Caching, Logging & Deployment
Configuration, structured logging, LLM caching, rate limiting, and Docker deployment.
Your AI chat API is beautiful. On your laptop, it streams responses, calls tools, parses structured output. Every request takes milliseconds. You ship it to production Monday morning.
Wednesday afternoon, your first paying customer runs an automation script. 100 requests in one hour. Your Slack floods with alerts: API costs spike. By evening, the bill hits $200. By Friday, $800.
You stare at the logs. Every single request goes to the LLM. Every response is parsed. Every calculation happens fresh. There's no memory. No deduplication. No boundary on who can call what.
This is the gap between "works on my laptop" and "survives its first day in production." Your beautiful code is a beautiful money printer—for OpenAI.
Production isn't just deployment. It's knowing four things: where your configuration lives so you can change it without redeploying; what your service is actually doing so you can debug when it breaks; how to cache responses so the same question doesn't cost twice; and how to say "no more" when demand spikes.
This week, you'll build the shield between your code and catastrophic costs. Configuration that adapts. Logging that explains. Caching that survives. Rate limits that protect.
Let's make your AI service survive contact with the real world.
Practice your skills
Sign up to write and run code in this lesson.
Caching, Logging & Deployment
Configuration, structured logging, LLM caching, rate limiting, and Docker deployment.
Your AI chat API is beautiful. On your laptop, it streams responses, calls tools, parses structured output. Every request takes milliseconds. You ship it to production Monday morning.
Wednesday afternoon, your first paying customer runs an automation script. 100 requests in one hour. Your Slack floods with alerts: API costs spike. By evening, the bill hits $200. By Friday, $800.
You stare at the logs. Every single request goes to the LLM. Every response is parsed. Every calculation happens fresh. There's no memory. No deduplication. No boundary on who can call what.
This is the gap between "works on my laptop" and "survives its first day in production." Your beautiful code is a beautiful money printer—for OpenAI.
Production isn't just deployment. It's knowing four things: where your configuration lives so you can change it without redeploying; what your service is actually doing so you can debug when it breaks; how to cache responses so the same question doesn't cost twice; and how to say "no more" when demand spikes.
This week, you'll build the shield between your code and catastrophic costs. Configuration that adapts. Logging that explains. Caching that survives. Rate limits that protect.
Let's make your AI service survive contact with the real world.
Practice your skills
Sign up to write and run code in this lesson.