For developers building voice AI products, Deepgram is a speech-recognition platform providing fast, accurate speech-to-text and text-to-speech APIs, powering voice infrastructure for companies like Five9 and Telnyx.
Presence & Market Position
#62 of 141
Traditional SaaS Going Headless
▲ 34 60-day move
Measures earned, engagement-weighted share of voice across the GTM voices panel. Methodology →
Deepgram is positioning itself as the full-stack layer for voice AI, extending beyond its original speech-to-text and text-to-speech APIs into voice-agent infrastructure and on-device deployment alongside the cloud product. The conversation around it runs across conference and expo appearances, announcements for its newest speech and voice-agent models, and integration news as other platforms add it as a selectable voice engine. Practitioner discussion covers configuration detail, like tuning interruption handling in a live voice agent, and comparison contexts where it sits alongside other speech-model providers. Hiring activity in newer markets also surfaces in the mix, pointing to active geographic expansion.
Nylas
Sendbird
AssemblyAI
Hightouch
MailerSendNASA, Five9, Wistia, Telnyx, Revenue.io, CallTrackingMetrics, Creditas, Jobcase
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Business Profile
Founded in San Francisco in 2015 by CEO Scott Stephenson, Deepgram built its business around speech-to-text and text-to-speech APIs for developers building voice products, with customers including NASA, Five9, Wistia, and Telnyx. The company raised a $130M Series C led by AVP in January 2026 at a $1.3B valuation, alongside an acquisition of voice-ordering startup OfOne, and reported crossing $100M in annual recurring revenue in August 2026. Independent and privately held with roughly 325 employees, Deepgram has since opened an Asia-Pacific headquarters in Singapore and named a new chief product officer as it extends beyond transcription into fuller conversational infrastructure.
Agent Readiness
Measures how easily your agents can build on it — API, MCP, CLI, SDK, docs depth. Methodology →
Deepgram has built out a full agent surface on top of its core speech APIs: an MCP server for connecting directly into agent clients like Claude Code and Cursor, a CLI, and a Voice Agent API for chaining speech-to-text, an LLM, and text-to-speech into one conversational loop. Agent Skills published on GitHub extend that further, giving builders reusable patterns for wiring voice into agentic workflows.
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