The main difference between local artificial intelligence and cloud-based AI is where the processing takes place. Local systems execute the necessary operations on the user’s own device, while cloud services send requests over the internet to external data centers and return the results.
Local AI, also known as on-device AI, can work on computers, tablets and smartphones. When a tool operates entirely on the device, tasks such as reviewing text or summarizing documents can be performed without sending files to external servers. This can offer greater privacy and, in some cases, allow the system to work offline.
However, local performance depends on the device’s hardware. Larger models may require substantial RAM and a more capable computer, while smaller models can run on simpler personal devices. Local systems may also have limitations in reasoning and response quality, depending on the task.
Cloud-based AI relies on external data centers. Services such as ChatGPT, Gemini and Claude process requests on the providers’ servers, allowing users to access advanced models without owning a device with powerful hardware. These services can also provide features such as Gemini’s Deep Think, Claude’s Artifacts and ChatGPT’s in-depth research.
The trade-off is that information sent to cloud chatbots is recorded on their servers and retained for a period that varies by service. Users should therefore avoid sharing personal, sensitive or confidential information. Cloud services commonly offer free access with daily limits, while paid plans can increase those limits.
The best option depends on the task, the available hardware and the desired level of privacy. Local AI is more suitable for simple tasks and confidential information when the software processes everything on the device. Cloud AI is better suited to advanced models, multistep planning, programming, large-scale analysis and research that requires an active internet connection, such as queries involving news or financial data.
The two approaches can also work together. Some tools process simple requests locally and send more demanding tasks to external servers. Apple Intelligence, for example, handles tasks on the iPhone, iPad or Mac whenever possible and uses Private Cloud Compute when more advanced models are needed. According to Apple, data sent to that system is processed to fulfill the request and is not stored on its servers.
Google also offers local models such as Gemini Nano, which can run on Android phones to summarize texts, revise messages and describe images without sending the data to the cloud. The Gemini app, meanwhile, uses online and local models for questions and activities that depend on Google services.
Samsung similarly divides Galaxy AI features between the device and external servers. Translations and some text suggestions can run locally, while content summaries and image editing may depend on cloud processing.
Sources: Canaltech.