I've watched a lot of dev teams burn weeks on this and I want to save you the time.

The failure pattern is almost always the same: someone grabs an AI API key, wraps it in a function, gets it working locally — then immediately hits a wall in production. Rate limits. Latency spikes. Model responses that looked fine in testing but fall apart with real user inputs.

The issue isn't the model. It's the integration layer.

1. Token budgeting is a product decision, not an afterthought

If you're not thinking about per-user token caps before you launch, you'll wake up to a massive bill. Design your token allocation into the product from day one.

2. Your retry logic is probably wrong

Most devs either retry too aggressively (burning tokens on bad prompts) or not at all (leaving users with silent failures). Exponential backoff with a max ceiling and a user-visible fallback is the move.

3. Streaming matters more than you think

First token latency is what users actually feel. If you're waiting for full completion before rendering anything, your app will feel slow even when the model is fast. Stream from the first token.

4. Separate your prompt engineering from your application logic

When prompts are buried in app code, iteration is painful. Treat them like config — version them, test them, don't let them rot in a string literal.

That's the short version. We built 9AI to handle the infrastructure side so devs can focus on the product — not babysitting API calls.

If you're building with AI APIs and want to talk architecture, drop a reply.