What Is an LLM Gateway, and Where It Fits in the Stack

An LLM gateway is presented as infrastructure between applications and model providers that provides a unified API for multi-model routing, token-based cost attribution and limits, semantic caching, observability, security controls, and failover, addressing risks such as uncontrolled AI spending illustrated by Uber’s reported 2026 budget overrun. Unlike conventional API gateways, which manage fixed-cost web-service traffic, LLM gateways are designed for streaming responses, token pricing, prompt-related security threats, and meaning-based caching; they can complement rather than replace API gateways. The text distinguishes an LLM gateway’s model-side role from a broader AI gateway, which may also govern agent tool calls and data access, and argues that routing simpler requests to cheaper models can substantially reduce costs while preserving performance. It describes a complete enterprise AI stack as application, model, and data layers, emphasizing that model routing alone does not control access to enterprise information. The post positions CData Connect AI as a complementary MCP-compliant data-access platform that provides governed, real-time, permission-filtered, and auditable connections to enterprise systems.

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