Model endpoints on Amazon SageMaker get held to machine learning standards rather than service standards. Teams track accuracy, drift, and prediction latency carefully, while availability behavior gets far less scrutiny even though the endpoint is a production service like any other.

The gap shows up in the calling application. Because a model is treated as authoritative, there's frequently no fallback path at all. When inference is slow or unavailable, the feature it powers has nowhere to go, and the failure reaches your customer as a broken product experience rather than a degraded one.

Add Failure Flags to your inference handler and you can force the question into the open. Inject latency into a feature store call and see whether your application waits or moves on. Inject an error and find out what the user gets when no prediction arrives.

Whether the answer is a cached prediction, a simpler heuristic, or a clearly communicated limitation, having decided in advance beats discovering there was no plan.