private model endpoint VPC autoscaling

Here’s the general idea for private model endpoints + VPC + autoscaling:

Architecture

• Model endpoint deployed in a private subnet inside your VPC
• Access only through:
  • Internal load balancer / private service endpoint
  • VPC peering / PrivateLink / private DNS, depending on cloud/provider
• Autoscaling based on:
  • CPU / GPU utilization
  • Request rate / latency
  • Queue depth
  • Custom metrics

Key considerations

1) Networking

• Use private IPs only
• Restrict access with:
  • Security groups / firewall rules
  • NACLs / route tables
  • IAM/service auth if supported
• Ensure private access to:
  • Container registry
  • Object storage for model artifacts
  • Logging/monitoring services
    Usually via NAT, VPC endpoints, or equivalent private connectivity

2) Autoscaling

• If using containers/Kubernetes:
  • Horizontal Pod Autoscaler for replicas
  • Cluster autoscaler for nodes
  • GPU node groups if needed
• If using a managed model endpoint service:
  • Enable instance auto scaling
  • Set min/max replicas or instances
  • Use provisioned concurrency or warm pools if cold starts matter

3) Model serving

• Load model on startup
• Keep warm state if possible
• Use batching where supported
• Consider separate scaling for:
  • inference replicas
  • GPU nodes
  • CPU preprocessing workers

4) Operational concerns

• Private endpoints can increase cold-start time
• GPU capacity may be constrained in private subnets/regions
• Need observability:
  • p95/p99 latency
  • error rate
  • saturation
  • memory/GPU usage

Example pattern

1. Client in VPC sends request to private endpoint
2. Internal LB routes traffic to model serving pods/instances
3. Autoscaler adds replicas when latency/CPU/GPU/queue metrics rise
4. Instances pull model from private artifact store through VPC endpoint

If you want, I can give you one of these next:

1. AWS-specific setup
2. Azure-specific setup
3. GCP-specific setup
4. Kubernetes deployment example
5. Terraform example