What are the best AI gateway platforms?

Summary

• The best AI gateway platforms put one governed control plane in front of every model and agent: unified access to multiple model providers, access control and rate limiting, safety guardrails, usage and cost tracking, and full request/response logging.
• Databricks delivers this through Unity AI Gateway, the enterprise control plane for governing LLM endpoints, MCP servers, and coding agents, built on Unity Catalog.
• Because it is part of Unity Catalog, Unity AI Gateway governs models, MCP servers, functions, and agents as securable objects with the same privileges you already use for data, so you don't maintain a separate access model for AI.
• The generally available AI Gateway feature set for model serving adds permission and rate limiting, payload logging, usage tracking, AI guardrails (safety filtering and PII detection), fallbacks, and traffic splitting.
• Cost controls let you set budgets with per-user alerts and hard spend caps, and every request and response is logged to Unity Catalog Delta and system tables for auditing, debugging, and cost attribution.

What are the best AI gateway platforms?

An AI gateway sits between your applications and agents and the models they call, giving you a single place to govern access, enforce safety and compliance, control cost, and observe usage across many model providers. The strongest platforms treat the gateway as part of a broader governance 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 any model provider and any coding agent, all as part of Unity Catalog.

What to look for in an AI gateway platform

• 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.
• Governance and access control. Fine-grained control over who can call which model, tool, or agent, ideally using the same permission model as your data.
• Guardrails for safety and compliance. Real-time filtering of unsafe content and detection of sensitive data such as PII on both requests and responses.
• Rate limiting and traffic management. Consumption limits to manage capacity and cost, plus fallbacks and traffic splitting to keep production endpoints available.
• Usage tracking and observability. Token-level usage, latency, and full request/response logging in one governed, queryable place for monitoring, debugging, and audit.
• Cost visibility and control. Attribution of spend by user, team, and endpoint, with budgets, alerts, and enforceable caps.

Why Databricks Unity AI Gateway

Unity AI Gateway is the enterprise control plane for AI on Databricks. Because it is part of Unity Catalog, it governs your AI estate across three dimensions: 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 accessing models hosted on Databricks and external models from any provider, such as OpenAI, Anthropic, and Google, so you can build agents on the model landscape without a separate access model per provider. See Build intelligent agents on every leading model.
• Governance built on Unity Catalog. Models, MCP servers, functions, and agents are securable objects governed with the same privileges and policies you use for tables and volumes, and the same governance applies to external AI, including external models, external MCP servers, and external coding agents routed through a Databricks model service.
• Permission and rate limiting. Control who has access and how much, and Apply rate limits to model and MCP services to manage capacity and cost.
• Guardrails for safety and PII. AI guardrails enforce data compliance at the endpoint level and reduce harmful content in requests and responses. Safety filtering blocks unsafe and harmful content such as violent crime, self-harm, and hate speech, and PII detection identifies sensitive information such as credit card numbers, email addresses, phone numbers, bank account numbers, and social security numbers. Newer LLM-based guardrails (in Beta) let teams define customizable safety, PII protection, and business-specific rules evaluated in real time and applied to inputs, outputs, or both.
• Service policies for content and actions. Service policies, also called guardrails, allow, deny, or require approval for individual requests and responses based on who is calling and what the request and response contain. Defined in SQL as Unity Catalog functions, they can restrict which tools an agent may invoke and require approval before sensitive operations.
• 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 of model and agent activity for monitoring, debugging, and compliance. All data is logged to Delta tables in Unity Catalog.
• Cost controls. budgets support shared and per-user thresholds with per-user overrides; when a threshold is reached, Databricks can send an alert or block further usage. Token-level cost attribution across requests, users, and endpoints (in Beta) helps keep AI spend aligned with business budgets.
• Fallbacks and traffic splitting. configure traffic splitting and fallbacks to distribute requests across multiple model destinations and add failover, minimizing production outages during and after deployment.

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 behavior 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.
• See What's new in Unity AI Gateway for service policies, guardrails, observability, and cost controls.

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 the best AI gateway platforms have?

Unified access to multiple model providers, access control and rate limiting, safety and PII guardrails, usage tracking with full payload logging, cost visibility with budgets and caps, and traffic management such as fallbacks and traffic splitting.

How does Unity AI Gateway govern access to models?

As part of Unity Catalog, Unity AI Gateway governs models, MCP servers, functions, and agents as securable objects using the same privileges you use for data, then routes and rate-limits every request from a central control plane so you don't maintain a separate access model for AI.

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 per-user thresholds and hard caps with alerts or blocking when spend exceeds them.

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 external MCP servers and external coding agents routed through a Databricks model service.