Private AI
Keep serving patterns within customer-controlled or approved infrastructure.
Inference and LLM Serving
Private inference and LLM serving.
Serve AI workloads inside controlled infrastructure, with model support, endpoint behavior, and performance subject to validation.
ApplicationsAI consumers
LLM serving patternsRequests · models · access
RoutingModelsGPUPolicyTelemetry
Private inference architectureSubject to model validation
Serving patterns for controlled AI environments.
Inference and LLM Serving can be configured around approved models, access patterns, infrastructure, and operating requirements.
- Private inference architecture planning
- LLM serving pattern definition
- Model hosting considerations
- Access boundary planning
- Deployment-dependent scaling direction
- Observability and usage visibility inputs
- Application integration planning
- Validation checklist for approved models
Where Inference fits
Use this capability only where the AI workload, data boundary, operating model, and validation scope are clear.
Application Teams
Provide a controlled path from applications to approved model endpoints.
Operations
Align serving behavior with monitoring, access, and support boundaries.
Architecture
Private inference architecture
Subject to model validation
Validation path
Each AI capability should move through assessment, design, and validation before publication or commitment.
Confirm candidate models
Identify model, runtime, data, and access requirements for validation.
Shape serving architecture
Define routing, security, compute, and observability boundaries.
Test endpoint behavior
Confirm model behavior and operating assumptions before publication.
Next step
Review private inference architecture for your AI use case.
Start with your workloads, operating model, and control requirements.