Rev AI: Speech-to-Text API with Human Backup | hokai.io
Rev AI's Reverb model transcribes at $0.003/min across 58+ languages, trained on 3M+ hours. Hybrid AI and human transcription gives 99% accuracy on request.
Rev AI is a speech-to-text API by Rev (Austin, TX, founded 2010) with $51.5M in funding. The Reverb model transcribes at $0.003/minute across 58+ languages, trained on 3 million human-transcribed hours. Teams can escalate audio to human transcriptionists at $1.99/minute for 99% accuracy. Key competitors: Deepgram, AssemblyAI, Google Cloud Speech-to-Text.
Pricing
Pay-As-You-Go: Reverb AI model from $0.003/min. Human transcription at $1.99/min for 99%+ accuracy. Enterprise: custom pricing with dedicated SLAs and HIPAA mode. No monthly minimums on Pay-As-You-Go.
Frequently Asked Questions
What is Rev AI and what does it do?
Rev AI is the developer API platform of Rev, an Austin-based transcription company founded in 2010 with $51.5M in funding. It provides speech-to-text APIs for both automated AI transcription and human transcription, covering 58+ languages for batch and real-time audio. The Reverb model, its core AI engine, was trained on over 3 million hours of human-transcribed audio. Teams use Rev AI to transcribe podcasts, legal depositions, media content, and call center audio at scale.
How much does Rev AI cost?
Rev AI uses pay-as-you-go pricing with no monthly minimum. The Reverb AI transcription model costs $0.003 per minute of audio. Human transcription (routed through the same API) costs $1.99 per minute for 99%+ accuracy by professional transcriptionists. Audio intelligence add-ons like sentiment analysis and topic extraction are billed separately. Enterprise plans include custom pricing, dedicated SLAs, and HIPAA-compliant processing.
What are the main features of Rev AI?
Rev AI's core feature is its Reverb speech model, which transcribes audio in batch (async) and real-time (streaming) modes across 58+ languages. Speaker diarization is included by default on all async requests, identifying up to 8 speakers without extra cost. Add-on modules include sentiment analysis, topic extraction, language identification, and a Forced Alignment API for word-level timestamps. The platform also offers access to human transcriptionists for 99%+ accuracy on difficult audio.
Is Rev AI free to use?
Rev AI does not have a traditional free tier for its API. It operates on a pay-as-you-go model where you pay only for the audio you process, with no monthly minimum. New accounts may receive trial credits; check rev.ai/pricing for current promotions. For testing, the Reverb open-source model can be self-hosted at no cost if you have the compute infrastructure.
What are the best alternatives to Rev AI?
The main alternatives are Deepgram, AssemblyAI, and Google Cloud Speech-to-Text. Deepgram is the better choice when you need sub-300ms real-time streaming latency for voice agents. AssemblyAI is stronger for multilingual audio intelligence features like sentiment analysis and content moderation across 99 languages. Google Cloud Speech-to-Text suits teams already inside GCP with committed cloud spend. OpenAI Whisper is a free self-hosted option for offline multilingual transcription.
Who is Rev AI best for?
Rev AI is best for English-language product teams needing affordable batch transcription: podcast producers, legal tech developers building deposition tools, and compliance teams requiring HIPAA-eligible processing. The hybrid AI-plus-human workflow makes it especially useful for teams where some audio requires near-perfect accuracy that automated models cannot reliably deliver. It is not suitable for global enterprises needing multilingual sentiment analysis or real-time voice agents requiring sub-300ms latency.
Does Rev AI have an API?
Yes, Rev AI is an API-first platform. It provides RESTful endpoints for asynchronous batch processing and WebSocket endpoints for real-time streaming. Official SDKs are available for Python, Node.js, and Java. Documentation is at docs.rev.ai. The API covers all features: speech-to-text, human transcription routing, sentiment analysis, topic extraction, language identification, and forced alignment.