When choosing a speech-to-text solution, one of the first decisions you'll face is whether to use cloud-based APIs or run transcription locally on your own hardware. Each approach comes with distinct tradeoffs in privacy, cost, and accuracy. This guide is part of our framework for choosing the right speech-to-text tool, helping you understand which deployment model fits your needs.
What's the Difference?
Cloud-based speech-to-text sends your audio to remote servers (Google, Amazon, Microsoft, or specialized providers) where powerful machines process it and return the transcript. You pay per minute of audio processed.
Local speech-to-text runs entirely on your device or on-premise servers. Models like OpenAI's Whisper can be downloaded and executed without any internet connection. Your audio never leaves your hardware.
The choice between them isn't always obvious—it depends on what matters most for your specific use case.
Privacy: Where Does Your Audio Go?
Privacy is often the deciding factor for journalists, healthcare professionals, legal teams, and anyone handling sensitive recordings.
Cloud Privacy Considerations
When you use cloud transcription, your audio travels to external servers. While major providers offer encryption and security certifications, the fundamental reality is that your data leaves your control. For many use cases, this is perfectly acceptable. For others—confidential interviews, medical dictations, legal depositions—it's a non-starter.
Cloud providers typically retain audio temporarily for processing and may use anonymized data to improve their models. Always review the provider's data handling policies before uploading sensitive content.
Local Privacy Advantages
Local transcription keeps everything on your machine. As security experts note, this provides "operational sovereignty"—the ability to transcribe sensitive content without creating any external digital footprint.
This matters for:
- Journalists protecting sources
- Healthcare providers handling patient information
- Legal professionals managing privileged communications
- Researchers working with confidential interview data
Tools like Whisper.cpp, VoiceInk, and various desktop Whisper implementations process audio entirely offline. The National Press Foundation lists local transcription as a best practice when handling delicate audio.
Cost: Per-Minute Fees vs. Hardware Investment
Cloud Pricing Reality
Cloud speech-to-text pricing looks simple on the surface—typically $0.004 to $0.02 per minute. But the total cost often exceeds headline rates:
- Google Cloud Speech-to-Text: $0.016/min standard, $0.004/min batch
- Azure Speech Services: $0.006/min batch, $0.0167/min real-time
- AssemblyAI: Starting at $0.0025/min
- Deepgram: Starting at $0.0043/min
However, production deployments require additional infrastructure—storage, compute functions, message queues, and data transfer. These ecosystem costs can double or triple your effective per-minute rate.
At scale, costs add up quickly. Processing 10,000 hours of audio at $0.01/min equals $6,000. Add premium features like speaker diarization or custom vocabulary, and costs climb further.
Local Cost Structure
Local transcription flips the cost model. Instead of per-minute fees, you invest in:
- Hardware: A modern laptop can run Whisper, though a GPU significantly speeds processing
- One-time software: Apps like Whisper for Desktop ($29 one-time) or open-source alternatives
- Electricity and maintenance: Minimal for occasional use, meaningful at scale
For light use, cloud often wins on cost. For heavy transcription volumes—podcasters processing weekly episodes, researchers transcribing hundreds of interviews—local processing pays off quickly.
The breakeven point varies, but many users find that after 50-100 hours of transcription, local processing becomes more economical.
Accuracy: Has the Gap Closed?
Historically, cloud services offered superior accuracy because they could run massive models on powerful hardware. That gap has narrowed significantly.
Current Accuracy Landscape
Modern local models achieve competitive accuracy. In recent benchmarks, OpenAI Whisper performs comparably to premium cloud services, with word error rates (WER) in the 5-12% range depending on audio quality and content type.
Key findings:
- Whisper (local): Consistently ranks among the most accurate options, matching or exceeding many cloud APIs
- Cloud services: WER varies widely (3.8% to 45%) depending on provider, audio characteristics, and configuration
- The real variable: Audio quality matters more than cloud vs. local—clear audio transcribes well everywhere
Where Cloud Still Leads
Cloud services maintain advantages in specific scenarios:
- Real-time streaming: Low-latency live transcription requires optimized infrastructure
- Rare languages: Some cloud providers support more language variants
- Continuous improvement: Cloud models update automatically without user intervention
Where Local Excels
Local models shine when:
- Customization matters: You can fine-tune Whisper for specific vocabularies or accents
- Consistency is key: Your results won't change when a provider updates their model
- Offline operation: No internet dependency means transcription works anywhere
Making the Decision
Choose Cloud When:
- You need real-time transcription for live events
- Volume is low to moderate (under 50 hours/month)
- You want zero infrastructure management
- Privacy requirements allow external processing
- You need features like automatic language detection across many languages
Choose Local When:
- Privacy is paramount—sensitive interviews, confidential meetings
- High volume makes per-minute pricing expensive
- You work in environments without reliable internet
- You want full control over your transcription pipeline
- Consistent, reproducible results matter (research, legal)
Consider Hybrid Approaches
Many organizations use both: cloud for general transcription, local for sensitive content. A journalist might transcribe routine interviews via cloud APIs but switch to local Whisper for whistleblower conversations.
Getting Started
If you're evaluating options, start by honestly assessing your privacy requirements and expected volume. For most users, a straightforward cloud-based tool handles transcription without complexity. Scriby uses cloud processing to deliver accurate transcripts with speaker identification, and its pay-as-you-go model means you only pay for what you use—no subscriptions, no commitments.
For those who need local processing, tools like Whisper for Desktop or the open-source Whisper.cpp provide capable offline transcription. The setup requires more technical comfort, but the privacy benefits are absolute.
The speech-to-text landscape continues evolving, with local models improving rapidly. Whatever you choose today, the good news is that accurate transcription is more accessible than ever—whether your audio stays on your device or travels to the cloud.