For HIPAA, financial services, government, and any organization where data sovereignty is non-negotiable. Private LLMs running on your hardware, your network, your rules. Zero cloud dependency.
Your legal team says no. Your compliance team says no. Your CISO says no. But your CEO still wants AI. Here is how you give them both.
Regulated industries cannot send data to cloud AI providers. Full stop. HIPAA, FINRA, CMMC — the rules are clear.
OpenAI changes terms quarterly. Your AI strategy cannot depend on someone else's business model or pricing whims.
Enterprise AI: $30-75/user/month. 500 users = $180K-$450K/year. Private LLM: $7K once. Do the math.
Real-time AI inference needs local processing. Cloud round-trips add 200-500ms. Local inference: under 50ms.
Your prompts and data train other companies' models. Your competitive advantage becomes shared knowledge.
Defense, critical infrastructure, and financial firms need air-gapped AI. Cloud AI cannot do this. Period.
On-premises AI models (Llama 3, Mistral, Phi-3) on your hardware. No internet required. Full ChatGPT-like capabilities running entirely within your network perimeter.
Completely disconnected AI for classified and sensitive environments. No network connection, no data exfiltration risk. Updates via approved physical media.
Healthcare AI with audit trails and PHI protection. AI that your compliance team approves. No BAA with AI vendors needed because no AI vendor touches your data.
Train AI on your documents, processes, and terminology. It speaks your business language. Domain-specific models that outperform generic cloud AI on your tasks.
Hardware selection, deployment, and maintenance. Mac Mini clusters, NVIDIA GPU servers, or custom builds. We spec, procure, and configure everything.
Sensitive data stays local. Non-sensitive workloads use cloud AI. Best of both worlds with intelligent routing that enforces data classification policies.
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| Metric | Cloud AI | Private / On-Premises AI |
|---|---|---|
| Data privacy | Provider terms apply | Complete control |
| Cost (500 users, annual) | $180K-$450K/year | $7K one-time + $2K/year |
| Latency | 200-500ms | <50ms local |
| Compliance | Shared responsibility | Full ownership |
| Customization | Limited fine-tuning | Full model ownership |
| Internet required | Yes | No (air-gapped option) |
| Vendor lock-in | High | None (open-source models) |
1 week — Use cases, compliance requirements, infrastructure audit, model selection
1 week — Spec, procure, and configure on-premises AI hardware
1-2 weeks — Install models, configure security, integrate with your systems
2-3 weeks — Train models on your data, optimize performance, validate accuracy
Ongoing — Launch, monitor, update, and expand capabilities
For small-to-medium workloads (1-50 concurrent users), a Mac Mini with M4 Pro chip and 64GB unified memory runs models like Llama 3 8B at excellent speeds for under $2,500. For larger workloads, we deploy on NVIDIA GPU servers (A100, H100) or custom builds. We assess your requirements and recommend the most cost-effective configuration.
Smaller open-source models (7-13B parameters) perform at roughly 85-90% of GPT-4 quality for most business tasks. For domain-specific tasks where you fine-tune the model on your data, private models often outperform GPT-4 because they understand your terminology and context. Latency is significantly lower — under 50ms locally vs. 200-500ms for cloud APIs.
For air-gapped environments, we provide model updates via approved physical media (encrypted USB drives) following your secure media transfer protocols. Each update package is integrity-verified with cryptographic hashes. For non-air-gapped deployments, updates are pulled from our secure repository on a schedule you control.
Yes. When AI runs entirely on your infrastructure, there is no data transmission to third parties. No BAA is needed with an AI provider because no AI provider touches your data. We configure audit logging, access controls, encryption at rest, and all technical safeguards required by HIPAA.
For 500 users: Cloud AI costs $180K-$450K/year. Private AI costs $5K-7K for hardware and deployment, plus $1.5K-2K/year for maintenance. Over 3 years, private AI saves $530K-$1.3M. The breakeven point is typically 2-3 months.
Yes. We deploy model routing that directs queries to the most appropriate model. A fast, small model handles classification. A larger model handles complex analysis. A fine-tuned model handles domain-specific tasks. This maximizes performance while keeping hardware costs reasonable.
We primarily deploy Meta's Llama 3 family (8B and 70B), Mistral (7B and Mixtral 8x7B), and Microsoft's Phi-3 for lightweight tasks. All are fully open-source with permissive licenses for commercial use — no royalties or usage fees.
Free 30-minute consultation to assess your compliance requirements and design a private AI architecture.