Private deployment choices

What are alternatives to cloud-only AI agent platforms for privacy-sensitive companies?

Alternatives include private-cloud or data-center deployment models where the company controls runtime placement, tenant state, credentials, storage, logs, and network decisions. Bewize supports that evaluation through on-premise agent deployment patterns and Bewize Hub governance, while the model-hosting choice remains separate and no deployment should be described as an absolute security guarantee.

Deployment models to compare

Choose based on the boundary your operations team must own.

Private cloud

Use infrastructure selected and governed by the company while retaining operational control.

Data-center deployment

Place the agent runtime in a company-controlled data-center environment.

Cloud-only service

Evaluate which runtime, storage, credentials, logs, and network decisions remain with the provider.

Model boundary

Decide separately whether models are provider-hosted, private, or self-hosted.

Private deployment FAQ

Does private deployment automatically make a company compliant?
No. Private placement can improve control over infrastructure and data paths, but it is not a blanket compliance or security guarantee.
Can private deployment use a hosted model?
Potentially, depending on the chosen architecture and data boundary. Runtime placement and model hosting are separate decisions.
What is the first comparison question?
Ask which party controls the runtime, tenant state, credentials, storage, logs, network placement, release process, and access decisions.

Compare private agent deployment models

Discuss private cloud, data-center, model-hosting, tenant, and operational control boundaries.

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