Q: What's the minimum setup to deploy an AI app to production?

Five things separate a laptop prototype from a deployed app. None of them are exotic.

1. Secrets out of the code. Your API keys move into environment variables, never into the source.

2. A hosting platform. Your app runs on a server somewhere: a simple platform like Vercel or Render while you learn, or AWS / Azure / GCP for enterprise-grade deployments.

3. Error handling. In production, API calls fail, users type nonsense, and services time out. Your app needs to catch failures and retry or degrade politely instead of crashing.

4. Logs. When something breaks at 11pm, print statements on your laptop won't help. Your app writes logs where you can read them on the platform.

5. A cost guard. Set a spending limit on your API account before strangers can trigger calls on your key.

That's the minimum. As your app grows, you add the heavier machinery: evals watching quality, monitoring dashboards, CI/CD so deployments are automatic and repeatable, and guardrails on input and output.