Once you know AI should run locally rather than in the public cloud, the question is how to place the hardware. There are three routes: your own on-site server (full control, but your power, cooling and people), colocation (your hardware in someone else's data center), and a turnkey appliance (fastest start). This comparison breaks each down by control, cost, time and data location, and says when each wins. Without confusing it with the on-prem versus cloud choice.
Build your own AI or buy off-the-shelf is rarely settled by price. Five factors tip the scales: time to the first result, the real cost of upkeep, access to people, how unusual the problem is, and who carries the risk. This comparison breaks each one down, adds a matrix of four vendor types, and explains when the middle option is best: buy the core, build your edge. Without ideology and without hidden costs.
The choice between AI on your own hardware and AI in the public cloud is rarely settled by a single number. Five factors tip the scales: whether the data can leave the company, the volume and how constant it is, the full cost of on-prem ownership, latency and offline operation, and NIS2 and the supply chain. This comparison breaks each one down, shows a decision table, and explains when the hybrid option makes the most sense. Without ideology and without hidden costs.
