Why Open-Source and Why On-Premise AI?
Cloud AI services like ChatGPT or Microsoft Copilot are currently at the peak of popularity. But what if you could run your own version of such AI—entirely on-premise, without relying on external vendors, with full control over your data, and with zero API fees? That’s exactly what OpenPrime AI enables—an open-source AI solution designed for organizations that take digital sovereignty seriously.
OpenPrime AI is a modern on-premise AI platform that connects open-source LLMs like LLaMA 3, Mistral, Qwen, or DeepSeek with a powerful environment for document processing, semantic search, and interactive data workflows—without the need for internet access or monthly API fees.
Why Build Your Own AI Infrastructure?
Deploying on-premise AI in your own data center comes with several benefits:
- Data stays in-house – No sensitive data sent to the cloud
- No vendor lock-in – Entire solution built on open-source components
- AI trained on your data – Relevant answers tailored to your domain
- No API call fees – Everything runs locally, with predictable costs
- Fully customizable – From model behavior to integration with your systems
Architecture: AI Running on Your Infrastructure
The entire solution runs on SUSE RKE2 Kubernetes or any certified Kubernetes distribution, deployed in your data center, on your hardware or in a virtualization platform like VMware. Key components include:
- OpenWebUI – A user interface with pipe/action function support
- Ollama – Orchestration of LLMs (LLaMA, Mistral, Gemma)
- Milvus – Vector database for semantic search
- MinIO S3 – Object storage for documents
- MLflow & PyTorch – For experimentation, logging, and model training
- Ansible + Helm + CI/CD – For fully automated deployments
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A GPU is not required but can significantly speed up responses—from 1 minute to just a few seconds.
When Does a Private AI Make Sense?
Security & GDPR Compliance
Your data never leaves your infrastructure. Every component is auditable, and you can integrate your own IAM systems (Keycloak, LDAP, Google Workspace…).
AI Trained on Your Internal Documents
Unlike public models, your own model can learn company terminology, documentation, contracts, and internal processes.
Low Operational Costs
No monthly API fees. Just an initial investment in infrastructure. For 100–1000 users, the total cost is significantly lower than commercial alternatives.
| Feature | Cloud AI | OpenPrime AI (on-premise) |
|---|---|---|
|
Data Control
|
Limited |
100 % |
|
Cost
|
API & compute |
No licenses |
|
Vendor lock-in |
Yes
|
None |
|
Customization |
Limited |
Fully open
|
|
Auditability |
Low |
Full |
Real-World Use Cases
Document Summarization and Comparison – e.g., contracts or CVs
AI Trainer for Employees – Private knowledge base + interactive training
Internal Helpdesk or Legal AI Assistant
Search Through Internal Data Using Natural Language
What’s Needed for On-Premise AI?
Infrastructure for approximately 100–1000 users:
- CPU: ~30 vCPU
- RAM: ~80 GB
- SSD: ~400 GB
- VMs: 8 (including HA for Milvus, MinIO, OpenWebUI)
- GPU: Recommended (e.g., L40s, A100)
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You don’t need to start with the most expensive servers—a CPU-only deployment is sufficient for testing and can be scaled up later.
For who is OpenPrime AI?
Executives & Leadership – Full data control, lower costs, higher security
IT & DevOps Teams – Experimentation, training custom models, API integrations
HR & Recruiters – Fast CV parsing, AI-powered onboarding
Infosec – Full auditability, zero data transfer to the cloud
Conclusion
If you’re looking to harness the power of AI without compromising on security, cost, or vendor independence, an open-source solution like OpenPrime AI is a logical step forward. With open code, local deployment, and a modular architecture, you gain complete freedom and flexibility.
Lukáš Cagarda
DevOps Engineer
