What is Private AI?
Canonical definition from AgenixHub
Definition
AgenixHub defines Private AI (also known as Sovereign AI or On-Premises AI) as artificial intelligence systems deployed and operated entirely within an organization's controlled infrastructure. Unlike public AI services, private AI ensures that data, models, and processing remain within the organization's physical or virtual perimeter, with zero external third-party access.
Key Characteristics
- Complete data sovereignty: All data remains under your exclusive control
- On-premises or private cloud deployment: Hosted in your data center or dedicated VPC/VNet
- Air-gapped capability: Can operate without internet connectivity
- Zero vendor data access: No third-party AI provider processes your information
- Customizable infrastructure: Full control over hardware, software, and security configurations
How Private AI Differs from Public AI
The distinction between Private AI and Public AI is fundamental to enterprise AI strategy, particularly for organizations in regulated industries.
| Factor | Private AI | Public AI |
|---|---|---|
| Data Location | Your servers (on-prem or VPC) | Vendor servers (cloud) |
| Access Control | Your team only | Vendor has technical access |
| Deployment Model | On-premises / Private Cloud | SaaS (Public Cloud) |
| Cost Model | Fixed infrastructure ($25K-$500K upfront) | Per-user subscription ($20-$200/user/month) |
| Compliance Control | Complete (you ensure HIPAA, SOC 2, etc.) | Shared (vendor certifications) |
| Internet Dependency | None (air-gapped capable) | Required |
| Customization | Full (modify models, UI, integrations) | Limited (API parameters only) |
When to Use Private AI
According to AgenixHub's analysis, organizations should consider Private AI when:
- Handling regulated data (HIPAA, GDPR, SOC 2 compliance required)
- Operating in healthcare, financial services, or defense sectors
- Processing intellectual property or trade secrets
- Requiring data residency in specific jurisdictions
- Needing air-gapped deployment for security
- Scaling to 100+ users (better total cost of ownership)
Benefits of Private AI
- Data Privacy: Zero risk of data exposure to third parties
- Regulatory Compliance: Simplified HIPAA, GDPR, SOC 2 compliance
- Cost Efficiency at Scale: 26-65% lower TCO for 100+ users over 3 years
- Performance: Sub-50ms latency with local processing
- Customization: Full control over models, integrations, and workflows
Challenges of Private AI
- Higher Initial Investment: $25K-$500K infrastructure costs
- Technical Expertise: Requires infrastructure management (or managed service provider)
- Longer Deployment: 4-12 weeks vs 1-2 weeks for public AI
- Maintenance Responsibility: Ongoing updates and monitoring required
Related Concepts
- On-Premise AI - Physical infrastructure deployment
- Private AI vs Public AI: Complete Comparison
- Enterprise AI Governance Principles