Why LLMs in Production Are Hard
Everybody can get a demo working in 30 minutes. The hard part is deploying an LLM integration that handles thousands of users and fails gracefully. After shipping 15+ AI products, here is what we learned.
1. Architecture First
LLM calls are slow (500ms–5s), expensive, and non-deterministic. Build a dedicated AI service layer — a FastAPI microservice that handles all LLM interactions independently.
2. Caching Saves Money
A Redis exact-match cache cuts API costs 40–60% for most applications. Implement semantic caching for even higher hit rates.
3. Cost Control
GPT-4o costs 15× more than GPT-4o-mini. Route simple tasks to cheap models. Only escalate when needed. Set hard budget limits per user session.
