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Quick Reference

Private AI — Quick Reference

Definitions, comparisons, and decision frameworks for private AI infrastructure.

Definition: Private AI

Private AI refers to AI systems — including training, fine-tuning, and inference — that run entirely on infrastructure owned or exclusively controlled by the organisation, with no data or model weights leaving the organisation's security perimeter. Private AI is the opposite of public cloud AI services (OpenAI API, Google Gemini API) where data is processed on third-party infrastructure. Private AI is required for organisations with data sovereignty requirements, regulated data (HIPAA, PCI DSS, ITAR), or competitive sensitivity concerns.

  • ▸On-premises private AI: GPU clusters in the organisation's own data center — maximum control, highest capital cost.
  • ▸Private cloud AI: dedicated GPU infrastructure in a colocation facility or private cloud — control without facility ownership.
  • ▸Air-gapped AI: completely isolated from external networks — required for classified, defence, and highest-sensitivity workloads.
  • ▸Hybrid private AI: private infrastructure for sensitive workloads, public cloud for non-sensitive burst capacity.

Private AI vs. Public Cloud AI

Comparison of private AI and public cloud AI for enterprise workloads.
FactorPrivate AIPublic Cloud AI
Data sovereigntyComplete — data never leaves perimeterLimited — data processed by third party
Compliance (HIPAA, PCI, ITAR)Fully controllableRequires BAA/DPA; some data types prohibited
Model IP protectionComplete — weights stay on-premisesModel weights may be exposed to provider
Cost at high utilisationLower TCO above 40% utilisationHigher at sustained utilisation
Cost at low utilisationHigher (fixed capital cost)Lower (pay-per-use)
Deployment timeline3–18 monthsHours to days
CustomisationFull control over hardware and softwareLimited to provider offerings

When to Choose Private AI Over Public Cloud AI

  • Regulated data: HIPAA (healthcare), PCI DSS (payments), ITAR (defence), GDPR (EU personal data) — where data cannot leave your perimeter.
  • Competitive sensitivity: proprietary training data, trade secrets, or model architectures that cannot be exposed to cloud providers.
  • Sustained high utilisation: GPU utilisation above 40% makes on-premises TCO lower than cloud over a 3-year horizon.
  • Latency requirements: inference latency below 50ms for real-time applications where cloud round-trip adds unacceptable overhead.
  • Air-gap requirements: classified, defence, or critical infrastructure workloads that require complete network isolation.
  • Model customisation: fine-tuning on proprietary data at scale where cloud costs are prohibitive.
6 Articles

Private AI Infrastructure

On-premises AI for regulated industries, data sovereignty requirements, and classified deployments. Complete architecture guides for healthcare, financial services, and defense.

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Reference

Key Concepts

Data Sovereignty

Ensuring that sensitive data never leaves your physical infrastructure — critical for regulated industries and government deployments.

Regulatory Compliance

HIPAA, SOC 2, FedRAMP, and other compliance frameworks as they apply to AI infrastructure design and operations.

Air-Gapped Deployments

Fully isolated AI infrastructure with no external network connectivity — required for classified and highly sensitive workloads.

On-Premises Architecture

Reference architectures for deploying AI infrastructure entirely within your own data center or colocation facility.

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