Run AI within your organization's boundaries.

Private AI deployment is an approach in which models and enterprise data run on infrastructure the organization controls. Sanal Çekirdek builds this deployment in three tiers: on-premises infrastructure, private cloud or VPC, and managed services. The architecture is model-agnostic; open-source and commercial models run under the same control framework.

This page covers the deployment architecture: tier selection, how models are operated, the data boundary, and the update discipline. The compliance needs this architecture addresses are covered on the Data Sovereignty and Compliance page.

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Three deployment tiers

Private AI is not a single installation pattern. Three tiers are weighed against data classification, existing infrastructure, and operating capacity: the organization's own data center, private cloud or VPC, and managed services on infrastructure that stays in-house. The decision balances confidentiality requirements against the operating load the team can carry.

On-premises: tightest data boundary, highest operating responsibility
Private cloud / VPC: dedicated resources with flexible capacity
Managed services: infrastructure in-house, operating duties shared

Running open-source models

Open-source models run on the organization's hardware; model weights and adaptations stay in-house. Model selection relies on comparative evaluation against the organization's own tasks rather than public leaderboards. Capacity planning is sized by model size, concurrency, and latency targets, and validated with load testing before purchase decisions.

Model weights and adaptations in the corporate inventory
Task-based model comparison with documented rationale
Capacity plan driven by model size and concurrency
Quantization and serving-layer optimization

Data boundary and access architecture

Prompts, responses, and source data do not leave the defined boundary. Model services run in segregated network zones; required external connections are inventoried, with their justification visible in the design. Access is defined through role-based rules tied to the corporate identity system and is logged.

Network segregation and externally closed model services
Role-based access tied to the corporate identity system
Prompt and response logs retained in-house
External connection inventory with recorded justification

Running models, versioning, and rollback

Installation is not a one-time task; the model must be operated throughout its lifecycle. New versions are tested against the organization's evaluation set and compared with the current version before reaching production. A rollback procedure to the previous version is defined at the start of every transition.

Model and prompt versioning records
Regular comparative evaluation on enterprise data
Controlled cutover and a rollback plan
Capacity, latency, and cost monitoring

How we work

We assess data classification, the use case, and operating capacity together.
We design the appropriate deployment tier and target architecture.
We bring the model live in your environment with a pilot use case.
We move to production against acceptance criteria and run versioning and monitoring together.

How success is measured

Inventory of connections and requests crossing the data boundary
Task-specific accuracy and version-to-version comparison results
Per-request latency, capacity saturation, and cost trends
Access log integrity and closure of access reviews

Frequently asked questions

Which deployment tier should be chosen for private AI?

The decision follows data classification, existing infrastructure, and operating capacity. The most sensitive data points to on-premises deployment, the need for dedicated resources to private cloud, and the need to delegate operating duties to the managed tier.

Are open-source models sufficient for enterprise use?

Sufficiency is measured through comparative evaluation on the organization's own task set; public leaderboards alone are not enough. The decision should be made by measurement.

How are model updates managed in a private deployment?

A new version is first tested against the organization's evaluation set, compared with the current version, and released through a controlled cutover. Rollback to the previous version is part of every transition.

How is the hardware investment sized?

Hardware needs are determined by model size, concurrency, and latency targets; there is no single standard. Pilot load tests clarify sizing before purchase decisions.

Private AI is not a hardware decision; it is a control and operating model. Let's map your deployment architecture together.

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