From the team.
Notes on private AI, infrastructure and the economics of running inference on hardware you own.
Free field report
Before you decide on AI, read the honest landscape.
Twenty pages on where AI actually cuts cost or does more with fewer people, and how to make a first move that pays for itself.
20 pp
Cloud vs. On-Premises AI: A Total Cost of Ownership Decision Guide
There is no universal answer to whether cloud or on-premises AI costs less. The deciding variable is sustained utilisation, not the headline price either option advertises. Cloud inference is billed by the unit, so its cost rises in a near-straight line with how much you use it. An on-premises appliance is a largely fixed asset cost with a low marginal cost to run, so once usage is high and steady enough, the per-inference cost keeps falling while the cloud bill keeps climbing. Somewhere between those two curves is a break-even point, and finding yours is what this guide is about. The reason most cloud-versus-on-premises comparisons reach the wrong conclusion is that they price the obvious line items and ignore the ones that actually move the total. This guide walks through the full cost picture for enterprise AI, the cost lines teams routinely under- and over-estimate, how to locate your own break-even, and why the deciding factor at the margin is often control, which no spreadsheet prices well.
What Is a Private AI Appliance? Running Enterprise AI On-Premises Without the Cloud
A private AI appliance is a pre-built, on-premises hardware platform that runs your own AI models and software entirely inside your network, with no cloud dependency. You connect power, attach it to your internal network, and deploy the open models and applications you choose. Every prompt, every document and every inference result stays inside your building. For European technology leaders, the question in 2026 is no longer whether to run AI on-premises. It is which platform delivers genuine sovereignty without trading one dependency for another. This guide explains what a private AI appliance is, why organisations are bringing AI in-house, how on-premises deployment compares to the cloud, and what separates a sovereign appliance from a rebadged data-centre reference design.