AI Gateway

Simple Definition

An AI gateway is a control layer that sits between your application and the AI providers you use (OpenAI, Anthropic, Google, etc.). It manages the flow of requests — deciding which model to send them to, how to handle failures, how much to spend, and how to log everything.

Think of it as a traffic controller for AI API calls.

Why AI Gateways Exist

Once an organization uses AI at scale — multiple teams, multiple use cases, multiple providers — managing everything directly through individual API integrations becomes chaotic:

Different apps use different models inconsistently
No single place to monitor costs
If one provider goes down, everything using it breaks
Logging and compliance are scattered across codebases

An AI gateway centralizes all of this into one managed layer.

What an AI Gateway Does

Feature	What it means
Routing	Send requests to GPT-4o, Claude, or Gemini based on rules or availability
Fallbacks	If OpenAI is down, automatically try Anthropic instead
Rate limiting	Prevent any one team or user from burning through the entire budget
Cost tracking	See exactly how much each request costs and who’s spending what
Caching	Return saved responses for repeated identical queries
Logging	Record all inputs and outputs for auditing and debugging
Load balancing	Distribute requests across providers for performance

Who Uses AI Gateways

Companies building AI-powered products that need reliability and cost control
Enterprises deploying AI across multiple teams and departments
Developers managing several AI API keys and models in one place
Organizations with compliance requirements who need to log AI interactions

Popular AI Gateways

Portkey — feature-rich gateway with routing, caching, and observability
LiteLLM — open-source, supports 100+ models
Kong AI Gateway — enterprise-grade, built on the Kong API platform
CloudFlare AI Gateway — simple setup, usage analytics, caching

API — the connections an AI gateway manages
Endpoint — the specific API addresses requests are routed to
LLM — the models an AI gateway connects to
AI Integration — a gateway simplifies complex AI integrations
Orchestration — gateways and orchestration often work together in AI architectures