What Is an AI Gateway?

Definition

An AI gateway is an infrastructure layer that sits between an organization’s applications or AI agents and the AI model providers they call. It centralizes the routing, security, cost control, and governance of AI traffic — giving teams a single, consistent control point for how their applications access large language models (LLMs) and other AI services.

In the same way an API gateway manages traffic to backend services, an AI gateway manages traffic to AI models — but with capabilities specific to AI workloads, such as token-based rate limiting, multi-provider model routing, and prompt-level security controls.

How an AI gateway works

An AI gateway exposes a single endpoint — often compatible with the OpenAI API format — that applications send their AI requests to. The gateway then:

1. Routes each request to the appropriate model or provider, based on policy, cost, availability, or capability.
2. Authenticates and authorizes the request, attaching identity so usage can be governed and attributed.
3. Applies policies such as rate limits, token budgets, and content guardrails before the request reaches the provider.
4. Records usage, latency, and cost for observability and chargeback.
5. Handles failures by retrying or failing over to an alternate provider.

Because applications talk to the gateway instead of directly to each provider, teams can change models, enforce policy, and control spend centrally — without modifying application code.

Why enterprises use an AI gateway

As organizations move from AI experimentation to production, several problems emerge that an AI gateway is designed to solve:

• Cost visibility and control — attributing AI spend to the right team, project, or agent, and enforcing budgets before overruns happen.
• Governance and compliance — applying consistent security, audit, and policy controls across every AI request.
• Provider flexibility — routing across multiple model providers and failing over when one is unavailable, avoiding lock-in.
• Security — redacting sensitive data, blocking prompt injection, and ensuring only approved models are used.
• Scale and reliability — handling high request volumes with predictable performance.

AI gateway vs. related concepts

• An AI gateway governs traffic to AI models; an API gateway governs traffic to general backend APIs. The AI gateway adds AI-specific capabilities like model routing and token rate limiting.
• An LLM gateway is a closely related term, often used interchangeably, that emphasizes routing to large language models specifically.
• An inference gateway typically emphasizes routing to self-hosted or in-cluster model inference endpoints.

Frequently asked questions

Is an AI gateway the same as an API gateway? No. They share the gateway pattern — a central control point for traffic — but an AI gateway adds capabilities specific to AI, such as multi-provider model routing, token-based rate limiting, and prompt-level guardrails. See AI gateway vs. API gateway.

Do I need an AI gateway for a single application? For a single small application calling one provider, often not. AI gateways become valuable as you add teams, providers, agents, or governance requirements — anywhere you need centralized control over AI traffic.

Can an AI gateway work with any model provider? Most AI gateways expose an OpenAI-compatible API and route to multiple providers, so applications use one interface regardless of the underlying model.

Tetrate Agent Router is an enterprise AI gateway built on Envoy AI Gateway, which Tetrate co-created and maintains. Learn more about Tetrate Agent Router or browse the AI gateway glossary.