Two dominant options for production speech-to-text. Different sweet spots.
Whisper for offline / batch and multi-language accuracy. Deepgram for real-time streaming, sub-300ms latency and voice agent workloads.
Whisper (from OpenAI) is state-of-the-art on multi-language batch transcription with strong Indic and code-mixed support. Streaming exists but is not its strongest surface.
Deepgram Nova-3 is purpose-built for streaming: sub-300ms latency, robust barge-in, and strong accuracy on English and top global languages.
For voice agents, Deepgram; for batch multi-language transcription, Whisper. Many stacks use both.
OpenAI's open-source (and API) speech-to-text model with strong multi-language coverage.
Managed speech-to-text optimised for real-time streaming with strong latency and diarisation.
| Criterion | Whisper | Deepgram |
|---|---|---|
| Streaming latency | ~600ms typical | <300ms typical |
| Multi-language depth | 100+ languages, strong Indic | 50+ languages, English-strong |
| Diarisation | Add-on step | Built-in |
| Self-host | Yes (open-source) | No |
| Cost per hour | Cheap (API + self-host) | $$ |
| Voice agent fit | OK for hybrid | Excellent |
Voice agents often use Deepgram for real-time streaming and Whisper for post-call analytics on the recording — best-of-both without one compromise.
Both strong. Google leads on some regional accents; Azure integrates cleanly with Azure OpenAI. Both are viable alternatives to Deepgram.
Yes with chunking + VAD, but latency and turn-taking are harder to tune than Deepgram out of the box.
Task-dependent. Whisper often wins on batch multi-language; Deepgram often wins on English streaming with noise and diarisation. Benchmark on your audio.
Insights, use cases and industries that put this decision into context.
How we cut a customer's monthly LLM bill 84% without touching accuracy — routing, distillation, caching and the boring engineering behind every dollar.
Every failed AI initiative we've audited failed on data — not models. The five data foundations we insist on before scoping a single copilot.
AI engineering is the discipline of turning models, data and tools into reliable business systems. Here's what it actually covers, how it differs from traditional software engineering, and where the ROI shows up.
Turn every call into structured decisions, actions and CRM updates.
Deflect 60%+ of tier-1 tickets without hurting CSAT.
AI-powered learning platforms
AI-powered healthcare software
Talk to a senior AI consultant from T7 about your industry, workflow, or product idea. Free, no commitment — reply within one business day.