AI Glossaryinfrastructure

API Gateway

A server that acts as a single entry point for AI API requests, handling routing, rate limiting, authentication, and load balancing across multiple AI providers.

How It Works

An API gateway sits between your application and AI providers (OpenAI, Anthropic, Google). It provides a unified interface so your app code does not need to handle provider-specific details. Gateways like LiteLLM, Portkey, and Helicone let you switch between providers, implement fallbacks (if OpenAI is down, route to Claude), and track costs across all providers in one dashboard. Key capabilities of AI API gateways: (1) Provider abstraction: same API format regardless of which model you call, (2) Fallback chains: automatic retry with a different provider on failure, (3) Cost tracking: monitor spend across all providers, (4) Rate limit management: queue requests to stay within provider limits, (5) Caching: cache identical requests to reduce costs. For production apps, a gateway becomes valuable when you use multiple AI providers or need reliability guarantees. Start without one (direct API calls are simpler), and add a gateway when you need provider fallbacks, cost monitoring, or multi-model routing.

Common Use Cases

  • 1Multi-provider AI routing
  • 2Cost tracking and budgeting
  • 3Automatic failover between providers
  • 4Rate limit management
  • 5Request caching and logging

Related Terms

Inference

The process of running a trained AI model to generate predictions or outputs from new inputs, as opposed to training the model.

Model Serving

The infrastructure and process of hosting a trained AI model and exposing it as an API endpoint for real-time or batch inference.

Latency

The time delay between sending a request to an AI model and receiving the response, critical for real-time user-facing applications.

Token Limits / Rate Limiting

Restrictions imposed by AI API providers on the number of tokens processed or requests made within a given time period.

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