On-device transcription processes your audio using AI models running locally on your computer. Your audio never leaves the device. Cloud transcription sends your audio to remote servers operated by a third party — typically achieving higher accuracy in exchange for that data transfer. The choice is fundamentally a privacy-versus-accuracy trade-off, though the gap is narrowing rapidly as on-device AI hardware improves.
What is on-device transcription?
On-device transcription runs a speech-to-text model directly on your CPU or Neural Processing Unit (NPU). The most common on-device model is OpenAI Whisper, available in sizes from Tiny (fastest, lower accuracy) to Large (slower, higher accuracy). Apple Silicon Macs (M1 through M4) run on-device AI efficiently via their unified memory architecture and dedicated Neural Engine. Key characteristics: audio never touches a network; works without internet; slightly lower accuracy than cloud at equivalent hardware cost; no recurring API cost per minute.
What is cloud transcription?
Cloud transcription streams your audio to servers operated by providers like Deepgram, AssemblyAI, or Rev. The server runs a large, continuously updated model on powerful hardware and returns the transcript — typically within 2–5 seconds. Key characteristics: 98–99% accuracy from production-grade models; real-time or near-real-time output; requires internet connectivity; audio is processed by a third party and may be retained per their privacy policy.
Privacy comparison
| Criterion | On-Device | Cloud |
|---|---|---|
| Where audio is processed | Your device | Third-party servers |
| Who can access recordings | Only you | You + the provider |
| Data after you delete | Gone from your device | Depends on provider policy |
| GDPR compliance | Easier — no data transfer | Requires DPA with provider |
| Breach risk | None — data not transmitted | Provider infrastructure risk |
| Offline capability | Yes | No |
Accuracy comparison
State-of-the-art cloud models (Deepgram Nova-2) achieve 98–99% word accuracy on clean English audio. On-device Whisper Large achieves 94–97% on the same audio. The gap is smaller on Apple Silicon Macs: the M-series Neural Engine runs Whisper Large at near-real-time speeds with ~96% accuracy — indistinguishable in practice for most meetings with clear audio. The accuracy gap is most noticeable with heavy accents, overlapping speakers, or dense technical terminology.
When to choose each
- ·Meetings contain confidential client, medical, or financial data
- ·You operate in a regulated industry (HIPAA, GDPR, SOC 2)
- ·Recording in locations without reliable internet
- ·Strong personal privacy preference regardless of regulation
- ·Accuracy is paramount and you need 98%+ with non-native speakers
- ·Real-time transcription is required
- ·Meeting content is not sensitive
- ·Fastest turnaround matters
How Wisprnote AI handles this
Wisprnote AI currently uses Deepgram for transcription (98%+ accuracy, SOC 2 certified, GDPR-compliant) but stores the resulting transcript and recording locally on your Mac — not in the cloud. Cloud sync is opt-in and end-to-end encrypted. Wisprnote never uses your transcripts or recordings to train AI models. On-device transcription via Whisper is on the roadmap for users who require zero data egress.
Frequently asked questions
Not yet, though the gap is closing. Leading cloud transcription (Deepgram Nova-2) achieves 98–99% accuracy. On-device Whisper Large achieves 94–97%. On Apple Silicon Macs, on-device transcription is fast enough for most workflows, but cloud remains more accurate for accented speech and technical terminology.
Most services send the audio file or audio stream to their servers. Depending on the service, this may include your audio, speaker identities, and timestamps. Some services retain a copy of the audio for a period defined in their privacy policy. Always check the data retention section before recording sensitive meetings with a cloud-based tool.
Yes — it depends on how the tool handles post-processing. Wisprnote AI uses cloud transcription (Deepgram) but stores the resulting transcript locally on your Mac. Check that your tool uses a GDPR-compliant provider, has a clear data retention policy, and does not use your audio to train models.
No. Recordings are stored locally on your Mac by default. Deepgram processes the audio and returns the transcript, but the audio file is not uploaded to or retained by Wisprnote's servers. Transcript cloud sync is available but opt-in.
No regulation explicitly requires on-device transcription, but HIPAA, GDPR, and financial services regulations constrain who can process sensitive data and how. On-device transcription is the simplest path to compliance because data never leaves your control. Cloud transcription can also be compliant if the provider holds appropriate certifications.