Skip to main content
Last updated on

AI governance with Unity AI Gateway

Beta

This page covers the new Unity AI Gateway (visible in the sidebar of the UI), which is currently in Beta. Account admins can enable access to this feature in the account console Previews page. See Manage Databricks previews.

For details on the previous version of AI Gateway (not Unity AI Gateway), see AI Gateway for serving endpoints.

Unity AI Gateway is the Databricks governance solution for enterprise AI. Built on Unity Catalog, it extends governance beyond your data and AI assets to the runtime interactions between models, agents, MCP servers, and tools. Control which AI services teams can use, route and manage AI traffic, set guardrails, and monitor usage from one control plane.

Get started​

Set up and apply AI governance across your AI assets, traffic, and service behavior.

Control which AI services teams can use​

Register AI assets as Unity Catalog securable objects, then grant and revoke access with the same privileges you use for tables and volumes. Agents are governed through these same securables: an agent is registered as a Unity Catalog model, and the tools it calls are governed as MCP services, functions, and connections.

    • Models
    • Govern registered ML models, including Databricks-hosted foundation models, with Unity Catalog privileges.
    • MCP tools
    • Govern MCP servers registered as Unity Catalog securables, with tool filtering and service policies.
    • Custom tools
    • Govern the Unity Catalog functions that agents use as tools, with the same privileges you use for data.
    • HTTP connections
    • Govern the Unity Catalog connections used to reach external APIs and MCP servers.
    • Create model services
    • Define and share model services as Unity Catalog securable objects across workspaces.

Route and manage AI traffic​

Unity AI Gateway routes requests to your model and MCP services from a central control plane, so you can manage capacity, availability, and spend across providers.

    • Apply rate limits
    • Enforce consumption limits on model services and MCP services to manage capacity and cost.
    • Manage budgets
    • Monitor spend and set per-user thresholds and hard caps across Databricks-hosted and external providers.
note

Unity AI Gateway features don't incur charges during Beta.

Set guardrails and access policies​

Service policies, also called guardrails, control how each request and response proceeds, based on its content and on who is making the call.

Monitor usage, cost, and risk​

Track activity, spend, and outcomes across all Unity AI Gateway services.

    • Monitor usage
    • Track requests, token usage, and latency for model services using system tables.
    • Analyze cost
    • Attribute Databricks cost to services, target models, principals, and tags.

Model serving endpoints (previous)​

The previous version of AI Gateway has governance features for model serving endpoints at the workspace level, including external model endpoints, Foundation Model API endpoints, and custom model endpoints.

On this page