Databricks Model Serving

Model Serving Endpoints

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

Gateway boundary: This skill owns legacy AI Gateway configuration attached to a Model Serving endpoint, such as serving-endpoints put-ai-gateway. An explicit Unity Gateway / Unity AI Gateway request or any databricks ai-gateway model service, MCP service, or model provider service belongs to databricks-unity-gateway.

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

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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:

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# 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.

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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 Foundation Model API endpoints below 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:

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  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.

MLflow Deployments client (Python alternative)

mlflow.deployments.get_deploy_client("databricks").create_endpoint(name=..., config={...}) takes the same JSON shape as the CLI. Two gotchas:

• tags= is a top-level kwarg, NOT a field inside config. Same [{key, value}] shape as serving-endpoints patch --add-tags.
• traffic_config.routes[].served_model_name = "<model>-<version>" (e.g. "turbine_failure-3"). The API auto-derives this from the entity, but you reference the exact string in traffic_config — get the format wrong and the route silently doesn't match.

Zero-downtime version swap

To roll an endpoint to a new model version: repoint the alias and call update_endpoint with the new served_entities + matching traffic_config. Missing either half is the common bug — alias-only doesn't update the endpoint; update_endpoint-only leaves the alias pointing at the old version.

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from mlflow.tracking import MlflowClient
from mlflow.deployments import get_deploy_client

registry = MlflowClient(registry_uri="databricks-uc")
deploy   = get_deploy_client("databricks")

registry.set_registered_model_alias(FULL_NAME, "prod", new_version)
deploy.update_endpoint(endpoint=ENDPOINT_NAME, config={
    "served_entities": [{"entity_name": FULL_NAME, "entity_version": new_version,
                         "workload_size": "Small", "scale_to_zero_enabled": True}],
    "traffic_config": {"routes": [
        {"served_model_name": f"{NAME}-{new_version}", "traffic_percentage": 100}
    ]},
})

The CLI equivalent is databricks serving-endpoints update-config <NAME> --json '...'. Either way, poll both state.ready and state.config_update afterward — see Endpoint Readiness below.

Endpoint Readiness

After create or update-config, the endpoint provisions compute and loads the model. Do not query the endpoint until it is ready. Two state fields matter and they mean different things:

• state.ready — READY once the endpoint has any working config. Stays READY during a version swap.
• state.config_update — NOT_UPDATING once the current config update finishes; IN_PROGRESS during a version swap.

A loop watching only state.ready will say "ready" mid version-swap while the old version is still serving. Poll both:

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databricks serving-endpoints get <ENDPOINT_NAME> --profile <PROFILE> \
  | jq '{ready: .state.ready, config_update: .state.config_update}'
# Fully ready when ready == "READY" AND 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.

Query an Endpoint

Chat / agent endpoints use the messages array:

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

Classical-ML endpoints use dataframe_records (one record per row):

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databricks serving-endpoints query <ENDPOINT_NAME> \
  --json '{"dataframe_records": [{"vibration": 0.42, "rpm": 18.3, "temp_c": 71.2}]}'

• Use --stream for streaming responses on chat endpoints.
• For embeddings or other custom schemas: use get-open-api <ENDPOINT_NAME> first to discover the request/response shape.

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.

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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:

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databricks apps manifest --profile <PROFILE>

If the output includes a serving plugin, scaffold with:

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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:

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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:

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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 — training, MLflow tracking, UC registration, custom PyFunc authoring, and hand-rolled ResponsesAgent code — see databricks-ml-training (experimental).

Foundation Model API endpoints

Pay-per-token, pre-provisioned in every workspace. New models land regularly and a static skill list goes stale fast — always list at runtime instead of hard-coding names. Filter by the databricks- name prefix AND by the served entity being in system.ai.* (other endpoints like databricks-app-template-serving share the prefix but aren't FM API endpoints). Use Databricks Unity Gateway (databricks-unity-gateway) to manage access to foundation model endpoints. Do not use databricks-model-serving legacy AI Gateway.

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# FM API endpoints in this workspace, grouped by task (chat / embeddings / etc.)
databricks serving-endpoints list \
  | jq -r '.[]
      | select(.name | startswith("databricks-"))
      | select((.config.served_entities[0].entity_name // "") | startswith("system.ai."))
      | "\(.task)\t\(.name)"' \
  | sort

Defaults when the user doesn't specify: pick the highest-numbered Claude Sonnet for agents, the highest-numbered -codex-max for code, databricks-gte-large-en for embeddings — resolve actual names from the live list above.

Off-platform streaming

For apps deployed outside Databricks Apps (Vercel, AWS, standalone Node.js) hitting Databricks legacy AI Gateway with Vercel AI SDK v6, see references/off-platform-streaming.md. Off-platform apps should be updated to hit Databricks Unity Gateway (databricks-unity-gateway). For AppKit-based apps, use the databricks-apps skill's built-in serving plugin instead.

Troubleshooting