Outcome
Route requests, cache intelligently, compress prompts, batch work, and control token spend per feature.
Production · Days 71-77
Cost and Performance Optimization
AI product margins depend on routing, caching, batching, distillation, compression, token budgets, and model gateways.
Intermediate 7 subtopics 7 daily blocks
Practice builds
AI cost calculatorSemantic cache layerModel router gateway
What to learn
Model cascading: cheap to expensive routing
Semantic caching, output caching, prompt caching
Batch APIs for non-real-time workloads
Distillation: small models trained from big model outputs
Prompt compression with LLMLingua-style approaches
Token budgeting per feature
Model gateways: LiteLLM, Portkey, OpenRouter
Daily study plan
Day 71: Calculate token budget and expected monthly cost for one AI feature.
Day 72: Add output caching for deterministic or near-deterministic tasks.
Day 73: Add semantic caching for repeated user intent.
Day 74: Design a cheap-to-expensive model cascade.
Day 75: Move non-real-time jobs into a batch workflow.
Day 76: Compress long prompts and measure quality loss.
Day 77: Add a model gateway abstraction and provider fallback.