I'm building a private AI app inside a VPC, what hosting options fit best?

For a private AI app inside a VPC, the best hosting option usually depends on what you care about most: privacy/isolation, latency, control, GPU access, and ops burden. In practice, the best fits are usually one of these:

Best-fit options

1. Self-managed GPU instances inside the VPC

Best when: you need maximum control and strong isolation.

• Run the model on GPU VMs in your VPC
• Put an internal load balancer / private endpoint in front
• Common stacks: Kubernetes, Docker + autoscaling, or VM-based inference servers
• Good for:
  • private data
  • custom models
  • strict network controls
  • predictable workloads

Tradeoff: you manage patching, scaling, observability, and model serving.

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2. Managed AI inference service with private networking

Best when: you want less ops but still need VPC-only traffic.

• Use a managed provider that supports:
  • private link / VPC peering / private endpoints
  • no public internet exposure
• Good for:
  • faster deployment
  • less infrastructure management
  • compliance-friendly architectures

Tradeoff: less control over runtime, model choices, and data path details.

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3. Private Kubernetes platform with GPU node pools

Best when: you’re building a multi-service AI system.

• Use private K8s in your VPC
• Add GPU node pools for inference workers
• Keep:
  • API service
  • retrieval service
  • vector DB
  • model serving
  • auth/logging all internal

Tradeoff: more complexity, but strong portability and scaling.

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4. Dedicated bare metal / single-tenant GPU infrastructure

Best when: you need strict isolation or heavy sustained throughput.

• Great for regulated workloads
• Better cost/perf for continuous inference at scale
• Useful for large models or high utilization

Tradeoff: less elastic, longer provisioning, more ops.

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What I’d recommend by scenario

If you want the simplest secure setup:

• Private managed inference service + VPC private connectivity

If you want the most control:

• GPU VMs in your VPC
• or private Kubernetes with GPU nodes

If you expect scale and multiple AI components:

• Private Kubernetes cluster + internal model serving + private vector DB

If compliance/isolation is top priority:

• Single-tenant or dedicated hardware inside private network boundaries

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Key architecture pieces to include

No matter which hosting option you pick, aim for:

• No public ingress
• Private subnets only
• Internal load balancer
• Private DNS
• VPC endpoints / private links to storage and managed services
• Secrets manager / KMS
• Network policies / security groups
• Audit logs + model access logs
• Egress control so data doesn’t leave unexpectedly

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Common stack pattern

A very common private AI architecture is:

• Frontend: internal app or VPN-only access
• API layer: private service in VPC
• Inference layer: GPU-backed model server
• RAG layer: internal vector DB + document store
• Auth: IAM/SSO
• Observability: private logs/metrics

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Quick rule of thumb

• Low ops / fast launch: managed private inference
• High control / custom: self-hosted GPU in VPC
• Enterprise platform: private Kubernetes
• High throughput / strict isolation: dedicated or bare metal

If you want, I can also give you:

1. a provider-by-provider comparison (AWS/Azure/GCP/OCI/etc.), or
2. a reference architecture for a private VPC AI app.