AI Engineering/Phase 11/Lesson 11

Caching, Rate Limiting & Cost Optimization

Most AI startups do not die from bad models. They die from bad unit economics. A single GPT-4o call costs fractions of a cent. Ten thousand users making ten calls per day costs $250 in input tokens alone -- before you charge a single dollar. The companies that survive are the ones that treat every API call as a financial transaction, not a function call.

Build 45 min Python

Prerequisites: Phase 11 Lesson 09 (Function Calling)

#Learning Objectives

Implement semantic caching that serves repeated or similar queries from cache instead of making a new API call
Calculate per-request costs across providers and implement token-aware rate limiting and budget alerts
Build a cost optimization layer with prompt compression, model routing (expensive vs cheap), and response caching
Design a tiered caching strategy using exact match, semantic similarity, and prefix caching for different query types

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.