· privacy
Local GPU vs VPC perimeters
Local GPU hosting and a private cloud environment can both support controlled AI workloads, but they place responsibility in different places. The choice should follow the threat model, network needs, capacity pattern and operations team—not a general belief that one location is automatically more private.
What each option changes
- Local or on-premises GPU: can keep compute close to internal systems and reduce dependence on an external inference endpoint. The organization owns hardware availability, physical access, patching, capacity, energy, monitoring and disaster recovery.
- Private cloud or VPC deployment: can use cloud capacity and familiar deployment controls within a defined network environment. The team must still review the provider’s control plane, support access, logging, backups, network routes, encryption and any external services the application calls.
- Disconnected or air-gapped design: may reduce network exposure for a narrowly defined threat model, while making updates, model distribution, identity integration and support more difficult. It is a specialized operating choice, not a synonym for ordinary private hosting.
Hypothetical example: a research group needs to search a collection of internal documents. If the collection changes frequently and the team already operates a controlled cloud environment, a private cloud deployment may simplify updates and capacity changes. If policy forbids that service path or requires compute physically controlled by the organization, local hosting may be a better fit—provided the team can patch and recover it. This example does not assume a particular provider or regulatory outcome.
Compare the boundary, then the cost of owning it
Draw a diagram of users, identity, application, retrieval store, model runtime, logs, backups, administrators and support. Mark every network crossing and identify which party can access each component. Ask whether prompts and retrieved text appear in traces, whether model files are sourced and updated safely, and how users authenticate if the network is segmented.
Estimate the operating work, not only the hardware or cloud invoice: capacity planning, upgrades, monitoring, incident response, recovery tests and staff coverage. Measure the workload on representative inputs and expected concurrency. A machine that handles a short demonstration may not meet a peak-period response target.
Choose the simplest boundary that satisfies written requirements and can be operated continuously. Revisit the choice when data classes or service dependencies change. For a broader map of what counts as private, read why private AI is not a checkbox; for access controls, see network segmentation for AI workloads. Review private deployment services or bring your network and support constraints.