Deepgram

Unternehmensdaten

Offizieller Name

Deepgram, Inc.

Einheitentyp

COMPANY

Gegründet

2015

Hauptsitz

United States

Unternehmensgröße

201–500

Marktrolle

B2B SaaS Provider

Offizielle Website

deepgram.com

Was Deepgram macht

Deepgram operates a proprietary voice AI platform delivered through APIs and enterprise deployments. It converts customer audio and text workloads into speech recognition, speech generation, voice-agent orchestration and structured conversational insights. The platform creates value by reducing the engineering work required to assemble separate speech, language-model and conversational-control components, while accommodating enterprise requirements for low latency, data control and self-hosted deployment.

Strategische Einordnung

KI-gestützte Einordnung aus der bestehenden Unternehmensrecherche; Interpretation und belegte Fakten sind zu unterscheiden.

Deepgram, Inc. is a private US B2B voice AI infrastructure company. It provides proprietary APIs and hosted software for speech-to-text, text-to-speech, real-time voice agents and conversational audio analysis. Developers, software platforms, contact centres, startups and enterprises use the platform to build speech-enabled applications and automated voice workflows across cloud, private-cloud, hybrid and self-hosted environments. The company generates revenue through metered API consumption, prepaid credits, annual Growth subscriptions and negotiated enterprise contracts. Pricing is tied to audio duration, text characters, conversation duration and analysis usage. Deepgram expanded its product scope through the acquisitions of Poised in 2024 and restaurant voice-automation platform OfOne in 2026.

Geschäftsmodell und Monetarisierung

Deepgram monetises primarily through usage-based API billing: speech-to-text is charged by audio duration, text-to-speech by input characters, Voice Agent API by conversation duration, and Audio Intelligence by analysis units or tokens. It also sells prepaid credits, annual Growth subscriptions starting at $4,000, and enterprise agreements with committed volumes, discounted usage, higher concurrency, support and private-cloud or self-hosted deployment terms. Embedded and partner arrangements use wholesale pricing and custom commercial agreements.

Metered speech-to-text, text-to-speech, Voice Agent API and Audio Intelligence usage

Pay-per-Use

Growth annual plans with committed credits

Software Subscription

Negotiated enterprise volume, support and deployment agreements

Software Subscription

Embedded and partner commercial agreements

Service Fee

Produkte und Fähigkeiten

Für diese Ansicht liegen keine Produkte mit zugeordneten Quellen vor.

Produkte und Marktkategorien

Zuletzt erfasste Signale

Datumsangaben beziehen sich auf die Quellenveröffentlichung. Ältere Einträge sind historischer Kontext, kein Beleg für ein neues Ereignis.

• Compare Speech-to-Text Per-Minute Pricing (EU 2026)

  2026-08-15dev.to

  Conversational AI · Erfasster Impact-Score: 2/5

  Practical guidance for EU startups on shortlisting and benchmarking external speech-to-text (STT) APIs for production use in 2026. The article recommends gating vendors by verified per-minute cost plus minimum billing unit, language and vocabulary quality, asynchronous workflows, EU data handling, and measured latency (p95). It emphasises modeling billing granularity clip-by-clip, running a representative benchmark corpus, and precommitting weights for cost/quality/latency scoring. Four STT candidates (OpenAI, Deepgram, AssemblyAI, Google Cloud) are named for the audio shortlist, while a separate post-processing model comparison may include Anthropic, Google (Gemini), OpenRouter, Together AI and others. The piece includes an executable TypeScript benchmark that models billed seconds, estimated cost, quality, p95 latency, and eligibility gates.

  • The article recommends shortlisting STT vendors by verified per-minute rate, minimum billing unit, supported languages/quality, asynchronous workflow, and EU data handling.
  • OpenAI, Deepgram, AssemblyAI, and Google Cloud are named as example external STT candidates to verify and benchmark.

• 10-Day Voice Agent Build for Local Indian Store

  2026-08-15dev.to

  Conversational AI & Chatbots · Erfasster Impact-Score: 2/5

  A developer built a production-oriented voice assistant for a local Indian grocery/general store as part of a 10-day challenge (VoiceForBharat). The project implements a real-time voice pipeline combining Deepgram speech-to-text, Google Gemini LLM, Murf Falcon text-to-speech, and LiveKit for audio transport. Features included multilingual (English/Hindi/Hinglish) handling, caller memory (SQLite), safety guardrails, specialist handoffs for returns/refunds, outbound SIP calling via LiveKit, and call analytics exposed through a FastAPI backend. The complete source code is published on GitHub (Codehunter0009/murf-livekit-starter).

  • The author built a voice assistant for a local Indian grocery/general store during a 10-day challenge called 10 Days of Voice Agents — VoiceForBharat Edition.
  • The voice pipeline uses Deepgram for speech-to-text, Google Gemini as the LLM, Murf Falcon for text-to-speech, and LiveKit for real-time audio transport.

• Validate Speech-to-Text by Cost Per Accepted Invoice

  2026-08-15dev.to

  Infrastructure · Erfasster Impact-Score: 2/5

  Practical guidance for EU startups selecting a speech-to-text API: instead of comparing headline per-minute prices, measure each candidate against a fixed, versioned corpus of real invoice-readings and compute cost per accepted invoice. Implement a small portable adapter interface, keep extraction and validation downstream, enforce strict EU data-handling gates, and record billed usage in provider-native units. Candidates like OpenAI, Deepgram, AssemblyAI and Google Cloud can be evaluated this way; contract terms, billing units, and an acceptance gate determine the winner for a given workload.

  • Evaluate speech-to-text providers by cost per accepted invoice using a fixed, versioned test corpus rather than headline per-minute pricing.
  • Recommended candidates to include in the evaluation: OpenAI, Deepgram, AssemblyAI, and Google Cloud.

• Developer Builds Multilingual AI Voice Agent 'Sadie'

  2026-08-15dev.to

  Conversational AI & Chatbots · Erfasster Impact-Score: 2/5

  A developer, Ayush Shinde, built Sadie — a multilingual AI voice agent — during the "10 Days of AI Voice Agents — #VoiceForBharat Edition" challenge. Over ten days he implemented real-time voice conversations, Hindi-English code-mixing support, persistent (permissioned) memory, tool integration, outbound phone calling via SIP, human escalation via Discord webhooks, an analytics dashboard, and multi-agent specialist handoffs (grammar, maths, full-stack). The system integrates LiveKit for real-time transport, Deepgram for STT, Google Gemini as the LLM brain, and Murf Falcon for TTS, with SQLite for memory and Next.js/Chart.js for analytics. The post documents architecture, implementation details, encountered bugs (telephony SIP encryption, logging issues, import paths), and instructions to run the public repository and demo.

  • Ayush Shinde built an AI voice agent named Sadie during the "10 Days of AI Voice Agents — #VoiceForBharat Edition" challenge.
  • Sadie supports real-time voice, Hindi-English code-mixed conversations, permissioned persistent memory (SQLite), external tool integration, outbound phone calls, human escalation, analytics, and specialist agent handoffs.

• Voice AI Assistant for Indian Farmers Built in 10 Days

  2026-08-15dev.to

  Conversational AI & Chatbots · Erfasster Impact-Score: 2/5

  A developer built "Farm & Field," a voice AI assistant for Indian farmers during the 10 Days of Voice Agents — VoiceForBharat Edition by Murf AI. The system integrates Speech-to-Text, an LLM, Text-to-Speech, and real-time transport (Deepgram, Google Gemini, Murf Falcon, LiveKit) with SQLite-backed memory, weather and market-price tools, human escalation, a specialist crop agent, outbound calling via LiveKit SIP, and call analytics. The agent supports Malayalam, English, and Manglish, requests consent before storing memory, and hands off to humans or specialists when appropriate. The project code is available on GitHub.

  • Author built Farm & Field during the 10 Days of Voice Agents — VoiceForBharat Edition by Murf AI.
  • Core stack: Deepgram (STT), Google (Gemini) for LLM, Murf Falcon (TTS), and LiveKit (real-time transport).

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