Databricks Model Serving and Dome

1. 01

   Add the connection

   The provider id is databricks. Databricks endpoints are per workspace, so set --endpoint to your workspace URL plus /serving-endpoints; the model is the serving endpoint's name.

   $ dome models add opus-databricks \
     --provider databricks \
     --endpoint https://<workspace>.cloud.databricks.com/serving-endpoints \
     --model databricks-claude-opus-5-5 \
     --api-key "$DATABRICKS_TOKEN" \
     --attributes '{"env":"prod"}' \
     --gateway prod-gateway

2. 02

   Pool it with Anthropic

   Add an Anthropic connection for claude-opus-5-5, tagged env: prod too, and pool the two. Agents send the pool's name.

   $ dome models pool create claude-opus-5-5 \
     --failover-max all --gateway prod-gateway
    
   $ dome models pool member add claude-opus-5-5 opus-databricks --priority 0
   $ dome models pool member add claude-opus-5-5 opus-anthropic --priority 1

3. 03

   Point your agent at the Gateway

   Point the OpenAI SDK at the Gateway with the agent's Dome key, and call the pool by name.

   from openai import OpenAI
    
   client = OpenAI(base_url=f"{GATEWAY_URL}/v1", api_key=DOME_AGENT_KEY)
   client.chat.completions.create(
       model="claude-opus-5-5",
       messages=[{"role": "user", "content": "Explain why this pipeline run failed."}],
   )

Commands and rules tested against a Dome workspace on October 1, 2026. For anything about Databricks itself, see Databricks's documentation.

forbid (principal, action == Dome::Action::"llm:invoke", resource is Dome::LLMModel)
unless { resource has env && resource.env == "prod" };