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LLM Gateway vs MCP Gateway: Understanding the New AI Infrastructure Stack
Modern AI systems are growing in complexity, requiring new infrastructure to manage multiple LLM providers and tool integrations. Directly connecting applications to numerous services becomes unmanageable at scale. An LLM Gateway simplifies this by offering a single entry point for all model interactions, handling authentication, rate limiting, and cost monitoring. LLM Routers are crucial because they intelligently select the most appropriate LLM for a given task, optimizing for cost and performance. The Model Context Protocol (MCP) is emerging as a standard for AI agents to interact with external tools and systems. An MCP Gateway provides a centralized layer for managing access to these MCP servers, enforcing policies and enhancing governance. While an MCP Proxy mainly handles connectivity and authentication, an MCP Gateway adds comprehensive management features. Registries, such as the MCP Registry, Agent Registry, and Skills Registry, are vital for discovering and cataloging available AI resources. The MCP Registry lists MCP servers, the Agent Registry tracks AI agents, and the Skills Registry details reusable agent capabilities. These registries prevent duplication and improve overall AI system governance. Ultimately, the effective integration and management of models, agents, and tools are paramount for future enterprise AI success, making these infrastructure layers indispensable.
