| name | databricks-model-serving |
| description | Manage Databricks Model Serving endpoints via CLI. Use when asked to create, configure, query, or manage model serving endpoints for LLM inference, custom models, or external models. |
| compatibility | Requires databricks CLI (>= v0.294.0) |
| metadata | {"version":"0.1.0"} |
| parent | databricks-core |
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:
databricks serving-endpoints -h
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>
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 |
