On-Device Speech-to-Text vs Cloud APIs: Tradeoffs for Privacy and Performance

Where should speech recognition happen—on the user's device or in the cloud? This architectural decision affects privacy, latency, accuracy, cost, and offline capability. There's no universal right answer, but understanding the tradeoffs helps you make the right choice for your use case.

The fundamental tradeoff

Cloud STT: Send audio to a server, get text back

• Pro: More powerful models, better accuracy, no device requirements
• Con: Requires internet, latency, privacy concerns, ongoing costs

On-device STT: Process audio locally on phone/computer

• Pro: Works offline, lower latency, audio never leaves device
• Con: Limited by device power, smaller models, harder to update

When cloud makes sense

Accuracy is paramount

Cloud providers can run models that are orders of magnitude larger than what fits on a phone. For applications where transcription errors have real consequences—medical dictation, legal transcription, accessibility tools—this accuracy gap matters. See our STT API comparison for how providers stack up.

You need broad language support

On-device models for 100+ languages would be enormous. Cloud services can dynamically load the right model for each language without bloating app size.

Continuous improvement is important

Cloud models can be updated instantly. On-device models require app updates that users may not install promptly.

Device diversity is high

Supporting on-device STT across iOS, Android, Windows, Mac, Linux, and various hardware capabilities is complex. Cloud provides consistency.

When on-device makes sense

Privacy is critical

For sensitive applications—healthcare, legal, personal journaling—keeping audio on the device eliminates entire categories of privacy risk. You can't leak data you never collected. The NIST Privacy Framework provides useful guidance for thinking through data handling.

We cover voice data privacy in accessibility tech in more detail separately.

Offline use is required

Field workers, travelers, rural users, or anyone in spotty connectivity can't depend on cloud services.

Latency matters

Network round-trip adds 100-500ms minimum. For real-time voice control or live captions, on-device can feel significantly snappier. See our comparison of real-time vs. batch transcription for architectural considerations.

Cost at scale

Cloud STT charges per minute of audio. For high-volume applications, on-device eliminates this variable cost (though development cost increases).

Hybrid approaches

The choice isn't always binary:

On-device first, cloud fallback

Process locally when possible; fall back to cloud for languages or conditions where on-device struggles.

Tiered processing

Use on-device for immediate feedback, then refine with cloud processing for the final transcript.

Edge processing

For IoT or embedded devices, process at a local server rather than individual devices or distant clouds.

Technical considerations

Model size vs. accuracy

On-device models must fit in memory and run on available compute. Whisper Tiny (~75MB) vs. Whisper Large (~3GB) represents the spectrum. Accuracy—measured by Word Error Rate (WER)—correlates with size. See our WER explainer for what the metric means.

Power consumption

Running neural networks drains batteries. Continuous on-device STT can significantly impact mobile device battery life.

Cold start time

Large on-device models take time to load. Cloud services are already running and respond immediately.

Update mechanisms

How will you improve the model over time? Cloud updates are instant; on-device requires thought about model delivery and versioning.

Platform-specific options

iOS

• Apple Speech framework (on-device available)
• Generally good accuracy for supported languages
• Privacy-friendly default

Android

• Google Speech Recognition (cloud-first, on-device options)
• Varies by device manufacturer
• On-device support improving

Browser

• Web Speech API (implementation varies)
• Generally cloud-based
• Privacy depends on browser vendor

Desktop

• OpenAI Whisper (can run locally)
• Vosk, Coqui STT (open source, on-device)
• Cloud APIs via SDKs

Making the decision

Ask these questions:

1. What happens if audio is intercepted or leaked? (Privacy sensitivity)
2. What happens if the network is unavailable? (Offline requirement)
3. What accuracy level is acceptable? (Quality bar)
4. What devices must be supported? (Platform constraints)
5. What's the volume and budget? (Cost structure)

The answers usually point clearly toward cloud, on-device, or a hybrid approach.

For a structured approach to evaluating providers against your requirements, see our requirements template.