When building a small AI demo, working with APIs usually feels simple.
You choose a model, send a request, test the response, and move on.
But once the project grows, the workflow becomes more complicated. The hard part is often not the model itself, but everything around it:
- Where do you compare available models?
- How do you track usage across projects?
- Where do developers find integration docs?
- How do teams keep API access organized?
- How do you avoid switching between too many dashboards?
This is the operational layer of AI development, and it is easy to underestimate.
A better AI API workflow usually needs:
- A clear place to explore models
- A simple way to manage API access
- Usage visibility
- Easy-to-find integration documentation
- A dashboard that connects these pieces together
This is the reason I started working on ChinaRouter.
ChinaRouter is a unified AI API gateway and admin dashboard. It focuses on model marketplace access, API gateway workflow, usage lookup, dashboard management, and integration documentation.
Website: https://chinarouter.net/
For developers building AI products: what part of API management becomes painful first?
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