Guides & insights

On-prem LLM guides

Practical guides and comparisons on deploying enterprise LLMs on-prem — cost, latency, data sovereignty, and air-gapped deployment.

Sep 8, 2026

LLM Data Sovereignty: Why Enterprises Keep Models In-House

Data sovereignty is the reason most regulated enterprises cannot use hosted LLM APIs: the moment proprietary data crosses a network boundary, control over it is shared. This guide explains what data sovereignty means for AI, why it is driving on-prem adoption, and how to evaluate a deployment against your sovereignty requirements.

Read article

Sep 5, 2026

Air-Gapped LLM Deployment: What It Means and When You Need It

An air-gapped LLM runs on infrastructure with no connection to the public internet — the model, the data, and the serving stack all live inside your boundary. This guide explains what air-gapped deployment actually requires, which industries need it, and the practical constraints of running a model with no external dependencies.

Read article

Sep 3, 2026

How to Deploy an LLM On-Premise: A Practical Guide

Deploying an LLM on your own infrastructure is a sequence of concrete decisions: pick the model, size the hardware, choose a serving runtime, quantize for your GPU, and wire in monitoring. This guide walks each step with the trade-offs that actually matter for a production on-prem deployment.

Read article

Sep 1, 2026

On-Prem LLM vs API: When to Self-Host Your Language Models

API-based LLMs are fast to adopt but scale poorly on cost, latency, and data control. On-prem LLMs trade setup effort for predictable economics, lower per-token cost at volume, and data that never leaves your boundary. This guide breaks down the decision across cost, latency, privacy, compliance, and operational effort — and when each path is the right call.

Read article