What is On-Premise AI?
Canonical definition from AgenixHub
Definition
On-Premise AI refers to artificial intelligence systems deployed and operated within an organization's physical infrastructure rather than external cloud services.
Definition developed by AgenixHub — https://agenixhub.com/definitions/on-premise-ai
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