Rev AI
Free tierThe world's most accurate speech-to-text API for developers, built for speed and global scale.
Free tier available·Technical·Powered by Rev AI (proprietary models trained on 7M+ hours of human-verified speech data)·API available
Key strengths
Industry-leading accuracy (lowest Word Error Rate)Least biased WER across ethnic background, nationality, gender, and accent57+ language supportDeveloper-friendly API with SDKsCloud and on-premises deploymentEnterprise-grade security (SOC II, HIPAA, GDPR, PCI compliant, 99.99% uptime)AI Insights (sentiment analysis, topic extraction, summarization, translation, language identification)Trained on 7M+ hours of human-verified speech data
Free tier + paid plans
San Francisco, USA
Founded 2010
Self-hostable
Facts verified against rev.ai on
Recent changes
- Plans updated: Pay-as-you-go with free credits (5 hours of Reverb ASR). Reverb Transcription: $0.20/hr (English); Reverb Foreign Language: $0.30/hr (57+ languages); Whisper Large: $0.005/min; Human Transcription: $1.99/min; Forced Alignment: $0.003/min; Language ID: $0.003/min; Translation: $0.002–$0.025/min; Sentiment Analysis: $0.0008/10 words; Summarization: $0.002–$0.025/min; Topic Extraction: $0.0008/10 words. Enterprise: volume-based pricing.
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Rev AI is well-suited for developers and engineering teams building:
- Real-time or async transcription pipelines using the Reverb ASR engine for English or 57+ foreign languages
- NLP enrichment workflows that chain transcription with sentiment analysis, topic extraction, and summarization via Rev AI's AI Insights endpoints — reducing reliance on separate NLP services
- Multilingual applications requiring language identification before routing audio to the appropriate transcription or translation endpoint
- Compliance-sensitive platforms (healthcare, finance, legal) leveraging HIPAA, SOC II, GDPR, and PCI compliance alongside on-premises deployment options
- Forced alignment tools for syncing transcripts to audio timestamps at $0.003/min
- High-accuracy benchmarking where lowest WER and least-biased transcription across ethnicity, nationality, gender, and accent is a product requirement
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