VPC vs on-prem vs air-gapped inference

The four deployment models

Private LLM inference breaks into four deployment modes, each with a different perimeter. Understanding where each draws the line is the first step in matching the architecture to your security requirements.

• VPC: GPUs run in your cloud account, connected over private networking. No hardware to manage. 5-7 days to production. Best for regulated, cloud-native workloads.
• On-prem: GPUs run in your own data center. Full physical control, floor-level latency. You manage hardware lifecycle — or HyperInfer manages it remotely over an outbound-only control channel.
• Private cloud: HyperInfer-operated hardware in your facility. Reserved, single-tenant capacity. No GPU ops team required. For organizations with facility investment but not GPU expertise.
• Air-gapped: zero external network connectivity of any kind. Model weights and updates arrive on physical media. The GPUs have never touched a network. For classified and critical infrastructure workloads.

The data boundary question

The four models differ most sharply on where the data perimeter sits. In a VPC deployment, prompts and completions stay inside your cloud account; the control plane sends only operational metadata — token counts, timestamps, model IDs — outbound. On-prem and private cloud tighten that further: everything stays within your facility, and the only external traffic is the optional control channel for orchestration. An air-gapped deployment eliminates even that — there is no outbound path of any kind, so the data boundary question stops being a question. If your data classification guide says network egress of operational metadata is still egress, air-gapped is your answer.

Ops burden and time-to-production

A VPC deployment reaches production fastest — typically within a week, because the cloud provider already runs the physical infrastructure. On-prem adds hardware provisioning, networking and physical security into your timeline, stretching it to weeks or months depending on your data center readiness. Private cloud sits between them: HyperInfer handles the GPU operations but your facility and power need to be ready. Air-gapped deployments take the longest because every update — model weights, container images, configuration changes — crosses the boundary on physical media through a documented, verifiable process. But for the organizations that need it, that process is the whole point.

When each deployment model makes sense

If your workload is cloud-native and regulated but not classified, VPC deployment hits the sweet spot — private, fast to production, minimal ops. If physical control is non-negotiable and you already operate a data center, on-prem wins. Private cloud is the answer when you have the facility but not the GPU operations team. Air-gapped is the only choice when your classification guide says a network connection is a violation. One thing worth noting: all four models use the same single-tenant, dedicated GPUs. The hardware isolation is identical. What changes is the perimeter — and that decision should be driven by your data classification, not by vendor preference. Read more about how HyperInfer maps to each deployment model.

One API, regardless of perimeter

Every deployment model exposes the same OpenAI-compatible API. Your application code doesn't change when you move from a VPC proof of concept to a production air-gapped deployment — just the base URL changes. The same chat/completions endpoint, the same streaming response format, the same client SDKs. And the same zero-retention guarantee holds whether the GPU is in Frankfurt, Dublin or a windowless SCIF in Virginia. The perimeter is different. The interface is the same.