LLM Gateway

An LLM Gateway is an infrastructure layer between applications and one or more LLM API providers. It gives applications a single controlled interface for model access while centralizing routing, authentication, observability, governance, and cost management.

Typical capabilities include:

• provider abstraction through one API;
• model routing and failover;
• request and response logging;
• token, latency, and cost tracking;
• prompt caching;
• rate limits and budgets;
• organization, team, or application-level attribution;
• agent guardrails; and
• policy controls for data retention or provider selection.

An LLM Gateway is different from a generic API gateway. A generic gateway treats requests as ordinary HTTP traffic. An LLM Gateway understands model names, context length, token usage, tool calls, streaming responses, provider-specific parameters, and AI-specific failure modes.

The gateway pattern is useful when an organization uses multiple providers or needs centralized controls across many products and teams. It can reduce vendor lock-in and make cost attribution more reliable. It can also introduce latency, provider feature lag, and another operational dependency.

For AI agents, an LLM Gateway can enforce model-access policy and capture usage across nested model calls. It does not replace agent tracing, because an agent trace also needs tool, retrieval, approval, and orchestration spans.

An LLM Gateway is closely related to an AI Gateway. The LLM term emphasizes model calls, while the AI Gateway term is often broader and may include agent tools, MCP traffic, and multimodal services.

AWS Prescriptive Guidance describes the LLM gateway pattern as a model access architecture for unified APIs, centralized policy enforcement, monitoring, cost management, and API normalization.