ai-gateway from Ferro Labs centralizes access to multiple large language models, acting as a self-hosted proxy that simplifies vendor management for production AI traffic. It routes requests, balances distribution, and applies fallback logic through a unified API without major client-side changes. Key capabilities include dynamic routing, observability hooks, and an extensible plugin surface for caching and rate limiting. The target audience is developers and DevOps teams seeking vendor neutrality and tighter operational control over model traffic.

What can you actually use ai-gateway for?

The gateway functions as an OpenAI-compatible entry point that unifies requests across many providers. It supports over 30 providers and more than 2,500 models, and exposes routing features such as weighted distribution and automated fallback mechanisms. Typical uses include multi-vendor routing, directing traffic to locally hosted endpoints, and applying custom response caching via plugins. Native support for the Model Context Protocol helps integrate tools that rely on standardized context exchange.

How reliable and fast is request handling at scale?

Built in Go for concurrency, the gateway demonstrates a very small runtime footprint and high throughput in benchmarks, with a design that targets low-latency routing under heavy load. It emits Prometheus metrics and detailed request logs for production observability, and includes automated health checks plus configurable fallback policies to reduce service interruptions. These elements make it suitable for high-volume environments that require measurable telemetry and predictable behavior.

Is integration and deployment straightforward for engineering teams?

Deployment options include a standalone Go binary and a Docker container, and the gateway accepts any OpenAI-compatible endpoint including local models. Integration rarely needs client rewrites; most applications only change the base API URL to begin routing through the proxy. An extensible plugin architecture enables custom rate limiting and caching, and the project is positioned for self-hosting so teams retain control over infrastructure and data flow.

A practical choice for teams that prioritize vendor neutrality and control

ai-gateway is a practical option for engineering teams that need centralized routing across multiple LLM vendors; user feedback indicates it is highly regarded for production stability and lowering infrastructure overhead. Teams should validate fallback behavior and monitoring in their own environments before wide rollout. For organizations that require self-hosted control and predictable telemetry, the gateway is a pragmatic infrastructure component.