Deepgram provides a suite of AI-powered audio APIs designed for developers and enterprises that need to process spoken language at scale. Its core offerings include speech-to-text transcription (supporting batch and real-time streaming), text-to-speech voice synthesis, and audio intelligence features such as summarization, sentiment analysis, topic detection, and language detection. The platform is built around low-latency, high-accuracy models trained on diverse audio data, and is accessible via REST and WebSocket APIs. Deepgram supports over 30 languages and offers multiple model tiers optimized for different accuracy and speed trade-offs. It is used across industries including contact centers, media, healthcare, and developer tooling. The platform also provides an Aura voice model for natural-sounding TTS output and a Nova model family for STT. Deepgram targets both individual developers through a self-serve API console and larger organizations through enterprise agreements with custom pricing and SLAs.
Target audience and deployment
- Solo / Freelancer
- Startup
- SMB
- Mid-market
- Enterprise
- Cloud
- On-premise
- API
Key features
Use cases
- Transcribe audio and video files at scale
- Stream real-time speech-to-text for live applications
- Generate natural-sounding speech from text
- Analyze call center conversations for insights
- Build voice AI agents and conversational interfaces
- Detect language and transcribe multilingual audio
Best for
- Developers who need to integrate accurate speech-to-text or text-to-speech into applications via API
- Contact center teams who need to analyze and transcribe customer call recordings at scale
- Product teams who need to add real-time voice capabilities to SaaS or communication platforms
- Enterprise architects who need a scalable, low-latency audio AI infrastructure with SLA guarantees
Integrations
Communication
Twilio, Zoom
Developer
Python SDK, Node.js SDK, Go SDK, .NET SDK, Rust SDK
AI models included
OpenAI