Private AI Hosting on Infrastructure You Control

What private AI hosting means and who needs it

With a hosted AI service, every prompt travels to a provider's systems for processing. For many uses that is acceptable. For others it is not: client files under confidentiality agreements, patient or financial records, source code, legal documents, or anything a contract or regulator says must stay within your control. Private AI hosting removes the third party from the path. The model weights sit on your server, inference happens there, and nothing is transmitted to an outside AI provider.

This is practical because open-weight models have become capable. Models from the Llama, Mistral and Qwen families handle summarization, extraction, classification, question answering over documents and many drafting tasks well. They are not identical to the largest hosted models on the hardest reasoning problems, and we are direct about that. We test candidates on your tasks first, and where it makes sense we design a hybrid setup that keeps sensitive work private.

• No prompts sent to outside AI providers
• Full control over logs and retention
• Predictable infrastructure cost at steady volume
• No dependence on a provider's model changes
• Can run without public internet access

Dedicated servers or cloud servers for running open-weight LLMs

The right infrastructure depends on the workload, so we size hardware per project rather than selling a fixed configuration. The main inputs are the model's memory footprint, how many people or processes will use it at once, acceptable response time and context length. Quantization can reduce memory needs with a modest effect on quality. Smaller models and embedding workloads can run on modest hardware, while larger models and heavy concurrency call for GPU acceleration.

Private AI hosting builds directly on our infrastructure services. A dedicated server gives you single-tenant hardware and steady performance for constant workloads. Cloud servers suit variable demand, pilots and environments that need to scale up or down. We can also deploy into your existing cloud account or on-premises hardware. In every case you receive the same deliverable: a secured, monitored, documented inference endpoint that your applications and automations can call.