What should I use for model serving if I need private networking?

If you need private networking for model serving, the best choice depends on where you’re running, but in general you want a serving option that can be deployed inside your own VPC/VNet/Kubernetes cluster rather than exposed publicly.

Good options

• Kubernetes-based serving:
  Use KServe, Seldon, Ray Serve, or BentoML on a private Kubernetes cluster.

  • Best when you need full control over networking
  • Can stay entirely inside private subnets
  • Easy to integrate with internal load balancers and private ingress

• Managed cloud serving with private endpoints:

  • AWS SageMaker with VPC-only access / private subnets
  • Azure ML with private link / managed VNet
  • Google Vertex AI with private service access / VPC-SC depending on setup
  • Best if you want managed infrastructure but still private access

• Self-hosted inference servers:

  • NVIDIA Triton Inference Server
  • vLLM or TGI behind a private internal load balancer
  • Good for high-performance LLM serving with network isolation

Rule of thumb

• If you want maximum privacy and control: Kubernetes + internal load balancer
• If you want managed and private: use your cloud’s native ML serving with private networking features
• If you need LLM serving specifically: vLLM or TGI in a private cluster is common

If you tell me your cloud (AWS/Azure/GCP/on-prem) and whether this is for an LLM or a general ML model, I can recommend the most suitable setup.