Can we run Claude or GPT models inside our own VPC?

Claude and GPT-class models can be run inside a private network boundary, though the exact mechanism differs by provider since neither Anthropic nor OpenAI offers direct on-premise deployment of their proprietary models. Claude is available through Amazon Bedrock and Google Cloud Vertex AI, both of which support private connectivity through AWS PrivateLink or Google Private Service Connect so that traffic to the model never traverses the public internet and stays within the customer's own VPC. GPT models are similarly available through Azure OpenAI with VNet injection and private endpoints, keeping inference traffic inside an enterprise's Azure virtual network. In all these cases the model weights themselves still run on the provider's infrastructure rather than truly on-premise, so running inside a VPC means private network connectivity to a managed endpoint rather than the model running on hardware the enterprise physically controls. For enterprises that need the model itself running on owned or dedicated infrastructure, an open-weight model such as Llama or Mistral served with vLLM or TensorRT-LLM on dedicated GPUs is the only option that achieves full infrastructure control. Nanobase AI configures private VPC connectivity for Bedrock and Azure OpenAI, or deploys open-weight alternatives, depending on how much infrastructure control a customer actually needs.

Private connectivity is not the same as private infrastructure

The phrase "run Claude or GPT inside our VPC" gets used loosely, and the distinction it glosses over matters for anyone making an infrastructure decision based on it. Private connectivity means traffic to a managed model endpoint never traverses the public internet; it does not mean the model weights are running on hardware the enterprise physically controls, since neither Anthropic nor OpenAI offers on-premise deployment of their proprietary models. For Claude specifically, this means calling it through Amazon Bedrock or Google Cloud Vertex AI over a private network path; for GPT-class models, it means Azure OpenAI with VNet integration. In every case, the model still runs on the provider's infrastructure.

The private connectivity options by provider and model

Across all three, the underlying pattern is identical: a private network interface routes API calls to the managed service without exposing traffic to the public internet, which satisfies many network security policies without requiring the model itself to move.

When private connectivity is sufficient, and when it is not

For most enterprise security policies concerned with network exposure, not sending inference traffic over the public internet, private connectivity to Bedrock, Vertex AI, or Azure OpenAI fully satisfies the requirement. This covers the common case of an internal application calling a foundation model where the concern is network path, not physical infrastructure ownership. The distinction becomes material specifically for organizations that require the model to run on infrastructure they physically control or fully audit, whether for the strictest compliance regimes or contractual obligations that go beyond network isolation. In that case, private connectivity to a managed endpoint does not satisfy the requirement regardless of how well the networking is configured.

The path to genuine infrastructure control

For enterprises that need the model itself running on owned or dedicated hardware, the only route is an open-weight model, such as Llama, Mistral, or Qwen, served with vLLM or TensorRT-LLM on GPUs the enterprise controls, whether on-premise or in a dedicated cloud tenancy. This trades Claude or GPT's specific capabilities for full infrastructure control and typically requires a separate model evaluation to confirm an open-weight alternative meets the quality bar the application needs. Some enterprises resolve this by using Claude or GPT through private connectivity for general tasks while running a self-hosted open-weight model for the specific workload that genuinely requires infrastructure-level control.

Frequently asked questions

Does PrivateLink or VNet integration change the model's behavior or output?

No, these are network-layer configurations that change how traffic reaches the model endpoint; they have no effect on the model's responses, latency characteristics beyond removing public internet routing, or any other functional behavior.

Can Claude run entirely on-premise?

Not as of 2026; Anthropic does not offer on-premise deployment of Claude. The available path is calling Claude through Amazon Bedrock or Google Cloud Vertex AI with private connectivity, which keeps traffic off the public internet but keeps the model running on the provider's infrastructure.

Is private connectivity to Bedrock or Azure OpenAI required for compliance, or optional?

It depends on the specific compliance framework and internal security policy; many frameworks do not strictly require it, but it is a common and relatively low-effort hardening step that many enterprises adopt regardless, given the modest additional cost.

If we need full infrastructure control later, can we migrate from Bedrock or Azure OpenAI to a self-hosted model?

Yes, though it requires provisioning GPU capacity, standing up a serving stack, and validating that an open-weight model meets the application's quality bar, which is a more involved migration than simply changing network configuration.

How Nanobase AI helps

Nanobase AI configures private VPC connectivity for Bedrock, Vertex AI, and Azure OpenAI, or deploys open-weight alternatives on infrastructure a customer fully controls, depending on how much infrastructure control a specific workload actually needs. Our on-premise LLM deployment guide covers the self-hosted path in more depth, and our solutions page outlines the full range of options.

Ready to discuss your project? Contact Nanobase AI or email hello@bumu.tech.