On-Device vs Cloud Transcription: What You Should Know
When you speak into a transcription app, where does your voice go? This is a question most people never ask — but it matters more than you might think.
Cloud Transcription: The Standard Approach
Most transcription services work by sending your audio to remote servers. Your voice is uploaded, processed by large models in data centers, and the text is sent back. This approach offers high accuracy and access to powerful models, but it comes with trade-offs.
Your audio travels across the internet. It may be stored on servers you don't control. In some cases, it may be used to train future models. For meetings with sensitive business decisions, medical conversations, or legal discussions, this creates real risk.
On-Device Transcription: A Different Model
On-device transcription processes your voice locally — on the hardware you own. The audio never leaves your phone, tablet, or laptop. On-device speech models can run efficiently on modern processors.
What dijin Does Differently
dijin uses on-device speech recognition. Your audio is processed locally and never uploaded to any server. Only the resulting text transcripts can optionally be synced — and even that is encrypted in transit.
Beyond privacy, on-device processing means dijin works offline. No Wi-Fi in the conference room? No problem. Recording in a basement? Still works. The transcription happens wherever you are.
Making Your Choice
| Factor | Cloud | On-Device (dijin) |
|---|---|---|
| Privacy | Audio sent to servers | Audio never leaves device |
| Offline | Requires internet | Works anywhere |
| Latency | Network round-trip | Instant local processing |
| Accuracy | High (large models) | High (on-device speech engine) |
| Storage | None local | ~1.5 GB model size |
| Sensitive use | Risk of exposure | Zero exposure |
The right approach depends on your needs. If you handle sensitive conversations — business meetings, medical notes, legal interviews — on-device transcription provides a level of protection that cloud services fundamentally cannot match.