In short. With local transcription the audio is processed on your computer and never leaves it: you have more control over data, no per-minute cost, but you need a powerful enough computer and some disk space for models. With cloud transcription the audio is sent to a server: it is convenient, quick to set up and works from any device, but you must trust (and verify) the provider on privacy, retention and costs. There is no absolutely better choice: it depends on how sensitive the meetings are and on your hardware.
What "local" and "cloud" mean
Local: a speech-recognition model runs on your device. The audio does not leave the computer to be transcribed. That does not automatically mean nothing leaves: the software can do other things on the network (updates, sync, AI features), so the settings must be read.
Cloud: the audio, or parts of it, is sent to a remote service that returns the text. The provider can be the app's own or a third party the app relies on.
There is a middle way, the hybrid approach: transcribe locally and use the cloud only as a fallback, for example if the local model is unavailable. In these cases the question to ask is when, exactly, the cloud gets used.
Privacy
Local
- Audio stays on your device: the risk surface is your computer's (access, disk encryption, backups).
- It is simpler to answer "where did the data end up?".
- For those handling confidential information (clients, HR, disputes) it is often a starting requirement.
Cloud
- You need to understand in which country the audio is processed, who sees it, how long it stays, and whether it is used to train models.
- A provider processing data on your behalf generally requires a data-processing agreement (DPA) and, outside the European Union, transfer safeguards.
- Good providers declare this clearly. If you cannot find answers, take that as an answer.
On the general regulatory picture, and on the need to inform participants, read recording a meeting and the GDPR. This is general information: for your case, consult a professional.
Costs
Local
Usually no per-minute cost: you pay (once) for the computer you already have and some disk space. The hidden cost is setup time and, on older computers, slowness. If the tool is a paid app, the price concerns the app, not the minutes.
Cloud
The typical model is subscription or pay-as-you-go, with an hours cap. Upside: predictable costs for small volumes. Downside: if meetings are many, the bill grows. Check what happens when hours run out (block, surcharge) and how much exporting data costs.
Quality
Slogans must be avoided here. Quality depends on three factors:
- Audio (microphone, separate tracks, noise): weighs more than everything else.
- The model: cloud services can use very large models and update them continuously. Locally, lighter versions of open models are often used (for example compressed Whisper variants), sometimes with a more accurate final pass. The gap, in Italian, depends on the chosen model and audio type, and must be measured on your own recordings.
- Tuning: glossary, pinned language, echo reduction, speaker separation.
To decide, run a comparative test: the same ten-minute recording, transcribed with both approaches, counting mistakes on names, numbers and technical terms. Twenty minutes, worth more than any comparison table. More advice in the guide on automatic transcription in Italian.
Speed and convenience
- Cloud: fast results even from modest devices, nothing to install, often collaboration features. Requires connection.
- Local: speed depends on the computer; a recent Apple Silicon Mac or a PC with a recent graphics card can transcribe in reasonable times, but that is not true of every device. Works offline, for example on a train or in a room with no network.
Comparison table
| Local | Cloud | |
|---|---|---|
| Where the audio goes | Stays on the device | Goes to an external server |
| Recurring cost | None per minute | Often per hour or per seat |
| Quality | Good with clean audio; model-dependent | Often bigger models |
| Requirements | Adequate computer, space for models | Connection |
| Offline | Yes | No |
| Collaboration | To build | Often included |
How to choose
Answer these questions in order:
- Do the meetings contain confidential or sensitive data? If yes, start from local, or from a cloud with documented contractual safeguards.
- Do you have a recent computer? If not, cloud may be the only workable option.
- How many hours do you transcribe per month? Few: cloud costs little. Many: local wins.
- Must several people work on the same texts? You need a shared part; assess whether optional sync suffices.
- Do you often work offline? Local.
Many people end up with a mix: local for confidential meetings, cloud for the rest.
Mind one detail: transcription is not everything
Even if audio is transcribed locally, what happens afterwards may use external services: an assistant that summarises, a sync, a networked language model. For each feature ask yourself: does this step send text outside the computer? Does it have a switch? A good tool answers these questions in settings, not only in the privacy policy.
Frequently asked questions
Is local transcription always more private?
For audio, usually yes: it does not travel. But the whole workflow counts: syncs, summaries, backups can take data outside the device.
Is cloud transcription less accurate?
Not necessarily: it can use bigger models. The real difference depends on your audio, so try with your own recording.
Do I need a powerful computer for local transcription?
You need a recent computer. On Apple Silicon Macs or PCs with a recent graphics card it is feasible in reasonable times; on older hardware it can be slow.
Can I use both?
Yes, and it is a common choice: local by default, cloud when the meeting is not confidential or the computer is not enough.
What to ask a cloud provider before using it?
Where the servers are, how long it keeps audio and texts, whether it uses them to train models, under which agreement it processes data, and how to delete everything. For a serious assessment involve whoever handles privacy in your organisation.