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.
Developers may need to answer questions like:
- Where do we compare available models?
- How do we track usage across projects?
- Where do we find integration docs?
- How do we keep API access organized?
- How do we 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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