Azure Speech (STT) vs OpenAI Whisper API
Speech-to-Text
| A Azure Speech (STT) | O OpenAI Whisper API | |
|---|---|---|
| Free tier | ✓ Free tier | Paid only |
| Pricing model | usage | usage |
| Price | $1 (Standard (1 hour)) | $0.006 (per minute) |
| Features | ||
| Languages | en, ja, zh, ko, fr, de | en, ja, zh, ko, fr, de, es |
| API | ✓ Available Docs ↗ | ✓ Available Docs ↗ |
| Homepage | Azure Speech (STT) ↗ | OpenAI Whisper API ↗ |
| Pricing Plans | Free$05 audio hours/mo free Standard$1/hrReal-time and batch Custom Speech$1.40/hr + training feeDomain-specific model fine-tuning | Pay-as-you-go$0.006/minFlat rate, all languages Open-source (self-host)$0Run Whisper model locally for free |
| Platforms | ||
| Integrations | Azure Bot Service, Power Platform, Teams, Dynamics 365, REST API / SDK | OpenAI Platform, Python SDK, Node.js SDK, REST API |
- Real-time and batch transcription with speaker diarization
- Custom Speech for domain-specific vocabulary fine-tuning
- 100+ language support—broadest among cloud STT providers
- Deep Azure ecosystem integration
- Custom model training adds complexity and cost
- SDK verbosity compared to Deepgram or AssemblyAI
- Latency slightly higher than Deepgram on real-time tasks
- Excellent multilingual accuracy across 99 languages
- Built-in translation to English from any supported language
- Very low cost at $0.006/min
- Open-source model available for self-hosting
- No real-time streaming—batch/file upload only via API
- No speaker diarization in the hosted API
- Rate limits can affect high-throughput workloads
AI Commentary
Azure Speech STT is the strongest enterprise STT offering for breadth of language support and compliance requirements. Custom Speech allows organizations to fine-tune models on proprietary vocabulary—critical for medical, legal, and technical domains. Real-time and batch modes are both well-supported. Its main competitive disadvantage versus Deepgram is slightly higher latency on streaming transcription tasks.
The hosted Whisper API offers the easiest path to OpenAI's speech recognition model without infrastructure management. Its multilingual accuracy—particularly on low-resource languages—is among the best available. The major drawback is the absence of real-time streaming, limiting it to asynchronous transcription workflows. Teams needing real-time streaming should run the open-source model on their own infrastructure or use Deepgram/Azure Speech instead.