Question

You operate a production chat application backed by an Azure AI Foundry model deployment. A newer version of the base model has been released, and you must roll it out to real users while minimizing risk. Your requirements are: expose the new version to only a small percentage of live traffic initially, keep the existing version serving the majority of requests, and be able to shift traffic gradually or roll back instantly if quality metrics degrade. Which deployment approach best meets these requirements?

Accepted Answer

Create a second deployment for the new model version and use traffic allocation to route a small percentage of requests to it, adjusting the split over time.

Answer

Update the existing deployment in place to the new model version so all traffic immediately uses the latest model.

Answer

Deploy the new version to a separate Foundry project and ask users to opt in by switching endpoints manually.

Answer

Increase the provisioned throughput quota on the current deployment and enable autoscale to absorb the new version's load.