# On-Premises API Testing for Private AI Infrastructure: What Are You Using?

> Source: <https://discuss.huggingface.co/t/on-premises-api-testing-for-private-ai-infrastructure-what-are-you-using/179173#post_1>
> Published: 2026-08-24 04:18:51+00:00

As AI applications move from experimentation to production, API infrastructure becomes another part of the stack that teams need to secure.

For teams running models, inference services, or internal AI APIs on private infrastructure, I’m wondering how others handle API testing.

A traditional cloud-based API testing workflow isn’t always ideal when APIs involve:

This is why I’ve been looking at **on-premises API testing platforms like Postman**.

One option I’ve been testing is **Apidog**, particularly its on-premises/self-hosted deployment.

What I find interesting is that it isn’t limited to sending requests. The same environment can cover:

For a private AI stack, I could imagine a workflow like:

**Model/Inference service → API → Apidog → automated tests → CI/CD → production**

The infrastructure question is particularly interesting to me.

If an inference API is running inside a private environment, should the API testing and development tooling live there as well?

Or is a cloud-based API testing workflow still practical for your use case?

For those running private AI infrastructure, what are you currently using to test internal APIs?

**Postman, a self-hosted tool, custom Python scripts, CI/CD tests, or something else?**