Local dictation processes speech on your device. Cloud dictation sends it to a remote service for processing. That difference affects connection needs, the path your audio takes, and how much work your own computer performs.

For AI dictation, ask about two steps: speech recognition and writing cleanup. An app can recognize your voice locally and still send the transcript to a cloud language model. If you need a completely offline workflow, both steps matter.

Follow the words through the whole process

A useful comparison starts with a simple sequence:

  1. The microphone captures your speech.
  2. Speech recognition turns the recording into text.
  3. An optional cleanup step edits that text.
  4. The result appears in the destination app.
  5. You save, sync, send or submit it.

Each step can have a different privacy boundary. The dictation app might keep the first three steps on your Mac while the document app syncs the result to a server. Calling the first part local does not describe the entire life of the document.

LoQuill's dictation workflow runs recognition and cleanup on the Mac. Initial activation and model downloads require internet, and updates and online license checks are separate network activities. See the product's privacy explanation for the details rather than treating local as a synonym for an app that never connects.

What local processing gives you

Once the necessary models are installed and ready, local processing can continue without reaching a remote recognition service. That is useful where connections are unavailable, unreliable or intentionally disabled.

It also keeps the dictation processing itself on the device. If avoiding an external audio-processing service is your priority, that is a clear architectural difference to look for.

The tradeoff is that your Mac has to run the models. Check the supported chip and operating system, download size, and real performance on the configuration you use. A tool that runs well on one computer is not evidence of the same experience on another.

What to ask about cloud processing

A cloud service can perform processing remotely, which changes the demands on the user's device. But the service still depends on connectivity, and its privacy answer depends on more than the word cloud.

Read the current documentation for retention, training use, deletion and settings. Sending audio for processing, retaining a recording, and using content to improve a model are different actions. A privacy setting that controls one may not control the others.

For example, Wispr Flow's official documentation describes cloud dictation processing and separate controls for model improvement and dictation cloud storage. Review those controls directly if you use Flow. It would be misleading to collapse them into a claim that all cloud tools handle every recording the same way.

Source: https://docs.wisprflow.ai/articles/3467817258-security-and-compliance-faq

Hybrid tools need a configuration check

Some tools offer both local and remote models. Superwhisper's model documentation, for example, lists on-device speech recognition and on-device language models alongside cloud options. Whether a particular workflow stays local depends on the models selected for both stages.

Source: https://superwhisper.com/models

That is why comparing product names alone is insufficient. Write down the actual configuration you intend to use and test it with the network disconnected. Do not assume an offline label on recognition also covers cleanup.

Choose around the work you actually do

If you need to write during travel or want dictation processing to stay on a supported Mac, start by testing a local workflow. If other requirements matter more, compare those directly instead of treating processing location as the only criterion.

Use the same realistic sample in each candidate: a short email, a project update and a sentence with names or numbers. Review the final text, the correction effort and whether the result arrives where you expect it.

Treat accuracy and speed as things to measure on your own tasks. There is no sound basis for saying local is always less accurate, cloud is always faster, or one processing model removes the need to review the text.

A short checklist before you decide

Can recognition work without internet? Can cleanup? What downloads are required? Which computers are supported? What is retained locally? What is sent to a server? What happens when I deliberately send feedback? Where does the finished text go? What will the license cost over the period I expect to use it?

Answering those questions gives you a more useful comparison than an offline badge or a subscription price by itself.

For LoQuill's current product details, see https://loquill.app/.