As Hugging Face workflows become more complex, especially when teams manage multiple models and inference endpoints, API access can become difficult to keep consistent.
I’ve been looking at this from an API governance perspective.
For example, a team may need to manage:
- Who can access specific endpoints
- Which users can modify API definitions
- Authentication and permissions
- API secrets and tokens
- Endpoint security requirements
- Documentation standards
- Changes to API contracts
- Audit trails for important changes
Hugging Face already provides different mechanisms for controlling access, including fine-grained tokens and organization permissions. But I’m wondering whether teams also use a separate API governance tool to manage the API lifecycle itself.
I’ve been testing Apidog for this use case, and I found its governance features interesting, particularly:
- RBAC for controlling team access
- Secret Scanner for detecting exposed credentials
- Endpoint Compliance Check for enforcing API standards
- Documentation Completeness Check
- Audit Logs for tracking changes
The combination of API development, testing and governance in one workspace seems useful when several people are working with AI/inference APIs.
I’m curious about other people’s setups:
How are you currently governing APIs around your Hugging Face models and inference endpoints?
Do you rely entirely on Hugging Face permissions, or do you also use an API management/governance tool?
And which governance feature has been the most valuable for your team: access control, secret scanning, compliance checks, documentation validation, or audit logs?