Models
The foundation models you can run on and create endpoints on, how to choose a base, and how an upgrade moves an endpoint to a newer base.
api.example.com, keys, and ids are placeholders.Data and Endpoints are the objects you create; every self-serve operation is inference. "Model" always means a base model.
The model registry is the documentation of record for every foundation model, current and retired: intended use, training data, evaluation, lifecycle status, and known limitations.
Choosing a base
Use the latest foundation model: it is what runs and new endpoints use when base is omitted, and it supports all three prediction task types (classification, regression, anomaly). Name a specific base only when you need a fixed comparison point.
A retired model answers runs, creations, and serves with 422 capability_unavailable. An endpoint created on it keeps that base until you move it with POST /v2/endpoints/:id/upgrade (see Endpoints); its earlier reports keep their base name, so the trail stays readable.
Bases are named model-{n} (s{n} in report names); the API version /v2/ is a separate axis. See Identifiers.