The two ways to run AI
AI can run in two broad places: locally, on your own device, or in the cloud, on powerful servers you connect to over the internet. Both approaches can use capable models, but they differ sharply in privacy, speed, cost, and what hardware they need. Choosing between them is not about which is universally better, but about matching the approach to the specific workload and its requirements.
Privacy and offline access
The biggest advantage of local AI is privacy: your data stays on your device and is not sent to an external service, which matters for sensitive information. Local AI can also work offline, with no internet required. Cloud AI, by contrast, sends your data to a provider's servers, which may be fine for non-sensitive tasks but is a real consideration for confidential work, and it needs a connection to function.
Capability and hardware
Cloud AI typically offers the most capable models, since providers run enormous hardware you could not match at home. Local AI is limited by your own device's power, so local models are often smaller and less capable, and demanding ones require a strong computer. There is a genuine trade-off: the cloud gives you top capability on modest hardware, while local keeps data private but within your machine's limits.
Speed and cost
Speed depends on the situation. Local AI avoids network delay and can feel instant for suitable models, but a weak device will run large models slowly. Cloud AI leverages fast servers but adds network latency. On cost, cloud AI usually charges per use, which adds up with heavy usage, while local AI's cost is mostly the upfront hardware, with little per-use expense after that. Usage patterns determine which is cheaper.
A checklist for deciding
To decide, ask: Is the data sensitive or private? Lean local. Do you need the most capable model or lack strong hardware? Lean cloud. Do you need offline access? Local. Is usage heavy and ongoing? Local may be cheaper long term; light or occasional use may favor the cloud. Often the answer is a mix, keeping private, routine tasks local and sending demanding, non-sensitive work to the cloud.
The bottom line
Local AI keeps data private, works offline, and has low per-use cost, but is limited by your hardware. Cloud AI offers top capability on modest hardware but sends data out, needs a connection, and charges per use. Decide by weighing data sensitivity, capability needs, offline requirements, and usage volume, often mixing both. Understanding the trade-offs helps you place each AI workload where it belongs.
Related on Skillo
See also: What is on-device AI? Explained, How to run a small language model on your own computer.
Sources
Published date reflects the original event date (2026-10-23). This article is original Skillo editorial written from the sources above; facts were verified in September 2026.
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