For the complete documentation index, see llms.txt. This page is also available as Markdown.

Databricks

Instructions for using Databricks models

To use a language model deployed to Databricks Mosaic AI Model Serving, specify the model endpoint name prefixed with databricks: in the from field and include the required parameters in the params section.

Parameters

Parameter
Description

databricks_endpoint

The Databricks workspace endpoint, e.g., dbc-a12cd3e4-56f7.cloud.databricks.com.

databricks_token

The Databricks API token to authenticate with the Databricks Models API. Use the secret replacement syntax to reference a secret, e.g., ${secrets:my_databricks_token}.

databricks_client_id

The Databricks Service Principal Client ID. Can't be used with databricks_token.

databricks_client_secret

The Databricks Service Principal Client Secret. Can't be used with databricks_token.

Example spicepod.yaml configuration, using personal access token

To learn more about how to set up personal access tokens, see Databricks PAT docs.

models:
  - from: databricks:databricks-llama-4-maverick
    name: llama-4-maverick
    params:
      databricks_endpoint: dbc-46470731-42e5.cloud.databricks.com
      databricks_token: ${ secrets:SPICE_DATABRICKS_TOKEN }

Example spicepod.yaml configuration, using Databricks service principal

Spice supports the Machine-to-Machine (M2M) OAuth flow with service principal credentials by utilizing the databricks_client_id and databricks_client_secret parameters. The runtime will automatically refresh the token.

The service principal must be granted the "Can Query" permission for model serving.

To learn more about how to set up the service principal, see Databricks M2M OAuth docs.

Additional Information

Refer to the Mosaic AI Model Serving documentation for more details on available models and configurations.

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