Deepgram

About Deepgram

Deepgram is a speech AI platform that provides developer-focused APIs for converting audio to text and text to audio. Its core offerings are automatic speech recognition (ASR) and text-to-speech (TTS), both accessible via REST and streaming APIs that integrate directly into applications, products, and workflows. The platform is designed to handle real-time transcription as well as pre-recorded audio files, making it usable across a broad range of deployment scenarios. The primary use cases include call center analytics, voice assistants, meeting transcription, podcast captioning, and any application that needs to process or generate spoken language at scale. Developers can access features such as speaker diarization, punctuation, language detection, custom vocabulary, and sentiment analysis depending on the plan and model selected. Deepgram also offers voice cloning and a selection of prebuilt AI voices through its Aura TTS product. The platform supports multiple languages and is built to prioritize low latency, which makes it particularly relevant for real-time applications like live captioning or conversational AI agents. Deepgram targets software developers, AI engineers, and product teams building voice-enabled applications rather than end users looking for a standalone transcription tool. It positions itself as infrastructure — a foundational layer that others build on top of — rather than a finished consumer product. Pricing operates on a pay-as-you-go model based on audio minutes processed or characters generated, with volume discounts available at higher usage tiers. A free tier with limited credits is available for developers who want to evaluate the API before committing to paid usage. Enterprise contracts with custom pricing, dedicated support, and on-premises deployment options are also available for organizations with stricter data handling or performance requirements. Overall, Deepgram suits teams that need reliable, scalable speech AI infrastructure without building the underlying models themselves.

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