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AI Gateway

What Is an AI Gateway?

Instead of each application handling authentication, logging, rate limiting, and content inspection on its own, requests are routed through the gateway, which enforces policy and monitors usage across every AI interaction. This becomes more valuable as organizations connect a growing number of applications and agents to multiple models and providers, at which point instrumenting each one individually stops being practical.

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Why It Matters

  • A gateway is the natural enforcement point for security controls that would otherwise need to be rebuilt inside every application that calls a model.
  • It gives security teams one place to see traffic across providers, rather than piecing together logs from each integration separately.
  • As more of an organization's AI traffic flows through agents and internal tools rather than a single chat interface, the case for a centralized checkpoint gets stronger, not weaker.

FAQ

Related, not identical. A general API gateway handles routing and auth for any API traffic. An AI gateway is purpose-built for the specific patterns of LLM traffic, prompts, streaming responses, token-based rate limits, and model-specific routing.

Less critical, but still useful for consistent logging and policy enforcement. The value compounds as the number of models, providers, and internal applications grows.

MCP Gateway is the same underlying pattern, scoped specifically to MCP traffic rather than all AI model traffic generally. See that page for the narrower, MCP-specific version.


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Related Terms


Model Context Protocol Gateway (MCP Gateway)

An MCP Gateway gives organizations centralized visibility and control over every MCP server and interaction in their environment, including the unauthorized ones.

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