Building a Centralized AI Tool Aggregator: Architecture, API Normalization, and Latency Tradeoffs

The article discusses the engineering challenges of building a web-based platform that aggregates multiple AI tools into a single interface, focusing on architecture, API normalization, and latency management.

Details

The author built a small web-based platform called 'Only AI Tools' that aggregates multiple AI tools into a single interface. The goal was to explore the engineering challenges of unifying these services under one system. The architecture is designed around a modular approach, where each AI tool is treated as an independent service, and a central routing layer handles requests and dispatches them to the appropriate external API. This abstraction allows the frontend to remain consistent while the underlying AI providers can be swapped or extended. One of the biggest challenges was dealing with inconsistent API outputs, which the author addressed by implementing a normalization layer that transforms all responses into a unified internal schema before they reach the UI layer. This simplified the frontend logic but added complexity in the backend. Latency also became a significant constraint when combining multiple AI services, as some APIs respond in 200-500ms while others take several seconds. The author had to introduce caching and partial rendering to mitigate these delays. The main takeaway is that the hardest part is not integrating AI tools individually, but designing a system that abstracts them cleanly without introducing too much overhead. The article also raises the question of whether this centralized architecture is scalable, or if future systems will shift toward agent-based models that dynamically select tools without explicit user-level routing.