AI Gateway: Definition and Examples

An AI Gateway is an infrastructure component that sits between your applications and the various AI model providers (OpenAI, Anthropic, Google, etc.). It works as an intelligent proxy that intercepts all requests intended for LLM APIs to apply cross-cutting features: authentication, rate limiting, caching, logging, routing, and observability.

The main benefit of an AI Gateway lies in centralizing the management of AI calls. Rather than integrating each provider's SDK directly into your code, you go through a unified interface that abstracts the differences between APIs. This allows easy switching from one model to another, setting up automatic fallback strategies in case of failure, and finely controlling costs through real-time token consumption tracking.

Beyond simple routing, modern AI Gateways offer advanced features like semantic caching (which avoids re-calling the API for similar requests), load balancing between multiple API keys or providers, detection of sensitive content (PII, confidential data) before sending, and detailed analytics dashboards to monitor latency, error rates, and costs per team or per project.

Solutions like Portkey, LiteLLM, Helicone, or Cloudflare AI Gateway illustrate this category of tools. They have become essential in enterprise architectures where multiple teams consume AI models, as they bring governance, security, and budget control at scale.