Every team that integrates more than one AI model provider eventually builds the same thing: a proxy layer that handles authentication, logging, retries, and cost tracking. The AI gateway pattern formalizes this into a dedicated infrastructure component that sits between your application code and all external model APIs.

Why a Gateway

Without a gateway, each service that calls an LLM implements its own retry logic, its own API key management, its own usage tracking. This leads to inconsistent behavior across services, duplicated code, and blind spots in cost monitoring. When a provider changes their API or raises prices, every service needs updating independently.

A gateway centralizes these cross-cutting concerns. Application code sends a generic inference request to the gateway. The gateway handles provider-specific API translation, authentication, and all operational concerns.

Core Capabilities

Unified API - The gateway exposes a single API contract regardless of backend provider. Application code does not need to know whether it is calling OpenAI, Anthropic, or a self-hosted model. Switching providers becomes a configuration change at the gateway layer, not a code change in every consuming service.

Request routing - The gateway routes requests to different models based on configurable rules. Simple routing uses static mappings. Advanced routing considers request characteristics: send short classification tasks to a smaller model, send complex reasoning tasks to a larger one. Cost-aware routing picks the cheapest provider that meets latency requirements.

Rate limiting and quotas - Per-team, per-application, or per-user rate limits prevent any single consumer from exhausting shared API quotas. The gateway enforces these centrally rather than trusting each service to self-limit.

Response caching - Identical prompts hitting the same model return cached responses. Semantic caching extends this to prompts that are similar but not identical. Caching at the gateway layer benefits all consuming services automatically.

Observability - Every request and response flows through a single point, making it straightforward to log token usage, latency, error rates, and costs. Dashboards built on gateway telemetry give a complete picture of AI spend and performance across the organization.

Content filtering - The gateway can inspect requests and responses for policy violations, PII, or prompt injection attempts before they reach the model or return to the user.

Implementation Approaches

Self-hosted gateways like LiteLLM Proxy, Portkey, or a custom Envoy/NGINX plugin give full control. Managed services like AWS Bedrock or Azure AI Gateway reduce operational burden but limit customization.

The key architectural decision is whether the gateway is a shared service or a sidecar. A shared service is simpler to operate but introduces a single point of failure. A sidecar pattern deploys the gateway logic alongside each service, trading operational simplicity for resilience.

When to Introduce a Gateway

A gateway adds value once you have more than one model provider or more than one team consuming AI APIs. For a single team using a single provider, the overhead of operating a gateway may not justify the benefits. The tipping point usually comes when the monthly AI bill becomes large enough that someone asks where the money is going and nobody can answer precisely.