What I learned building an API gateway for multiple LLMs

I am building VectorNode AI, an OpenAI-compatible API gateway for developers who want to use GPT, Claude, Gemini, DeepSeek, Qwen, and other models through one integration layer.

The product started from a simple pain: every model provider has slightly different APIs, pricing, availability, latency, and model behavior. That is manageable in a small demo, but it becomes messy when a real product needs fallback, cost control, and model testing.

A few things I have learned so far:

1. Developers do not want another SDK unless there is a strong reason. Keeping compatibility with the OpenAI SDK lowers the first-test friction a lot.

2. The gateway has to be useful before it is fancy. Fast setup, clear model names, predictable errors, and simple pricing matter more than a big feature list.

3. Global plus Chinese model access is a real use case. Some builders need GPT or Claude for one workflow, but also need DeepSeek, Qwen, or other Chinese LLMs for Chinese-language products and regional availability.

4. Trust is the hardest part. For an API gateway, docs, examples, Postman tests, GitHub quickstarts, and clear support channels are not marketing extras. They are part of the product.

I am still improving the onboarding, quickstart docs, and model comparison pages.

Website: https://www.vectronode.com/

GitHub quickstart: https://github.com/yeallen441-del/vectorengine-quickstart

If you build AI products, what would you need to see before trying a third-party API gateway in a real side project?