Model-Context Protocol (MCP) is a standardized layer that allows an LLM to communicate with various software tools without needing custom integrations for each. It acts like a universal translator, enabling the LLM to 'speak English' while the MCP handles communication with each tool's unique API.

A comprehensive AI management system requires more than just an LLM router. It needs three distinct gateways: a Model Gateway for controlling LLM access, an MCP Gateway for secure tool and data interaction, and an Agent Gateway to govern communication between different autonomous agents and provide a "kill switch."

An API gateway for local LLMs should preserve the shape and data of tool call protocols without executing the functions themselves. This maintains a critical security and architectural boundary, preventing the gateway from becoming an insecure code execution environment with access to the file system, browser, or other local resources.

Instead of interacting with a single LLM, users will increasingly call an API that represents a "system as a model." Behind the scenes, this triggers a complex orchestration of multiple specialized models, sub-agents, and tools to complete a task, while maintaining a simple user experience.

Samsara built a central endpoint that abstracts away complexities of using different LLMs like OpenAI or Gemini. This gateway handles cost, security, and compliance, allowing any product engineer to quickly build and deploy AI features without specialized expertise.

Inference backends focus on complex runtime problems like GPU scheduling and quantization. API gateways should handle different concerns like request validation and lifecycle endpoints. Separating these layers prevents duplicating API logic across runtimes and allows each component to specialize, leading to a cleaner architecture.

The term "OpenAI-compatible" is ambiguous for local backends. It can mean anything from accepting a similar request shape to partially working streaming. True compatibility with modern clients requires state, lifecycle management, and strict event semantics, a much higher bar that most simple endpoints fail to meet.

While starting with a vertically integrated system is fine, enterprises inevitably need two key components: an LLM Gateway to manage and route traffic to various models, and an MCP Gateway to securely connect those models to real-world systems.

For serious development or internal tools, logs are insufficient. An API gateway provides essential operational signals—like latency metrics, error rates by model, and readiness checks—that help diagnose failures unrelated to model quality. These gateway-specific metrics are crucial for building reliable systems on top of local LLMs.