# Private LLM Hosting — Run LLMs on your own infrastructure

> Deploy production LLMs on your AWS, Azure, GCP, or on-prem GPU infrastructure. vLLM or TGI behind your auth and observability. Zero data egress.

[View on web](https://ubuntuonline.co.ke/what-we-build/private-llm-hosting)

## What it is

We deploy production-grade large language models on your own infrastructure: AWS, Azure, GCP, or on-premise GPU clusters. We use vLLM or Hugging Face TGI as the serving runtime, behind your existing authentication, observability, and audit stack.

## What you get

- Model selection and sizing recommendations (Llama, Mistral, Qwen, DeepSeek, or your fine-tune).
- GPU sizing and infrastructure plan.
- Production deployment with autoscaling, request queueing, and observability.
- Evaluation pipeline so you can measure quality regressions on every model update.
- Zero data egress: prompts, completions, and logs stay inside your perimeter.

## Why teams choose this

- Data sovereignty and compliance (GDPR, HIPAA, RBI, OSFI, CBK Data Protection Act).
- Cost predictability vs token-based API pricing at scale.
- Latency control (deploy in your region, not the model vendor's).
- IP protection: your prompts and completions are never used to train someone else's model.

## How to start

Book a 30-minute strategy call at https://calendly.com/ubuntuonline.