What should I look for when choosing an enterprise AI gateway?

Summary

• An enterprise AI gateway is a single governed control plane between your applications and agents and the models they call, so you can govern access, enforce safety, control cost, and observe usage across many providers.
• Look for unified, provider-agnostic model access, fine-grained access control, safety and PII guardrails, rate limiting and traffic management, granular usage and cost tracking, and full payload logging.
• Databricks delivers this through Unity AI Gateway, the enterprise control plane for AI built on Unity Catalog.
• Because it is part of Unity Catalog, models, MCP servers, functions, and agents are governed as securable objects with the same privileges you already use for data, so you don't maintain a separate access model for AI.
• The gateway adds rate limiting, safety and PII guardrails, service policies, usage tracking, payload logging to Unity Catalog inference tables, budgets with alerts and caps, and traffic splitting with fallbacks.

What should I look for when choosing an enterprise AI gateway?

An AI gateway sits between your applications and agents and the models they call, giving you one place to govern access, enforce safety and compliance, control cost, and observe usage across many model providers. The strongest gateways treat governance as part of a broader system rather than a standalone proxy, so the same policies that protect your data also govern your models, tools, and agents. On Databricks, that control plane is Unity AI Gateway: it routes every model and MCP request, enforces rate limits and cost controls, applies guardrails, and records usage across model providers and coding agents, all as part of Unity Catalog.

What to look for in an enterprise AI gateway

• Unified, provider-agnostic access. One entry point to models hosted on the platform and to external models from any provider, so teams aren't locked into a single model or vendor.
• Access control. Fine-grained, consistent permission enforcement over who can call which model, tool, or agent, ideally using the same permission model as your data.
• Guardrails for safety and compliance. Runtime filtering of unsafe content and detection of sensitive data such as PII on both requests and responses.
• Rate limiting and traffic management. Per-user, per-group, and per-endpoint limits to manage capacity and cost, plus fallbacks and traffic splitting to keep production endpoints available.
• Usage tracking and cost attribution. Token-level usage and granular attribution of spend by user, team, and endpoint, with budgets, alerts, and enforceable caps.
• Payload logging and observability. Full request and response logging in one governed, queryable place for monitoring, debugging, and audit.

Why Databricks Unity AI Gateway

Unity AI Gateway is the enterprise control plane for AI on Databricks. Because it is part of Unity Catalog, the same governance that protects your data extends to your entire AI estate: Unity Catalog manages every model, MCP server, function, and connection as a securable object; Unity AI Gateway routes and governs the traffic to those assets; and service policies govern the content of each request and response. See How AI governance works on Databricks.

• Unified access to any model. Unity AI Gateway is a single control plane for models hosted on Databricks through Foundation Model APIs and for external models from any provider, so you can build on the model landscape without a separate access model per provider. See Build intelligent agents on every leading model.
• Access control via Unity Catalog. Models, MCP servers, functions, and agents are registered as Unity Catalog securables and governed with standard privileges—EXECUTE to use a model, CREATE SERVICE to register one, MANAGE to administer—so the same governance you use for tables and volumes applies to the whole AI stack.
• Rate limiting and traffic management. Rate limits are enforced per principal (user, group, or service principal) with query-per-minute and token-per-minute caps, and you can configure traffic splitting and fallbacks to distribute requests and add failover, minimizing production outages.
• Guardrails for safety and PII. AI guardrails block unsafe and harmful content and detect sensitive information such as PII on requests and responses, and can be evaluated in real time on inputs, outputs, or both.
• Service policies for content and actions. Service policies allow, deny, or require approval for individual requests and responses based on who is calling and what the request and response contain, and can be run in log mode first for dry-run testing before enforcement.
• Usage tracking and payload logging. usage tracking monitors requests, token usage, and latency through system tables, and payload logging captures full request and response data in Unity Catalog inference tables, creating a centralized, queryable record for monitoring, debugging, and compliance.
• Cost controls. budgets support shared and per-user thresholds; when a threshold is reached, Databricks can send an alert or block further usage, and spend can be attributed granularly by user, team, and endpoint.

Getting started

• Start with AI governance with Unity AI Gateway to set up the control plane for your AI estate.
• Read How AI governance works on Databricks to understand asset, traffic, and content governance.
• Apply rate limits and configure traffic splitting and fallbacks on your model services.
• Enable usage tracking and payload logging, then set budgets with alerts and caps.

FAQs

What is an AI gateway?

An AI gateway is a centralized control plane between your applications or agents and the models they call. It brings governance, safety, monitoring, and cost control to model traffic so organizations can adopt AI securely at scale.

What capabilities should an enterprise AI gateway have?

Unified access to multiple model providers, fine-grained access control, safety and PII guardrails, rate limiting and traffic management, usage tracking with full payload logging, and cost visibility with budgets and caps.

How does Unity AI Gateway govern access to models?

As part of Unity Catalog, it governs models, MCP servers, functions, and agents as securable objects using standard privileges such as EXECUTE, CREATE SERVICE, and MANAGE, then routes and rate-limits every request from a central control plane.

How does Databricks track AI usage and cost?

Usage tracking monitors requests, token usage, and latency through system tables, payload logging captures full requests and responses in Unity Catalog inference tables, and budgets let you set thresholds and caps with alerts or blocking when spend is exceeded.

Does the AI gateway work with external models and providers?

Yes. Unity AI Gateway provides one governed entry point to models hosted on Databricks and to external models from any provider, and the same governance extends to MCP servers and coding agents routed through a Databricks model service.