Deployment comparison
How do private AI deployment models compare?
Private AI can run on a desk-side workstation, an on-premises server, a customer-managed private cloud, or a controlled hybrid route to a hosted model. The right choice depends on what is allowed to cross the customer boundary, who operates the infrastructure, the required model capability, latency, update process, and the evidence the organization needs to retain.
In practice: Security depends on the actual data flow, operators, provider terms, identity model, and release process—not the deployment label alone.
01
The comparison at a glance
Use the table as a starting point for architecture discovery. Record the actual answer for every component rather than treating “private cloud” or “on premises” as a complete security claim.
| Model | What may leave the local network | Primary operator | Best fit | Main tradeoff |
|---|---|---|---|---|
| Desk-side / local | Nothing unless an explicit connector is enabled. | Customer team or managed operator | Small, isolated workflows and strict data locality | Limited capacity, hardware lifecycle, local support |
| On-premises server | Policy-approved telemetry or updates only. | Customer IT with implementation support | Shared internal workflows and central identity | Infrastructure and model operations remain substantial |
| Customer-managed private cloud | Traffic stays inside approved cloud accounts and private links; provider control-plane metadata may remain. | Customer cloud/platform team | Elastic capacity with customer networking and keys | Cloud configuration and service boundaries need close review |
| Controlled hosted-model route | Only the approved, minimized prompt or masked payload. | Customer workflow plus model provider | Frontier capability where policy permits external inference | Provider terms, egress, retention, and payload minimization |
02
Draw three boundaries, not one
A deployment diagram should distinguish the data plane, the control plane, and the evidence plane. The data plane carries files and model context. The control plane manages identity, policy, keys, versions, and release permissions. The evidence plane stores what happened and who approved it. These planes can have different operators and retention rules.
- Data plane: files, OCR text, retrieval passages, prompts, model outputs
- Control plane: users, service accounts, policies, keys, configuration, deployment
- Evidence plane: source references, tool calls, validation results, reviewer actions, exports
03
Decision criteria that change the answer
Model quality alone rarely decides a regulated deployment. Teams should score each candidate route against data classification, required context length, throughput, response time, recovery targets, update cadence, operator skills, provider contract, evidence needs, and total operational ownership.
- Maximum sensitivity and permitted egress
- Quality on the organization’s approved evaluation set
- Peak and sustained workload
- Identity, network, key, and secrets integration
- Patching, rollback, monitoring, and incident ownership
- Retention, deletion, audit export, and portability
04
A defensible hybrid route
Hybrid does not have to mean uncontrolled copying. A local stage can classify the document, remove or tokenize sensitive values, select only necessary context, and apply a policy decision before an approved payload reaches a hosted model. The response can return to the customer environment for deterministic validation and human approval. The evidence record should show each transformation and route decision.
05
Sources
Primary references and technical material used in this guide.
- [1] NIST SP 800-207: Zero Trust Architecture ↗
Authoritative guidance on resource-centric access and trust decisions.
- [2] NIST AI Risk Management Framework ↗
Lifecycle risk, governance, measurement, and management.
- [3] GDPR, Article 25 ↗
Official data-protection-by-design text.
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