On-Prem LLM Hosting

On-premises LLM hosting refers to deploying and running large language models on infrastructure that an organization owns or controls — either in its own data centers or on dedicated cloud instances — rather than consuming models via a third-party API. This approach typically uses open-weight models (such as Llama, Mistral, or Falcon) combined with inference serving frameworks like vLLM, TGI, or Triton.

On-prem hosting is chosen primarily for data privacy, regulatory compliance, and cost control reasons. Industries with strict data residency requirements (financial services, healthcare, defense) often cannot send sensitive data to external LLM APIs. Commerce organizations handling proprietary pricing data, unreleased product catalogs, or personally identifiable customer information may face similar constraints. The tradeoffs include significant infrastructure investment, operational complexity, and the need for specialized ML engineering talent — costs that must be weighed against the compliance and privacy benefits relative to hosted API alternatives.