 2070 of 2116
AI Cost Engineering: Where the Money Actually Goes
Practical levers that cut AI bills 5-10x without quality loss.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2cost engineering
- 3model routing
- 4caching
Concept cluster
Terms to connect while reading
Section 1
The premise
AI costs scale with input and output tokens, model choice, and call volume. Most production AI features have 5-10x of waste in their default architecture, recoverable without quality loss.
What AI does well here
- Routing easy queries to cheaper models and hard ones to expensive ones
- Caching identical or near-identical requests
- Compressing system prompts and few-shot examples without losing meaning
- Streaming and early-stopping to avoid paying for tokens you do not show
What AI cannot do
- Make output free — every token billed is a token generated
- Cache infinitely — caches eat memory and grow stale
- Eliminate the need to track per-feature unit economics
Key terms in this lesson
End-of-lesson quiz
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