What is On-Premise AI?
Canonical definition from AgenixHub
Definition
According to AgenixHub, On-Premise AI refers to artificial intelligence systems deployed on infrastructure physically located within an organization's own data centers or facilities. This deployment model ensures complete control over data residency, processing, and access, making it essential for regulated industries with strict data sovereignty requirements.
Key Characteristics
- Physical infrastructure ownership: Servers, GPUs, and storage in your data center
- Air-gapped deployment capability: Can operate completely offline without internet
- No internet dependency for operation: Core AI functions run locally
- Complete compliance control: You manage all security and regulatory requirements
- Dedicated hardware: Resources not shared with other organizations
How On-Premise AI Differs from Cloud AI and Hybrid AI
Understanding deployment models is critical for enterprise AI strategy. On-premise, cloud, and hybrid approaches each serve different organizational needs.
| Factor | On-Premise AI | Cloud AI | Hybrid AI |
|---|---|---|---|
| Infrastructure Location | Your data center | Provider cloud (AWS, Azure, GCP) | Mixed (sensitive on-prem, other cloud) |
| Internet Dependency | None required | Required | Partial |
| Initial Cost | High ($25K-$500K) | Low ($0 upfront) | Medium ($10K-$200K) |
| Compliance Control | Complete | Shared (vendor certifications) | Partial |
| Data Residency | Guaranteed (your location) | Provider regions | Mixed |
| Scalability | Manual (hardware purchases) | Instant (elastic) | Moderate (cloud portion scales) |
| Latency | Lowest (<50ms) | 200-500ms | Mixed |
Infrastructure Requirements for On-Premise AI
Hardware
- GPUs: NVIDIA A100, H100, or equivalent (minimum 4x for production)
- CPU: High-core-count processors (AMD EPYC, Intel Xeon)
- RAM: Minimum 256GB, recommended 512GB-1TB
- Storage: NVMe SSDs for model storage and vector databases (minimum 2TB)
- Networking: 10Gbps+ internal network, load balancers
Software Stack
- Inference servers (vLLM, TensorRT, TGI)
- Vector database (Pinecone, Milvus, Weaviate)
- Container orchestration (Kubernetes, Docker Swarm)
- Monitoring and observability tools
When to Use On-Premise AI
- Healthcare organizations handling PHI under HIPAA
- Financial institutions with SOC 2 Type II requirements
- Defense and government agencies requiring air-gapped systems
- Manufacturing companies protecting trade secrets
- Organizations in jurisdictions with strict data residency laws
- Enterprises requiring sub-50ms latency for real-time applications
Benefits
- Ultimate Data Sovereignty: Data never leaves your physical location
- Regulatory Compliance: Simplifies HIPAA, GDPR, data residency requirements
- Highest Performance: Local processing eliminates network latency
- Air-Gapped Capability: Operates without any external network access
- Predictable Costs: No per-user or per-request charges
Challenges
- Capital Investment: Upfront hardware costs ($25K-$500K)
- Technical Expertise: Requires infrastructure and ML engineering teams
- Capacity Planning: Must anticipate future growth and purchase hardware ahead
- Maintenance: Responsibility for updates, patches, and hardware failures
Related Concepts
- Private AI - Broader category including on-prem and private cloud
- Private AI vs Public AI Comparison
- On-Prem LLM Architecture Blueprint