Run Powerful LLMs on
Your Own Infrastructure
When data can't leave your walls, API-based LLMs aren't an option. We deploy and optimize open-source models like Llama, Mistral, and Phi on your servers — giving you enterprise AI capabilities with zero data exposure and predictable costs.
Model Quality (vs GPT-4)
Inference Speed
Cost Efficiency
Data Privacy Score
When Self-Hosted LLMs Are the Right Choice
Not every org needs a private LLM — but for these situations, it's the only option that works.
Bank Internal AI Tools
Regulators won't let customer data hit third-party APIs. A self-hosted LLM powers internal tools — document analysis, code generation, report drafting — without any data leaving the bank's network.
Banking & FinanceHealthcare Clinical AI
PHI can't be sent to OpenAI. A private Llama deployment processes clinical notes, generates discharge summaries, and answers physician queries — all within the hospital's HIPAA-compliant environment.
HealthcareDefense & Government
Classified and sensitive government data requires air-gapped AI. Self-hosted models run on government-approved infrastructure with no internet connectivity.
Government & DefenseLaw Firm Confidentiality
Attorney-client privilege means legal documents can't be processed by external AI. A private LLM handles contract review, research, and drafting entirely within the firm's systems.
LegalHigh-Volume Cost Optimization
When you're making 500K+ API calls per month, self-hosting becomes dramatically cheaper. One client cut their monthly LLM spend from $48K to $14K by moving to self-hosted.
Enterprise SaaSWhat We Deploy and Optimize
From model selection to GPU optimization — we handle the entire self-hosted LLM stack.
Model Selection & Benchmarking
We benchmark Llama 3, Mistral, Phi, Qwen, and other open-source models against your specific tasks — picking the best quality-to-cost ratio for your use case.
Inference Optimization
vLLM, TGI, and custom serving configurations optimized for your hardware. Quantization (GPTQ, AWQ) to fit larger models on smaller GPUs without meaningful quality loss.
Fine-Tuning on Your Data
LoRA and QLoRA fine-tuning to specialize the model on your domain — improving accuracy by 15-30% on domain-specific tasks.
Infrastructure Architecture
GPU cluster sizing, load balancing, auto-scaling, and multi-model serving designed for your throughput and latency requirements.
Security & Access Control
API gateway with authentication, rate limiting, usage logging, and role-based model access. Production-grade security for internal AI services.
Monitoring & Observability
Dashboards for GPU utilization, inference latency, queue depth, model accuracy, and cost per request — all the metrics you need to manage the system.
How We Deploy Your Private LLM
Requirements Analysis
We assess your use cases, data sensitivity requirements, expected throughput, latency targets, and existing infrastructure to design the right solution.
Model Benchmarking
We test 3-5 candidate models against your actual prompts and tasks — measuring quality, speed, and resource consumption on your hardware.
Infrastructure Setup
GPU provisioning, model serving stack deployment, API gateway configuration, and load balancing — all infrastructure-as-code for reproducibility.
Fine-Tuning & Optimization
Domain-specific fine-tuning, quantization, and prompt optimization to maximize quality while minimizing hardware requirements.
Production & Handover
Full documentation, runbooks, monitoring setup, and knowledge transfer to your team. You own and operate the system — we provide ongoing support as needed.
Your Data Is Too Sensitive for Third-Party APIs.
Deploy a private LLM on your infrastructure — free architecture assessment and model benchmarking for your use case.
Book Free ConsultationEnterprise LLM capabilities with zero data exposure.
Self-hosted LLMs give you the power of modern AI without the privacy trade-off. Your prompts, your data, and your outputs never leave your infrastructure.
Built for the Most Demanding Compliance Environments
When regulators, auditors, and CISOs are watching, your AI infrastructure needs to be bulletproof.
Why Teams Choose Us for Self-Hosted LLMs
We've done this 20+ times. The lessons we've learned save you months of trial and error.
Tell Us About Your Private LLM Requirements
Describe your use case, data sensitivity, and infrastructure — we'll respond with an architecture proposal and model benchmark plan.
Case Study
Regional Bank Deploys Private LLM for Document Analysis — Cuts Processing Time 65%
A regional bank with $8B in assets needed AI-powered document analysis for loan processing but couldn't send customer financial documents to external APIs due to regulatory and board-level data governance policies.
The Problem
Strict data governance policies prevented the bank from using cloud-based AI APIs, leaving manual document processing as the only option.
Our Approach: We deployed a fine-tuned Llama 3 70B model on the bank's private cloud (AWS GovCloud). The model was fine-tuned on 12,000 anonymized loan documents to extract income data, identify discrepancies, flag risk factors, and generate preliminary underwriting summaries. The entire system runs within the bank's VPC with no external network access. GPU infrastructure was sized for 200 concurrent document analyses with P95 latency under 8 seconds per document.
Frequently Asked Questions
For general tasks, there's a gap — maybe 10-15% on benchmarks. But after fine-tuning on your domain data, self-hosted models often match or exceed API models on your specific tasks. The gap is closing rapidly with each new open-source release.
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