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

Type	When to Use	Key Detail
Pay-per-token	Foundation Model APIs (Llama, DBRX, etc.)	Uses `system.ai.*` catalog models, simplest setup
Provisioned throughput	Dedicated GPU capacity	Guaranteed throughput, higher cost
Custom model	Your own MLflow models or containers	Deploy any model with an MLflow signature

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"
    }],
    "traffic_config": {
      "routes": [{
        "served_entity_name": "<ENTITY_NAME>",
        "traffic_percentage": 100
      }]
    }
  }' --profile <PROFILE>

Discover available Foundation Models: check the system.ai catalog in Unity Catalog.
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.

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.

Task	Command	Notes
List all endpoints	`list`
Get endpoint details	`get <NAME>`	Shows state, config, served entities
Delete endpoint	`delete <NAME>`
Update served entities or traffic	`update-config <NAME> --json '...'`	Zero-downtime: old config serves until new is ready
Rate limits & usage tracking	`put-ai-gateway <NAME> --json '...'`
Update tags	`patch <NAME> --json '...'`
Build logs	`build-logs <NAME> <SERVED_MODEL>`	Get `SERVED_MODEL` from `get` output: `served_entities[].name`
Runtime logs	`logs <NAME> <SERVED_MODEL>`
Metrics (Prometheus format)	`export-metrics <NAME>`
Permissions	`get-permissions <ENDPOINT_ID>`	⚠️ Uses endpoint ID (hex string), not name. Find ID via `get`.

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 add a tRPC route to call it from your app. For the full app integration pattern, use the databricks-apps skill and read the Model Serving Guide.

Troubleshooting

Error	Solution
`cannot configure default credentials`	Use `--profile` flag or authenticate first
`PERMISSION_DENIED`	Check workspace permissions; for apps, ensure `serving_endpoint` resource declared with `CAN_QUERY`
Endpoint stuck in `NOT_READY`	Check `build-logs` for the served model (get entity name from `get` output)
`RESOURCE_DOES_NOT_EXIST`	Verify endpoint name with `list`
Query returns 404	Endpoint may still be provisioning; check `state.ready` via `get`
`RATE_LIMIT_EXCEEDED` (429)	AI Gateway rate limit; check `put-ai-gateway` config or retry after backoff

databricks-model-serving