AI Gateway vs MCP Gateway: Key Differences Explained
Blog post from NeuralTrust
AI gateways and MCP gateways govern different stages of AI-agent workflows and are generally complementary rather than interchangeable. An AI gateway acts as a reverse proxy for LLM requests, providing multi-provider routing, fallback, token-based rate limits, cost tracking, caching, and prompt and response security controls such as PII masking or jailbreak detection. An MCP gateway sits between agents and Model Context Protocol servers, controlling the subsequent tool-use traffic through authentication, per-agent and per-tool authorization, server discovery, audit logs, and inspection of tool calls and results for unsafe activity or data exposure. MCP, introduced by Anthropic in 2024 and now governed through the Linux Foundation’s Agentic AI Foundation, standardizes connections between AI applications and external tools, reducing custom integration work but creating governance needs that the protocol does not inherently address. Organizations whose systems only make model calls may need only an AI gateway, while production agents that access databases, workflows, or other MCP-enabled tools typically need both layers, potentially through a shared control plane, alongside runtime monitoring of agent behavior.
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