Model Serving Endpoints

FIRST: Use the parent databricks-core skill for CLI basics, authentication, and profile selection.

Model Serving provides managed endpoints for serving LLMs, custom ML models, and external models as scalable REST APIs. Endpoints are identified by name (unique per workspace).

Endpoint Types

Endpoint Structure

Serving Endpoint (top-level, identified by NAME)
  ├── Config
  │     ├── Served Entities (model references + scaling config)
  │     └── Traffic Config (routing percentages across entities)
  ├── AI Gateway (rate limits, usage tracking)
  └── State (READY / NOT_READY, config_update status)

• Served Entities: Each entity references a model (from Unity Catalog or MLflow) with scaling parameters. Get the entity name from served_entities[].name in the get output — needed for build-logs and logs commands.
• Traffic Config: Routes requests across served entities by percentage (for A/B testing, canary deployments).
• State: Endpoints transition NOT_READY → READY after creation or config update. Poll via get to check state.ready.

CLI Discovery — ALWAYS Do This First

Do NOT guess command syntax. Discover available commands and their usage dynamically:

# List all serving-endpoints subcommands
databricks serving-endpoints -h

# Get detailed usage for any subcommand (flags, args, JSON fields)
databricks serving-endpoints <subcommand> -h

Run databricks serving-endpoints -h before constructing any command. Run databricks serving-endpoints <subcommand> -h to discover exact flags, positional arguments, and JSON spec fields for that subcommand.

Create an Endpoint

Do NOT list endpoints before creating.

databricks serving-endpoints create <ENDPOINT_NAME> \
  --json '{
    "served_entities": [{
      "entity_name": "<MODEL_CATALOG_PATH>",
      "entity_version": "<VERSION>",
      "min_provisioned_throughput": 0,
      "max_provisioned_throughput": 0,
      "workload_size": "Small",
      "scale_to_zero_enabled": true
    }],
    "traffic_config": {
      "routes": [{
        "served_entity_name": "<ENTITY_NAME>",
        "traffic_percentage": 100
      }]
    }
  }' --profile <PROFILE>

• Discover available Foundation Models: see references/training-and-serving.md § Foundation Model API endpoints for the runtime-list snippet and default-picking rules. You can also check the system.ai catalog in Unity Catalog, or run databricks serving-endpoints list --profile <PROFILE> to see what's deployed in the workspace. Use databricks serving-endpoints get-open-api <ENDPOINT_NAME> --profile <PROFILE> to inspect a specific endpoint's API schema.
• Long-running operation; the CLI waits for completion by default. Use --no-wait to return immediately, then poll:

  databricks serving-endpoints get <ENDPOINT_NAME> --profile <PROFILE>
  # Check: state.ready == "READY"

• For provisioned throughput or custom model endpoints, run databricks serving-endpoints create -h to discover the required JSON fields for your endpoint type.

Endpoint Readiness

After create or update-config, the endpoint provisions compute and loads the model. Do not query the endpoint until it is ready.

Poll for readiness:

databricks serving-endpoints get <ENDPOINT_NAME> --profile <PROFILE> -o json
# Ready when: state.ready == "READY" AND state.config_update == "NOT_UPDATING"

Provisioning may take several minutes. Provisioned throughput endpoints take the longest (GPU allocation). Queries to endpoints that are not yet READY return 404 or 503 errors.

Query an Endpoint

databricks serving-endpoints query <ENDPOINT_NAME> \
  --json '{"messages": [{"role": "user", "content": "Hello, how are you?"}]}' \
  --profile <PROFILE>

• Use --stream for streaming responses.
• For non-chat endpoints (embeddings, custom models): use get-open-api <ENDPOINT_NAME> first to discover the request/response schema, then construct the appropriate JSON payload.

Get Endpoint Schema (OpenAPI)

Returns the OpenAPI 3.1 JSON schema describing what each served model accepts and returns. Use this to understand an endpoint's input/output format before querying it.

databricks serving-endpoints get-open-api <ENDPOINT_NAME> --profile <PROFILE>

The schema shows paths per served model (e.g., /served-models/<model-name>/invocations) with full request/response definitions including parameter types, enums, and nullable fields.

Other Commands

Run databricks serving-endpoints <subcommand> -h for usage details.

What's Next

Integrate with a Databricks App

After creating a serving endpoint, wire it into a Databricks App.

Step 1 — Check if the serving plugin is available in the AppKit template:

databricks apps manifest --profile <PROFILE>

If the output includes a serving plugin, scaffold with:

databricks apps init --name <APP_NAME> \
  --features serving \
  --set "serving.serving-endpoint.name=<ENDPOINT_NAME>" \
  --run none --profile <PROFILE>

Step 2 — If no serving plugin, add the endpoint resource manually to an existing app's databricks.yml:

resources:
  apps:
    my_app:
      resources:
        - name: my-model-endpoint
          serving_endpoint:
            name: <ENDPOINT_NAME>
            permission: CAN_QUERY

And inject the endpoint name as an environment variable in app.yaml:

env:
  - name: SERVING_ENDPOINT
    valueFrom: serving-endpoint

Then wire the endpoint into your app via the serving() plugin or a custom route in onPluginsReady. For the full app integration pattern, use the databricks-apps skill and read the Model Serving Guide.

Develop & deploy new models

This skill is ops-focused (manage existing endpoints). For the dev-side flow — train a model, register to Unity Catalog, log a PyFunc or ResponsesAgent, deploy — see the references below.

Troubleshooting