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

# Databricks

> Configure Databricks Model Serving to access foundation models

Configure Databricks Model Serving to access foundation models and custom models through Braintrust.

## Authentication

Choose between two authentication methods:

* **Personal Access Token (PAT)**: Use a Databricks personal access token for authentication
* **Service Principal OAuth**: Use OAuth with a service principal for authentication

## Configuration

| Field | Description |
| - | - |
| **API base URL** URL String | Required. Your Databricks workspace URL in the format `https://{workspace-name}.cloud.databricks.com`. [Documentation](https://docs.databricks.com/dev-tools/api/latest/index.html#workspace-instance-names) |
| **Authentication type** `pat` \| `service_principal_oauth` | Optional. Choose between personal access token or service principal OAuth. Default is `pat`. [Documentation](https://docs.databricks.com/dev-tools/api/latest/authentication.html) |
| **Secret** String | Required if using `pat` auth type. Your Databricks personal access token. [Documentation](https://docs.databricks.com/dev-tools/api/latest/authentication.html#token-management) |
| **Client ID** String | Required if using `service_principal_oauth` auth type. Client ID for service principal authentication. [Documentation](https://docs.databricks.com/dev-tools/api/latest/oauth.html) |
| **Client Secret** String | Required if using `service_principal_oauth` auth type. Client secret for service principal authentication. [Documentation](https://docs.databricks.com/dev-tools/api/latest/oauth.html) |

## Models

Databricks provides access to several foundation models through Model Serving.

### Foundation models

* Meta Llama 3.1 70B Instruct
* Meta Llama 3.1 8B Instruct
* Mistral 7B Instruct
* Mixtral 8x7B Instruct
* MPT-7B Instruct

### Custom models

Deploy your own fine-tuned models through Databricks Model Serving.

### Loop models

[Loop](https://docs.braintrust.dev/loop.md) can use GPT models served through Databricks, such as `databricks-gpt-6-1-sol`. See [Models and providers](https://docs.braintrust.dev/loop/manage.md#models-and-providers) for the supported IDs and version requirements.

## Setup requirements

1. **Databricks Workspace**: Ensure you have access to a Databricks workspace
2. **Model Serving**: Enable Model Serving in your workspace
3. **Authentication**: Set up either PAT or service principal authentication
4. **Model Endpoints**: Deploy the models you want to use as serving endpoints

## Endpoint configuration

Configure the following for model endpoints in Databricks.

1. **Serving Endpoint Name**: Use this as the model name in Braintrust
2. **Endpoint URL**: Automatically constructed from your workspace URL
3. **Authentication**: Uses the configured authentication method

## Additional resources

* [Databricks Model Serving Documentation](https://docs.databricks.com/machine-learning/model-serving/index.html)
* [Foundation Model APIs](https://docs.databricks.com/machine-learning/foundation-models/index.html)
* [Authentication Guide](https://docs.databricks.com/dev-tools/api/latest/authentication.html)
* [Model Serving Pricing](https://databricks.com/product/pricing/model-serving)


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