Databricks Model Serving (ops) plus MLflow model development (dev): manage serving endpoints, train and register models to Unity Catalog with @prod aliases, batch-score via spark_udf, build custom PyFunc /…
---
name: databricks-model-serving
description: "Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage AI Gateway rate limits; discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation)."
compatibility: Requires databricks CLI (>= v0.294.0)
metadata:
version: "0.4.0"
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, GPT-5, Claude, Gemini, etc.) | Uses `system.ai.*` catalog models, pre-provisioned in every workspace. Discover at runtime — see [Foundation Model API endpoints](#foundation-model-api-endpoints) below. |
| 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
… install to load the full skill