Your Data. Your Models. Your Infrastructure.
We deploy production-grade LLMs that never leave your security perimeter. Complete data sovereignty, zero vendor lock-in, and enterprise-class performance with your own data.
Total Data Sovereignty
Your data never leaves your infrastructure. Not for training, not for inference, not ever. Full control, full compliance.
No Vendor Lock-in
Run open-source models like Llama, Mistral, and more. Switch models freely. Your infrastructure, your choice.
RAG with Your Data
Connect your LLMs to your proprietary data securely. Enterprise RAG pipelines that keep everything inside your perimeter.
Production-Grade LLM Stack
A complete, layered architecture designed for enterprise security, performance, and maintainability.
Application Layer
Custom AI apps, chatbots, copilots, workflow automation
Security & Governance Layer
Guardrails, PII filtering, audit logging, access control
RAG & Data Pipeline Layer
Document ingestion, vector embeddings, retrieval, reranking
Model Serving Layer
vLLM / TGI, model routing, load balancing, quantization
Infrastructure Layer
GPU compute, networking, storage, orchestration (K8s)
Deploy on Your Terms
Choose the deployment model that fits your security requirements, infrastructure, and scale.
On-Premise
Full deployment on your own hardware and data center. Maximum control, air-gap capable, zero external dependencies.
- Complete air-gap support
- Your hardware, your rules
- Zero external network calls
Private Cloud
Deployed in your own VPC on AWS, Azure, or GCP. Cloud scalability with private-network-only access.
- VPC-isolated deployment
- Cloud-native scaling
- Private endpoints only
Hybrid
Combine on-prem for sensitive workloads with private cloud for burst capacity. Best of both worlds.
- Sensitive data stays on-prem
- Cloud burst for peak loads
- Unified management plane
Private AI vs. Public API
See how private deployments compare to public API services across the dimensions that matter most to enterprises.
| Feature | On-Premise | Private Cloud | Public API |
|---|---|---|---|
| Data Sovereignty | full | full | none |
| Data Leaves Your Network | never | never | always |
| Vendor Lock-in | none | minimal | high |
| Customization / Fine-tuning | full | full | limited |
| Compliance Control | full | full | partial |
| Latency | lowest | low | variable |
| Upfront Investment | higher | medium | low |
| Long-term Cost at Scale | lower | lower | higher |
| Air-Gap Capable | yes | possible | no |
Everything on this topic
8 articlesWhat Is a Private LLM?
A private LLM runs on infrastructure you control, so prompts and documents never leave your boundary. What that means in practice, what it costs, and when it is genuinely required.
Can You Run LLaMA 3 On-Premise? Hardware Requirements and Architecture
Detailed hardware requirements for running LLaMA 3 on-premise, covering GPU options, VRAM needs, quantization tradeoffs, serving architecture, and cost analysis vs cloud APIs.
How to Deploy a Private ChatGPT Alternative for Your Enterprise
A complete guide to deploying a private, self-hosted ChatGPT alternative using open-source LLMs. Covers model selection, architecture, RAG integration, and cost analysis.
Private LLM vs. Cloud API: Total Cost of Ownership for Enterprise
A rigorous TCO comparison of private LLM deployment versus cloud API consumption at enterprise scale, covering hardware, operations, and hidden costs.
On-Premise LLM Deployment: The Enterprise Architecture Guide
A comprehensive architecture guide for deploying large language models on-premise, covering GPU selection, model serving, RAG integration, and high availability.
Best Open-Source LLMs for Enterprise Deployment in 2026
An evaluation of leading open-source LLMs for enterprise use in 2026, comparing Llama 3, Mistral, Qwen, DeepSeek, and others across benchmarks and licensing.
GPU Infrastructure Planning for Enterprise LLM Deployment
A practical guide to GPU infrastructure planning for enterprise LLM workloads, covering hardware selection, VRAM sizing, multi-GPU strategies, and procurement.
Air-Gapped AI: Deploying LLMs in Disconnected Environments
How to deploy and operate large language models in air-gapped and disconnected environments for classified, compliance, and critical infrastructure use cases.