How do I choose an AI gateway?

Summary

• Choosing an AI gateway comes down to one question: will AI governance plug into the same system that governs your data, or become a separate silo you maintain and audit on its own?
• Score candidates on multi-provider model access, unified access control, safety and PII guardrails, rate limiting, usage and cost tracking, observability, and portability across clouds.
• The control should hold as you move from single model calls to multi-step agents, so weigh how a gateway governs MCP servers, tools, and agents, not only which model each request hits.
• On Databricks the answer is Unity Gateway, the generally available AI control plane built on Unity Catalog, which governs models, MCP servers, functions, and agents as the same securable objects you use for tables.
• Unity Gateway's real-time guardrails and service policies are currently in Beta, so confirm their status before depending on them in production.

How do I choose an AI gateway?

Choose the AI gateway that makes AI governance part of the system you already use to govern data, not a separate proxy you bolt on and audit by itself. An AI gateway sits between your applications, agents, and the models they call, so the right one gives you a single governed place to control access, enforce safety, cap spend, and see every request across many model providers. The wrong one adds a second permission model, a second audit trail, and a blind spot the day an agent starts calling tools on its own. Judge candidates on how well they unify access, governance, guardrails, cost control, and observability, and on whether that control holds as you grow from single model calls to multi-step agents.

What is an AI gateway?

An AI gateway is a control plane between the code that requests AI and the models, tools, and agents that serve it. It routes each request, applies access and safety policy, records usage, and returns the response, so teams share one governed path to AI rather than scattering API keys and one-off integrations across projects. A strong gateway secures models, tool servers, and agents as first-class assets, the way a data platform secures tables and views.

Key capabilities to evaluate

Weigh any AI gateway on these criteria before a product name enters the conversation.

Capability	Why it matters
Provider-agnostic access	One entry point to hosted and external models avoids lock-in to a single vendor.
Governance tied to your data model	Governing models, tools, and agents with your data permissions keeps one access model and one audit trail.
Safety and PII guardrails	Real-time checks block unsafe content and catch sensitive data in prompts you didn't anticipate.
Rate limiting and traffic control	Per-user limits, fallbacks, and routing keep endpoints available and capacity predictable.
Usage and cost tracking	Spend attributed by user, team, and use case, with budgets and hard caps, ties AI cost to value.
Observability	Full request and response logging in a governed store makes debugging, auditing, and compliance possible.
MCP, tool, and agent governance	As agents call tools and other agents, you control what each may reach, not only which model it uses.

How Databricks approaches the AI gateway

On Databricks, the control plane is Unity Gateway, generally available in 2026 and built on Unity Catalog. Models, MCP servers, functions, and agents are securable objects governed with the same privileges you grant on tables and volumes, so one permission model and one audit trail cover both data and AI. See how AI governance works on Databricks.

• One entry point to any model. Unity Gateway routes requests to models hosted on Databricks and to external models from any provider, with provider API keys registered centrally in Unity Catalog so credentials aren't copied across projects. Changing a provider doesn't mean rewiring your apps.
• Access control and rate limiting. Grant who can call each model, tool, or agent with standard Unity Catalog privileges, and apply rate limits per user and per service to manage capacity and cost.
• Usage tracking and payload logging. Usage tracking records requests, token usage, and latency in system tables, and payload logging captures full requests and responses in Unity Catalog inference tables, all in governed Delta you can query.
• Cost controls. Budgets attribute spend across users, teams, and use cases and enforce alerts and hard caps that stop requests when a limit is reached.
• Guardrails and service policies (Beta). Service policies allow, deny, or require approval on individual requests and responses based on the caller and the content, with built-in guardrails for PII, unsafe content, jailbreak attempts, and hallucination checks. They run in real time and are currently in Beta, enabled through the account console previews, so confirm their status before production use.

The same governance extends to external models, external MCP servers, and coding agents routed through a Databricks model service. See Unity Gateway is now generally available.

FAQs

What matters most when choosing an AI gateway?

Whether AI governance uses the same access model and audit trail as your data. Reusing existing permissions avoids a second silo to maintain, secure, and reconcile.

Should an AI gateway support more than one model provider?

Yes. Model quality and pricing shift, so a provider-agnostic gateway with one entry point and central credentials lets you change models without re-integrating each application.

How does an AI gateway control AI cost?

Through usage tracking and budgets: spend attribution by user, team, and use case, plus alerts and hard caps that stop requests at a threshold, backed by request-level logging.

Are Unity Gateway guardrails generally available?

Unity Gateway itself is generally available, but its real-time guardrails and service policies are currently in Beta, enabled through the account console previews. Confirm the current status before relying on them in production.