How do I deploy a model with private networking and VPC access?

To deploy a model with private networking and VPC access, the usual pattern is:

1. Put the model endpoint behind private networking

   • Use a private endpoint / internal load balancer / private service endpoint instead of a public internet-facing URL.
   • Disable or restrict public ingress.

2. Connect the model service to your VPC

   • Create a VPC connector / private network attachment / peering link depending on the cloud or platform.
   • Route traffic from the model runtime to resources inside your VPC (databases, storage, internal APIs, etc.).

3. Place dependent resources in the same private network

   • Database, cache, feature store, object storage gateway, secrets manager, etc. should be reachable via private IP or private endpoints.

4. Lock down access

   • Use IAM/service accounts, security groups/firewalls, network policies, and optionally mTLS.
   • Allow only specific subnets, service accounts, or workloads.

5. Test connectivity

   • Verify the model can reach VPC resources and that external traffic cannot reach the endpoint unless explicitly allowed.

Because the exact steps depend heavily on the platform, here are the common setups:

If you’re on AWS

• Deploy the model on SageMaker with a VPC configuration
• Use private subnets
• Add security groups
• Use VPC endpoints for S3, CloudWatch, ECR, STS, etc.
• If needed, expose internally via internal Application Load Balancer

If you’re on GCP

• Use Vertex AI private endpoint or private service connect
• Attach to a VPC network
• Use private Google access / private service networking as needed

If you’re on Azure

• Use Azure ML private endpoint
• Integrate with VNet
• Use private link for storage, key vault, container registry, etc.

If you’re on Kubernetes

• Run the model as a service in a cluster connected to your VPC
• Use:
  • private ingress
  • internal load balancer
  • network policies
  • optionally service mesh for mTLS

If you want, I can give you a step-by-step deployment guide for a specific platform like AWS SageMaker, Vertex AI, Azure ML, or Kubernetes.