Running AI on your own hardware vs the cloud: the honest trade-off

For a few years "use AI" meant "send your prompt to someone else's server." That is no longer the only option: open models can run on your own machine, privately and at no per-use cost. Local AI is genuinely useful and genuinely limited, and the honest choice between it and the cloud comes down to privacy, capability and hardware — not ideology.

What local AI gives you

Privacy is the big one: nothing you type leaves your machine, which solves at a stroke the problem in what not to paste into AI — sensitive work, client data, anything confidential can be processed without it touching a third party. It also means no per-use cost, no rate limits, and no dependency on a service staying up or staying free. For repetitive or private tasks on data you cannot share, that is a decisive advantage.

What it costs you

Capability and convenience. The best local models you can run on a normal computer are typically behind the largest cloud models on hard reasoning, and quality scales sharply with hardware — a serious local setup wants a lot of memory and, ideally, a capable GPU. There is setup and fiddling, and the model is fixed rather than silently improving. So local trades raw capability and ease for privacy and control.

How to choose

Choose local when the data is sensitive, the task is well within a smaller model's ability, and you value privacy and zero marginal cost over peak capability. Choose the cloud when you need the strongest reasoning, the latest features, or you simply want it to work without managing hardware — accepting the data trade-offs, which you can reduce via the settings in data-training settings. Many people sensibly do both: local for private or routine work, cloud for the hard problems. It is the same local-versus-cloud reasoning as the smart-home version — decide what must stay in your house and what is worth sending out.