Getting an AI application to work once is increasingly easy. Operating it reliably is a different engineering problem.

A prototype can tolerate manual steps, unpredictable latency, limited observability and a handful of users. A production AI service cannot.

This lightning talk explores the transition from an AI prototype to a cloud-native system, focusing on the engineering concerns that become important as usage grows: workload isolation, scalability, observability, failure handling, resource consumption, model and service dependencies, and the operational cost of inference.

Rather than focusing on a specific cloud provider or product, we will look at the architectural shift required when AI becomes another production workload that platform teams need to operate.

The goal is to give developers and platform engineers a practical mental model for recognizing when an AI experiment has outgrown its prototype architecture and what needs to change next.