> ## Documentation Index
> Fetch the complete documentation index at: https://docs.datafold.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Databricks

Serve agent inference from your Databricks workspace's model serving endpoints — foundation models (pay-per-token), provisioned throughput, or external-model gateways. Credentials come from an existing [Databricks data connection](/integrations/databases/databricks); no separate key is stored.

## Prerequisites

A Databricks data connection authenticated with a **personal access token**, **M2M OAuth**, or **Azure Entra ID** service principal. (Per-user OAuth connections can't be used for inference — the agent runs without a user context.)

## Configure in Datafold

1. Navigate to **Settings** → **Integrations**, click **Add New Integration**, and choose **Databricks**.
2. Select the **Data connection** whose workspace hosts your serving endpoints.
3. **Models** — the workspace's ready LLM serving endpoints are listed automatically. Select the ones to expose (e.g. `databricks-claude-sonnet-4-5`).
4. **Model API surfaces** — Datafold infers each endpoint's wire protocol (Anthropic messages for Claude endpoints, chat completions or Responses for others) from its name. Override per model for custom-named endpoints.
5. Click **Save**.