A single successful AI API request is not enough for production.
If your app uses GPT, Claude, Gemini, DeepSeek, Qwen, or other models through one OpenAI-compatible API gateway, I think the integration should be tested as a system: configuration, SDK compatibility, model names, JSON output, latency, retries, fallback, and Postman verification.
I published the full checklist here:
https://github.com/yeallen441-del/vectorengine-quickstart/blob/main/AI_API_TESTING_CHECKLIST.md
What I test before shipping
1. Base URL and API key
Most migration issues come from the wrong base URL, wrong API key, or unavailable model name. I test one small request with curl or Postman before touching production code.
2. SDK compatibility
For an OpenAI-compatible gateway, the goal is to keep the same OpenAI SDK request shape and only change the API key, base URL, and model name.
Example base URL:
https://www.vectronode.com/v1
3. Structured output
Many production workflows need valid JSON. I test whether the response parses, whether required fields exist, and how the app handles bad output.
4. Latency and fallback
A useful integration log should include model name, feature name, request duration, retry count, token usage, and error status.
These fields make it easier to decide when to use a premium model and when to route to a lower-cost fallback.
VectorNode AI is the OpenAI-compatible API gateway I am building around this workflow: