
I use Deepgram for both speech-to-text (STT) and text-to-speech (TTS) in AI applications and workflow automation. It provides accurate transcription across multiple languages and produces natural-sounding speech with low latency, making it suitable for real-time and batch processing. The biggest advantage has been its language support and output quality. The transcription accuracy is consistently high, including for accents and multilingual content, and the TTS voices sound clear and natural. The API is straightforward to integrate, the documentation is well organized, and the response times are fast. Overall, Deepgram has been a reliable solution for building voice-enabled applications and automating audio processing tasks. The initial setup is also very easy. Review collected by and hosted on G2.com.
Deepgram performs very well overall, though there are a few areas that could be improved. I would like to see broader language and dialect support, particularly for less commonly used languages and regional accents. The TTS voice selection could be expanded with more natural and expressive voices across additional languages. Pricing for high-volume usage could also be more competitive, especially for developers building large-scale AI applications. More built-in features for speaker diarization, custom pronunciation, and voice customisation would make the platform even more versatile. Review collected by and hosted on G2.com.
Thank you for the thoughtful review. We're glad the multilingual accuracy and the documentation have made it straightforward to build voice-enabled apps. We do have some good news about our TTS voices. We've released several new voices with Flux TTS this week that have expanded expressiveness controls. We hope you will give them a try in your projects!
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