Question 1

What is private GenAI infrastructure?

Accepted Answer

Private GenAI infrastructure is a complete generative AI stack—models, orchestration, governance, and application layer—that operates entirely within your organization's controlled environment. Unlike cloud-hosted GenAI services where prompts and data flow to external providers, private GenAI infrastructure processes everything on-premises or in your own data center. This means your proprietary data, trade secrets, and regulated information never leave your security perimeter. The infrastructure includes GPU compute for model inference, LLM orchestration for routing and managing models, security layers for access control and audit, and an application framework for building AI-driven apps.

Question 2

How do you deploy generative AI without cloud dependencies?

Accepted Answer

Deploying GenAI without cloud dependencies requires four layers: (1) GPU compute infrastructure—NVIDIA DGX, H200, or Jetson systems depending on scale; (2) model hosting using open-weight models like Llama 3.1, Mixtral, or DBRX that can be downloaded and run locally; (3) an orchestration runtime that handles model routing, security, data connectors, and policy enforcement; and (4) an application factory that provides templates, workflows, and governance-by-default for building production AI apps. Enfuse's sovereign AI platform provides the runtime and factory layers, so your team focuses on applications rather than infrastructure plumbing. The entire stack operates with zero external network dependencies.

Question 3

What compliance frameworks does private GenAI support?

Accepted Answer

Private GenAI infrastructure inherits compliance from your existing infrastructure controls, making it suitable for ITAR (International Traffic in Arms Regulations), FedRAMP (Federal Risk and Authorization Management Program), HIPAA (Health Insurance Portability and Accountability Act), SOX (Sarbanes-Oxley), CMMC (Cybersecurity Maturity Model Certification), GDPR data residency requirements, and industry-specific frameworks like PCI DSS for financial services. Because data never leaves your perimeter, the compliance burden is dramatically simplified compared to cloud AI services where you must audit the provider's controls in addition to your own.

Question 4

Can private GenAI infrastructure scale across departments?

Accepted Answer

Yes. A well-architected private GenAI platform serves as a shared enterprise resource. The runtime layer handles multi-tenant model serving—different departments can use different models or share common ones, with role-based access controlling who can access what. The App Factory layer enables each department to build their own AI applications using shared infrastructure without interfering with others. GPU resources can be allocated dynamically based on demand, and the governance framework ensures every application meets organizational standards regardless of which team built it. This shared-service model makes private GenAI infrastructure more cost-effective at scale than per-department cloud AI subscriptions.

Question 5

What models can run on private GenAI infrastructure?

Accepted Answer

Any open-weight model can run on private infrastructure. The current generation of open models—including Meta's Llama 3.1 (8B to 405B parameters), Mistral's Mixtral and Large models, Databricks' DBRX, and dozens of specialized models—match or exceed the performance of cloud-exclusive models on most enterprise benchmarks. You can also fine-tune these models on your proprietary data to create custom models that outperform general-purpose alternatives for your specific use cases. The Enfuse runtime supports model routing across multiple models, so you can use smaller, faster models for simple tasks and larger models for complex reasoning—optimizing both performance and GPU utilization.

Question 6

How does private GenAI differ from VPN-based cloud AI?

Accepted Answer

A VPN creates a secure tunnel between your network and the cloud provider—but your data still arrives at and is processed on the provider's infrastructure. The provider's employees, systems, and processes can still access your prompts and outputs. A VPN protects data in transit, not data at rest or in processing. Private GenAI infrastructure is fundamentally different: the models run on your hardware, in your data center, under your control. No data leaves your perimeter at any point. This distinction is critical for ITAR-regulated content, classified information, trade secrets, and any data subject to data residency requirements. VPN-based cloud AI is not sovereign—it's encrypted cloud AI.

Question 7

What is the cost difference between private and cloud GenAI?

Accepted Answer

Cloud GenAI charges per token—typically $0.01-0.06 per 1K tokens for capable models. At low volumes this is attractive, but costs grow linearly with usage. Private GenAI requires upfront infrastructure investment (GPU hardware, networking, power/cooling) but has zero marginal cost per token. The crossover typically occurs at 1-2 million tokens per day sustained usage, where private infrastructure achieves 40-60% lower total cost of ownership within 18-24 months. For enterprises running multiple AI applications across departments, the economics strongly favor private infrastructure. We provide detailed TCO modeling as part of our architecture consultation.

Question 8

How long does it take to deploy private GenAI infrastructure?

Accepted Answer

With the Enfuse platform, pilot deployments typically take 6-8 weeks from hardware availability to first production application. This includes runtime installation, model optimization for your hardware, security integration with your existing identity and access management, and development of an initial AI application. Full production rollout with governance integration, multi-department access, and app catalog deployment takes 3-6 months. This compares favorably to 12-18 month timelines for building private GenAI capability from scratch.

Feature	Sovereign AI	Cloud AI
Data Location Where generative AI processing occurs	Your infrastructure	Provider's datacenters
Data Egress Whether data leaves your network perimeter	Zero	All prompts & outputs transmitted
Model Selection Freedom to choose and customize AI models	Any open-weight model	Provider's catalog only
Compliance Scope Regulatory frameworks supported	Full regulatory coverage	Limited to provider certifications
Training on Your Data Fine-tune models with proprietary data safely
IP Ownership Full ownership of trained models and outputs
VPN-Based Cloud AI Bypass VPN doesn't prevent data egress to AI providers	Not needed	Still sends data to provider
Multi-Department Scaling Shared infrastructure across business units

Private Generative AI Infrastructure for Regulated Enterprises

The Problem with Cloud-Hosted Generative AI

Zero Data Egress

Full Regulatory Coverage

Proprietary Model Training

Private GenAI Architecture: How It Works

GPU Compute Layer

Orchestration Runtime

Governance Framework

Application Factory

Who Needs Private GenAI Infrastructure?

Defense & Intelligence

Financial Services

Healthcare

Government

Private GenAI vs Cloud GenAI: Key Differences

Deployment Models for Private GenAI

Fully Air-Gapped

Private Network

Hybrid with Policy Control

Continue Learning

What Is Sovereign AI?

On-Prem LLM Deployment

Air-Gapped AI Platform

Sovereign Compute

Frequently Asked Questions

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