I'm building a company-wide AI layer and need to restrict model access by team; what should I use?

Use organization/team-level access control with an allowlist of models per team.

In practice, the right pattern is:

1. Create teams/groups that mirror your company structure

   • e.g. Marketing, Support, Engineering, Finance

2. Assign model permissions to each team

   • Example:
     • Marketing → GPT-4.1, image models
     • Support → GPT-4.1-mini, tool-enabled models
     • Engineering → coding models, higher-context models
     • Finance → only approved text models

3. Enforce access at the gateway / middleware layer

   • Don’t rely on client-side UI restrictions.
   • Put model selection behind your API or orchestration layer and validate:
     • user identity
     • team membership
     • allowed model list
     • workspace/tenant policies

4. Optionally add policy rules

   • region restrictions
   • data sensitivity rules
   • budget limits
   • prompt/output logging permissions

If you’re asking what to “use” technically, the common answer is:

• RBAC (role-based access control) for team-based permissions
• or ABAC (attribute-based access control) if you need more nuanced rules like department + region + data class + request type

If you want, I can sketch a simple architecture for model routing and team-based authorization or give you a policy schema example.