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
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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