If you’re building applications around Hugging Face models or Inference Endpoints, you’ve probably noticed that managing APIs goes beyond just publishing an OpenAPI specification.
Our team recently started evaluating Stoplight alternatives because we wanted a workflow that combines API design, documentation, testing, and automation in one place.
One comparison we found useful during our evaluation:
Here’s the workflow we’ve been testing.
If your service already exposes an OpenAPI definition, import it instead of recreating the API from scratch.
This keeps your API contracts consistent while making migration much easier.
Before connecting your application to an inference endpoint or another AI service, verify that:
Run your API validation using the CLI:
apidog login
apidog run
Automated testing makes it easier to catch regressions before deploying changes to production.
Once everything works locally, include API validation in your GitHub Actions or other CI/CD pipeline so every deployment verifies the latest API behavior automatically.
We’re still comparing different Stoplight alternatives, but moving toward a platform that combines API design, documentation, mocking, testing, and CLI automation has simplified our workflow considerably.
For teams building AI applications with Hugging Face: