Why LLMProvider is not enough: designing an AI Gateway for SaaS in Go

The article explores the architectural challenge of scaling AI solutions in SaaS products. The author notes that using a simple LLMProvider interface becomes inefficient as system complexity grows, particularly when managing multiple models, data privacy, fallback mechanisms, cost control, and observability. Instead of scattering AI logic throughout the backend, the author proposes moving request management into a dedicated AI Gateway built with Go. This approach allows for centralized control over request routing, caching, authentication, and monitoring, significantly improving system maintainability and scalability. The article details the design of such a gateway, emphasizing the importance of decoupling business logic from the infrastructure aspects of interacting with language models. This makes the architecture more flexible and resilient to changes within the rapidly evolving AI ecosystem.