---
title: Models
url: https://docs.schemalabs.ai/models
description: The Schema 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.
---

# Models

> The Schema 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.

Data and Endpoints are the objects you create; every self-serve operation is inference. "Model" always means a Schema base.

The [model registry](https://www.schemalabs.ai/model-card) is the documentation of record for every Schema model, current and retired: intended use, training data, evaluation, lifecycle status, and known limitations. `GET /v2/models` returns the bases available to your org and which one is the default, plus any Enterprise fine-tune you own; lifecycle, evaluations, and task support live on each model card in the registry.

## Choosing a base

Use the latest Schema 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](https://docs.schemalabs.ai/endpoints#upgrade-a-new-base-shipped)); its earlier reports keep their base name, so the trail stays readable.

Bases are named `schema-{n}` (`s{n}` in report names); the API version `/v2/` is a separate axis. See [Identifiers](https://docs.schemalabs.ai/api-reference#identifiers).

## Enterprise fine-tunes

Fine-tuning is an Enterprise program. A fine-tune is served behind an ordinary endpoint you own, bills as ordinary inference, and carries the same held-out report contract. Contact [sales](mailto:info@schemalabs.ai).