databricks-model-serving
SkillDatabricks 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 ra
Install
git clone https://github.com/databricks/databricks-agent-skills.git ~/.claude/skills/databricks-model-servingWhat is databricks-model-serving?
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).
What this can do
Capabilities declared in this component's own frontmatter — not inferred.
Inherit all session tools
Declares no tool restrictions — inherits every session tool
~179 tokens of context used while enabled, before you invoke anything
Documentation
README · ~7 min readModel 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 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
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