Choose the placement.
Compare local and hosted options against quality, latency, capacity and cost. Check provider terms, supported regions and data handling.
Private & hybrid AI
Choose where it runs.Control where data goes.
Choose the deployment around the workload. Model serving, access controls, networking and monitoring engineered as part of one system.
Explore the capabilityENGINEERING FOCUS
01Data classification
02Permitted model route
03Controlled execution
Private is an architecture decision.
A local application can still send information to a hosted model. Map the complete request path, including logs, embeddings, tools and fallback providers, before describing a system as private.
Enforce the boundary.
Control outbound connections, isolate workloads and protect credentials. Make allowed model routes explicit; do not use an unrestricted fallback.
Operate the platform.
Plan upgrades, capacity, recovery and observability. Local inference adds infrastructure responsibilities as well as deployment options.
A possible workflowIllustrative example.
Keep a sensitive retrieval index inside the organisation and allow only approved requests to reach a hosted model.
On-premise does not automatically mean isolated or compliant. Every data path and operational dependency needs to be checked.
A closer look
Good questions.
Straight answers.
Must everything run locally?
Only when the requirement calls for it. Hybrid designs may be appropriate when the data boundary is explicit and enforceable.
Will a local model perform as well?
That depends on the task and model. Evaluate representative work, infrastructure cost and operating effort before deciding.
Technical reference: NVIDIA: runtime isolation and policy (opens in a new tab)
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