Enterprise Apps
Give applications a controlled path to approved private LLM capabilities.
Private LLM Endpoints
Private LLM endpoints under enterprise control.
Design controlled LLM access patterns where model support, endpoint behavior, and performance remain subject to validation.
Enterprise appsApproved users
Private LLM endpoint architectureAccess · routing · validation
APIAuthPolicyModelLogs
Contractual scope appliesEndpoint and access-control validation required
Endpoint architecture, not a public availability claim.
Private LLM Endpoints can be configured based on model, API, access-control, and operating validation.
- Private endpoint architecture planning
- Access-control boundary design
- Application-to-model request flow
- API gateway considerations
- Monitoring and usage visibility inputs
- Logging approach based on scope
- Model and endpoint validation planning
- Contractual operating boundary definition
Where Private Endpoints fits
Use this capability only where the AI workload, data boundary, operating model, and validation scope are clear.
Security Teams
Align endpoint access with policy, identity, and logging requirements.
AI Operations
Keep endpoint behavior visible within the agreed operations model.
Architecture
Contractual scope applies
Endpoint and access-control validation required
Validation path
Each AI capability should move through assessment, design, and validation before publication or commitment.
Map endpoint consumers
Identify applications, users, access paths, and model requirements.
Set access boundaries
Shape authentication, authorization, routing, and logging approach.
Confirm endpoint behavior
Test model, API, and operational behavior before exposure.
Next step
Design private LLM endpoints with clear operating boundaries.
Start with your workloads, operating model, and control requirements.