Last year I helped a startup integrate the OpenAI API into their product. It was a chat feature — users could ask questions about their data and get natural language answers. The integration took about a day. Three days after launch, the founder messaged me: "Hey, something's wrong. Our AWS bill just showed an unexpected charge."
It was $340. For three days. They had 60 users.
The issue wasn't a bug — it was that production API usage looks nothing like a tutorial. The tutorial shows you openai.chat.completions.create() and returns a response. The tutorial doesn't show you what happens when users send 500-token messages, when they open 15 browser tabs each maintaining their own chat context, or when one user fires requests 30 times per minute because they think it's broken.
This guide covers what the tutorials skip: rate limiting, token counting, cost guards, streaming, error handling with retries, and model selection. These aren't optional additions — they're what separates a demo from a production feature.
Why Production Is Different
