Most AI demos look great on a laptop. The problems start when you try to put them in front of real users. Latency gets unpredictable, costs climb fast, the model times out halfway through a request, and suddenly your nice demo is a support ticket.

In this talk I'll walk through how I approach shipping AI features to production from the backend side. I'll cover the patterns that have actually helped me: handling long-running model calls with queues and background jobs, streaming responses so users aren't staring at a spinner, retries and fallbacks when a provider is slow or down, caching to keep costs sane, and basic guardrails around inputs and outputs.

Then I'll move to deployment. I'll show how I package and deploy an AI-backed service on the cloud (GCP), what I watch in terms of monitoring and logs, and the mistakes I've made along the way so you don't have to repeat them.

I'll finish with a live demo. I'll take a simple AI feature built with NestJS, deploy it, and show it running end to end.

I work on backend systems handling around 20M requests a day, so this comes from real production work, not theory. You should leave with patterns you can use on Monday.