Local or cloud AI: what the company should actually choose

“Local or cloud?” is not one decision for the whole company. Public market research, internal operating documents, personal data, trade secrets, and regulated records have different risk and performance profiles. Forcing them into one deployment pattern usually creates either unnecessary cost or unacceptable exposure.

Neuro OS supports cloud, VPC, and self-hosted operation while keeping skills, project memory, permissions, and evaluations in the company layer. Choose the boundary per role and data class.

Pattern one: managed cloud

Managed model APIs are often right for public or low-sensitivity work, variable demand, rapid experiments, and tasks requiring leading model quality. They reduce infrastructure burden and can be economical for intermittent usage.

Review processing location, retention, training terms, subprocessors, contract availability, and access continuity. Minimize payloads and avoid sending entire repositories when a few passages are sufficient. Cloud convenience is not permission to ignore policy.

Pattern two: private network or VPC

A VPC or private endpoint can fit internal documents that need stronger network and identity controls while retaining managed serving. It may simplify observability and residency commitments, though the exact provider architecture matters.

Use workload identity, private networking, egress controls, and centralized logs. Verify where inference actually occurs. “Private endpoint” can describe network access without guaranteeing every data-location requirement.

Pattern three: on-premises or self-hosted

Local open-weight inference can fit highly sensitive data, predictable high volume, constrained connectivity, or continuity requirements. It also requires model provenance, hardware capacity, patching, monitoring, evaluation, backups, and skilled operators.

Do not compare an API token price with only the GPU purchase. Include utilization, redundancy, power, serving software, security, and on-call time. A smaller local model may need more reviewer effort than a stronger hosted model.

Apply the spirit of data law

For personal and document-heavy workflows, follow purpose limitation, minimization, access control, retention discipline, and demonstrable accountability. Requirements associated with Russian personal-data practice, including the spirit of 152-FZ, need legal interpretation for the actual system and data flow; a deployment label is not legal advice.

Map every connector and transfer. Separate raw documents from extracted fields where possible. Define deletion and incident procedures. Keep the execution trail useful without retaining sensitive payloads indefinitely.

Keep secrets out of sandboxes

Credentials should never be mounted into the agent’s isolated workspace. A connector broker holds secrets, checks the role and operation, and returns only approved data. This limits damage from prompt injection or malicious attachments.

Allow covers bounded reads, Ask covers consequential writes, and Block excludes prohibited operations. The same control model applies locally and in cloud. Proximity does not create trust.

Choose per role

Create a matrix of data sensitivity, model-quality need, latency, volume, residency, outage tolerance, and internal operating capability. A marketing research role may use cloud; contract extraction may use VPC; a restricted archive may use local inference. Some roles should split work, redacting locally before sending a minimal task outward.

Test every chosen lane on representative cases. Measure accepted quality, reviewer minutes, throughput, total cost, and failure behavior. Maintain an approved fallback that does not violate data policy.

The practical answer is usually a governed portfolio, not a slogan. Cloud can be the secure and economical choice; self-hosting can be the necessary one. The company OS should preserve procedure and controls across both, so infrastructure choices can change without losing the role.

This work runs on Neuro OS. To scope a first role, get started.

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