Enterprise Voice AI platform designed for developers building voice-first products using speech-to-text, text-to-speech, or speech-to-speech APIs. Over 200,000 developers build with Deepgram's voice-native foundational models, accessed via APIs or self-managed software. Start building with $200 in free credits!
Just received an email about Nova-3. Looks really interesting. We have been playing around with real-time language switching on our phone calls. Looks like Nova-3 would support way more languages beyond just English and Spanish. Could be a game changer. Hoping to get beta access to the API soon.
Reviewers see Deepgram as a strong fit for real-time voice products, with repeated praise for fast streaming transcription, solid accuracy, reliable performance, and APIs that are easy to work with. Users say it helps voice assistants feel responsive, while a few also mention useful text-to-speech and speaker diarization. Feedback from the makers of Routine, Omi, and Shimmer ADHD reinforces the speed and transcript quality theme. Main gaps: clearer debugging when quality drops and some complaints about text-to-speech accuracy.
fast performance (13)easy integration (2)text-to-speech (5)multilingual support (4)easy-to-use API (4)real-time transcription (9)high accuracy (19)speech-to-text API (13)
Deepgram is a solid speech-to-text layer for voice AI products. I like it because transcription speed and accuracy directly affect the whole voice experience. If the transcript is slow or messy, the assistant feels broken even if the rest of the system is good.
Deepgram fits well for real-time voice workflows where you need fast transcripts, streaming, and decent reliability without building the speech layer yourself.
What needs improvement
Debugging could be clearer when transcription quality drops. In real calls, issues can come from noise, accents, mic quality, latency, or silence handling. More visibility into why a transcript was uncertain or delayed would help developers tune the experience faster.
I considered Deepgram mainly for real-time voice AI use cases. Whisper-style models are useful, but for interactive voice products, speed and streaming behavior matter a lot. Deepgram feels more suited for that kind of workflow.
If Toby mishears your answer, the feedback is worthless, and nothing breaks the illusion faster. Deepgram’s streaming transcription is fast enough that Toby responds as soon as you stop talking, and it held up across accents and nervous, half-finished sentences, which is how real candidates speak under pressure. Keyterm prompting also helps with company names and role-specific jargon. Plus the price per minute makes a £5 session viable.
We use Deepgram to transcribe live AI-driven training calls, where SDRs and sales reps practice conversations with an AI agent impersonating real prospects. The fast, accurate transcription is essential — it enables our coaching system to deliver instant feedback and correction right after each mock call. This real-time loop helps reps improve faster, and we’re thankful to the Deepgram team for enabling it.
What's great
fast performance (13)real-time transcription (9)high accuracy (19)