Can I host a model in my own VPC?

Yes — in many cases you can host a model inside your own VPC, but it depends on which model/provider you mean and what “host” means.

Common options

1. Self-host open-source models in your VPC

   • You run the model on your own GPUs/instances in AWS/GCP/Azure or on-prem.
   • Examples: Llama, Mistral, Qwen, Gemma, etc.
   • This is the most direct way to keep traffic and data inside your network.

2. Use a managed service deployed into your VPC

   • Some cloud AI services support private networking, VPC peering, PrivateLink/PSC, or private endpoints.
   • You still use the vendor’s platform, but traffic stays on private network paths.

3. Deploy a model serving stack in your VPC

   • You can run frameworks like vLLM, TGI, Triton, Ollama, Ray Serve, BentoML, or Kubernetes-based inference stacks.

Things to check

• Model license: Some models allow self-hosting; others have restrictions.
• Compute requirements: You’ll need enough GPU memory/throughput.
• Networking: Ensure private egress/inbound rules, IAM, secrets, logging.
• Data residency/compliance: If privacy is the reason, verify no telemetry leaves the VPC.
• Operational burden: Patching, scaling, monitoring, failover, and cost are on you.

If you mean a specific provider/model

I can give you a precise answer if you tell me:

• the model name
• the cloud provider (AWS/Azure/GCP/other)
• whether you want self-hosted or managed private deployment

If you want, I can also outline a reference architecture for hosting an LLM in your own VPC.