Compare Unity Gateway to other AI governance tools.

Summary

• AI governance tools should govern every AI asset (models, agents, tools, and MCP servers), control the traffic to them (rate limits, budgets, usage logging), enforce behavior with guardrails and policies, and keep a full audit trail and lineage.
• The real differences between tools are how many of these layers they cover natively, whether they govern AI with the same system that governs your data, and whether enforcement happens at runtime on both the request and the response.
• Databricks delivers AI governance with Unity Gateway and Unity Catalog together: models, agents, tools, and MCP servers are registered as governed Unity Catalog securables, and every model and tool request routes through one control plane.
• Unity Gateway is generally available; its service policies and guardrails are in Beta, so evaluate those capabilities against your rollout timeline.
• Governance decisions and model and tool calls are recorded in Unity Catalog system tables (system.access.audit) with unified tracing and lineage, so compliance teams get one queryable record.

Compare Unity Gateway to other AI governance tools.

AI governance tools converge on a common job: control who can use which models, agents, and tools, enforce what those assets are allowed to do, cap spend, and record every interaction for audit. Where they differ is coverage and integration: whether one tool governs all AI assets or only model endpoints, whether it governs AI with the same system that governs your data, and whether policies are enforced at runtime on both the request and the response. Databricks approaches this with Unity Gateway and Unity Catalog together, so AI assets are governed as first-class objects alongside your data and all model and tool traffic flows through one control plane.

What to evaluate in an AI governance tool

Capability	Why it matters
Breadth of governed assets	Governing models, agents, tools, and MCP servers as managed objects, not just model endpoints, closes gaps as agentic systems grow.
Shared governance with data	Reusing the same permission model, lineage, and audit as your data avoids a separate silo to certify and reconcile.
Traffic control	Routing every request through one control plane enables rate limits, budgets, provider fallback, and usage tracking across hosted and external models.
Runtime policy enforcement	Allow, deny, or require approval on both the request and the response, so unsafe inputs and outputs are caught in the moment.
Built-in guardrails	Detection for sensitive data, prompt injection, unsafe content, and hallucination reduces the need to assemble separate safety services.
Cost controls	Spend attribution and hard budget caps that stop requests, not just alert after the fact, keep AI spend accountable.
Audit and lineage	A complete, queryable record of every governance decision and every model and tool call, with lineage across assets and data.

How Databricks approaches AI governance with Unity Gateway

Databricks governs AI across three dimensions.

• Asset governance. Models, agents, tools, and MCP servers are registered as securables in Unity Catalog (catalog, schema, and service), with standard privileges to use, to register, and to administer them. The same role-based and attribute-based access controls that govern data extend to every AI asset, with unified discovery, lineage, and audit.
• Traffic governance. Unity Gateway routes every model and tool request through one control plane. It manages routing across Databricks-hosted and external models with traffic splitting and fallback, enforces rate limits on requests and tokens per caller, attributes spend by user, team, tool, and use case, and applies hard budget caps that stop requests when a limit is reached. Requests, token usage, and latency are logged to Unity Catalog system tables, and payload logging can capture full request and response detail.
• Behavior governance. Service policies (Beta) evaluate AI traffic on the request (on call) and on the response (on result) and return allow, deny, or require-approval decisions, invoking built-in guardrails, a custom LLM-as-judge, or custom SQL functions. Guardrails (Beta) detect sensitive data with block, mask, or ask handling, prompt injection and jailbreak attempts on the request, and unsafe content and hallucination on the response. Policies also control which tools an agent may invoke based on identity and request context.

For audit and observability, the system.access.audit table records governance decisions as ground truth, unified tracing captures model and tool activity with agent identity (a user via on-behalf-of, or a service principal), and lineage sits alongside data lineage in Unity Catalog. Together this gives regulated teams role-based access control, audit logging across the AI lifecycle, and monitoring in one governed layer.
Lifecycle: Unity Gateway is generally available; service policies, guardrails, and native agent support are in Beta, with agents governed through the prior gateway in the meantime. Read how AI governance works on Databricks, the Unity Gateway general availability announcement, what's new with service policies, guardrails, observability, and cost controls, and the Unity Catalog product page.

FAQs

What does an AI governance tool actually govern?

The AI assets your organization runs, models, agents, tools, and MCP servers, plus the traffic to them. Strong tools control who can use each asset, enforce what it can do at runtime, cap spend, and log every call for audit. Databricks governs all of these with Unity Gateway and Unity Catalog.

Can Unity Gateway govern models from multiple providers?

Yes. It routes requests across Databricks-hosted and external models through one control plane, applying traffic splitting, fallback, rate limits, budgets, and usage logging consistently regardless of the underlying provider.

Are Unity Gateway's guardrails and policies generally available?

Unity Gateway itself is generally available. Its service policies and guardrails are in Beta, and native agent support is coming, so plan an evaluation if your rollout depends on those specific capabilities.

How does Databricks support compliance and audit for AI?

Every governance decision and model or tool call is recorded in Unity Catalog system tables, including system.access.audit, with unified tracing and end-to-end lineage across assets and data. Compliance teams get one queryable record instead of logs scattered across tools.

How is governing AI different from governing data?

The assets and risks differ, such as prompts, model outputs, tool calls, and agent identity, but the control model can be shared. Databricks registers AI assets as Unity Catalog securables and reuses the same access control, lineage, and audit foundation used for data.