API Gateway (AI Context)

Why It Matters

API gateways solve the complexity of managing AI services at scale. Without one, every client needs to know which model server to hit, handle retries themselves, manage API keys, and deal with version changes. That’s unsustainable as your system grows.

For AI engineers, gateways enable sophisticated deployment patterns. A/B testing different models? Route a percentage of traffic to each. Rolling out a new model version? Gradual traffic shift with automatic rollback. Managing costs across multiple LLM providers? Intelligent routing based on request type, cost, and latency requirements.

Gateways also provide crucial observability. Every request flows through one point, making it easy to track usage patterns, identify bottlenecks, detect anomalies, and attribute costs to specific users or features.

Implementation Basics

An AI-focused API gateway typically handles:

1. Authentication & Authorization API key validation, JWT verification, usage quotas. For multi-tenant systems, ensure requests only access authorized models and data.

2. Traffic Management Rate limiting to prevent abuse and protect backend resources. Load balancing across model replicas. Circuit breakers to handle failing backends gracefully.

3. Request Transformation Normalize requests from different clients into the format your model expects. Add context, inject system prompts, or route to different model versions based on request parameters.

4. Response Handling Support streaming responses for LLMs. Cache common responses to reduce costs and latency. Transform outputs into client-expected formats.

Popular options: AWS API Gateway, Kong, Envoy, or custom FastAPI middleware. For LLM-specific needs, consider tools like LiteLLM that handle provider-agnostic routing.

Start simple. Basic authentication and rate limiting cover most early needs. Add sophisticated routing as your deployment patterns mature.