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.

Don't give LLMs full control. Use deterministic code for core logic, validation, and enforcing rules. Delegate only tasks requiring flexibility or understanding of unstructured input to the LLM, treating it as a specialized component, not the entire system.

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.

Don't let LLMs make raw HTTP calls. Instead, provide a code execution tool with a statically typed SDK. This environment can run a type-checker, instantly catching errors when the model hallucinates a non-existent endpoint or parameter, then provide helpful, in-context documentation to correct its mistake.

Top inference frameworks separate the prefill stage (ingesting the prompt, often compute-bound) from the decode stage (generating tokens, often memory-bound). This disaggregation allows for specialized hardware pools and scheduling for each phase, boosting overall efficiency and throughput.

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.

Modern LLM clients expect more than just text generation. They require state management, lifecycle endpoints, and consistent API contracts, features often missing from local inference servers. An API gateway layer can bridge this gap between a simple model server and a full-featured platform.