Speech-To-Text
Choosing between Deepgram and Gladia for your speech-to-text and audio intelligence needs often comes down to these five critical questions: How fast do you need results? How many languages do you support? What audio intelligence features matter most? How do you prefer to pay? What compliance certifications do you require?
Speech-To-Text
Deepgram has positioned itself as a comprehensive voice AI platform, offering everything from speech-to-text and text-to-speech to conversational AI capabilities. With its end-to-end, broad learning architecture and developer-focused approach, it has become a popular choice for enterprises building voice-enabled applications at scale.
Speech-To-Text
Deepgram has established itself as a major player in the speech-to-text space, offering developers and enterprises a fast, accurate transcription platform built on end-to-end deep learning. Its combination of real-time streaming, batch processing, and audio intelligence features makes it a go-to choice for companies building voice-enabled applications.
Deepgram has positioned itself as a comprehensive voice AI platform, offering everything from speech-to-text and text-to-speech to conversational AI capabilities. With its end-to-end, broad learning architecture and developer-focused approach, it has become a popular choice for enterprises building voice-enabled applications at scale.
Deepgram has positioned itself as a comprehensive voice AI platform, offering everything from speech-to-text and text-to-speech to conversational AI capabilities. With its end-to-end, broad learning architecture and developer-focused approach, it has become a popular choice for enterprises building voice-enabled applications at scale.
To create this Deepgram review, the platform has been analyzed extensively. Based on the findings, Deepgram is the ideal choice if:
However, Deepgram might not be the best choice if:
For teams with these priorities, Gladia represents a notably different approach in the speech AI space: a pure-play specialist focused exclusively on transcription and audio intelligence, supporting over 100 languages with native code-switching. This review includes a detailed comparison with Gladia at the end, as the contrast between Deepgram's generalist platform strategy and Gladia's specialist positioning helps illustrate the trade-offs teams face when choosing a speech AI provider.
If multilingual accuracy and a focused speech AI partner are already top priorities, Gladia's free tier is available here.
Table of contents:
Deepgram is a voice AI company founded in 2015 by Scott Stephenson, Adam Sypniewski, and Noah Shutty.
The founders, all former physicists from the University of Michigan, initially built wearable devices to record their daily conversations. The challenge of searching through hundreds of hours of audio led Stephenson to apply learning techniques he had developed for detecting dark matter signatures in waveform data.
This origin story shaped Deepgram's technical approach: rather than using traditional speech recognition methods, they built an end-to-end broad learning architecture that processes audio directly. The company has since raised over $100 million in funding, including investments from NVIDIA, Y Combinator, and Madrona.
Today, Deepgram positions itself as more than a transcription service.
The platform offers a unified voice AI stack that includes speech-to-text (STT), text-to-speech (TTS) through their Aura product, audio intelligence features, and a Voice Agent API for building conversational AI applications. This comprehensive approach targets developers and enterprises who want to build voice-enabled products without stitching together multiple services.
With a growing team, Deepgram serves customers ranging from startups to Fortune 100 companies, particularly in contact centers, media, healthcare, and finance.
Deepgram's core offering is its Speech-to-Text API, which supports both real-time streaming via WebSocket connections and batch processing via REST API.
The platform offers several model options: Nova-3 is their latest general-purpose model optimized for accuracy, while Flux is designed specifically for real-time conversational AI with lower latency.
The API accepts audio in various formats and returns transcripts with word-level timestamps and confidence scores. Key features include speaker diarization (identifying different speakers without specifying the number beforehand), smart formatting that adds punctuation and formats entities like dates and phone numbers, and the ability to boost recognition of specific keywords.
For batch processing, Deepgram can transcribe one hour of audio in approximately 30 seconds. Real-time transcription achieves latency under 300 milliseconds. The platform supports over 40 languages, including multilingual code-switching capabilities, with specialized models for healthcare terminology.
Deepgram's Text-to-Speech offering, branded as Aura, converts written text into spoken audio.
The Aura-2 model is designed for enterprise applications, featuring over 40 distinct voices with various personas and localized accents.
Source: Deepgram
The API supports both REST endpoints for single requests and WebSocket connections for streaming text-to-speech, which is particularly useful when generating audio from large language model outputs. Deepgram emphasizes sub-200ms time-to-first-byte latency, making Aura suitable for real-time conversational applications.
The voices are trained on conversational data rather than narrated scripts, which Deepgram claims produces more natural-sounding output for interactive scenarios. Language support now includes English, Dutch, French, German, Italian, Japanese, and Spanish, with continued expansion planned.
Beyond transcription, Deepgram provides audio intelligence features that analyze the content of audio files.
Source: Deepgram
These capabilities include:
These features are accessed through the same API as transcription, with results returned in structured JSON format. The models are designed to be task-specific rather than general-purpose, which Deepgram claims improves accuracy for specialized use cases.
The Voice Agent API represents Deepgram's move into the conversational AI space.
It provides a unified interface that combines speech-to-text, large language model orchestration, and text-to-speech into a single pipeline.
The API handles real-time conversational dynamics, including interruption detection (when users speak while the AI is talking) and turn-taking prediction. Developers can use Deepgram's integrated stack or bring their own LLMs and TTS models while still using Deepgram's orchestration layer.
Function calling capabilities allow the voice agent to interact with external systems, such as querying databases or triggering actions based on conversation context. The system maintains conversation history and supports real-time prompt updates.
While Deepgram offers a comprehensive voice AI platform, several considerations may affect certain use cases. These trade-offs reveal a platform optimized for full-stack voice AI development rather than specialized multilingual transcription.
Language Coverage and Accuracy: Deepgram supports over 40 languages, though with varying degrees of accuracy across its language portfolio.
Some users have reported accuracy issues with common European languages like French and Danish, prompting migrations to specialized multilingual providers. For organizations requiring broader linguistic coverage, some competitors support over 100 languages. If your use case involves many less common languages or frequent code-switching between languages, a specialized multilingual provider may be more suitable.
Platform Complexity for STT-Only Needs: The comprehensive nature of Deepgram's platform (spanning STT, TTS, audio intelligence, and voice agents) may be more than what some organizations need. For teams that simply require high-quality transcription without the voice agent and TTS capabilities, the platform's breadth can translate to unnecessary complexity.
Pricing Considerations at Scale: While Deepgram's base transcription rates are competitive, the modular pricing structure means costs can accumulate.
Deepgram uses a per-minute billing model.
For its recommended multilingual model, Nova-3 Multilingual is priced at $0.0092/min on Pay As You Go (approximately $0.55/hr). Features like speaker diarization ($0.0020/min), redaction ($0.0020/min), and keyterm prompting ($0.0013/min) are billed as add-ons on top of that base rate.
Audio Intelligence features (sentiment analysis, topic detection, summarization, and intent recognition) use an entirely different billing unit, priced per token rather than per minute, making direct per-hour comparisons with all-inclusive providers difficult.
Organizations with high volumes and multiple feature requirements should carefully calculate total costs, as a fully-featured Deepgram configuration can approach or exceed the rates of providers that bundle all features into a single price.
Additionally, Deepgram's listed rates require participation in their Model Improvement Program, and opting out means forfeiting the 50% discount.
Data Usage Considerations: Deepgram's default configuration includes participation in their Model Improvement Program, where audio data may be used to improve their models. Opting out is available, but comes at a cost: customers forfeit their 50% discount on listed rates. Some privacy-conscious organizations prefer providers where data exclusion is included in the standard pricing.
Generalist Expansion May Create Competitive Tension: As Deepgram expands into end-to-end voice AI solutions, including TTS and voice agents, companies building their own voice agent products may find their infrastructure provider increasingly competing in the same space.
These considerations aren't oversights; they reflect Deepgram's strategic focus on building an integrated voice AI platform with broad capabilities. However, they create opportunities for alternatives that prioritize focused speech AI, extensive multilingual support, and transparent pricing.
Gladia takes a different approach: a speech AI platform built from the ground up for multilingual excellence.
Founded in 2022 by Jean-Louis Quéguiner and Jonathan Soto, Gladia emerged from direct frustration with transcription services that couldn't accurately handle accents and multilingual conversations.
Unlike Deepgram's expansion into voice AI generalist territory, Gladia deliberately stays vertical in the speech recognition space. This "pure player" positioning means Gladia focuses exclusively on transcription and audio intelligence.
Solaria is Gladia's current primary model, purpose-built for critical real-time and async applications.
The model delivers industry-leading accuracy with a 94% Word Accuracy Rate across English, Spanish, French, and other common languages when tested on real-world, noisy audio.
What sets Solaria apart is its approach to latency.
While many providers report "under 300ms" latency, Gladia distinguishes between partial latency (time to first transcript) and final latency (time to complete, stable transcript). Solaria delivers partial transcription in under 120ms; approximately twice as fast as leading alternatives, enabling the kind of natural, uninterrupted dialogue that real-time voice applications demand.
The model was trained on over 1.5 million hours of diverse audio, including noisy and phone-quality recordings, to ensure performance in real-world conditions. This includes adaptive speech processing for loud call center environments and robust handling of background noise.
Gladia's standout capability is its support for over 100 languages and dialects, more than twice Deepgram's coverage.
The platform offers native code-switching, meaning it can transcribe conversations where speakers switch between languages naturally; a capability that's essential for international teams but often poorly supported by competitors.
This includes support for many underserved languages that other providers don't cover, such as Bengali, Punjabi, Tamil, Urdu, Persian, Hebrew, Pashto, and 42 others completely unsupported by alternatives. As a European-founded company, multilingual capability has been foundational to Gladia's identity from the start, not an afterthought.
Independent benchmarks on datasets like Google FLEURS and Mozilla Common Voice show Gladia's Solaria model outperforming Deepgram's Nova-3 across multiple languages, particularly in challenging conditions with accents and background noise.
Gladia emphasizes "precision"; accurately transcribing specific entities like email addresses, names, phone numbers, and industry-specific terminology. Internal benchmarks on noisy telephony audio show Gladia's precision is at least 17% more accurate than alternatives, including Deepgram.
The platform offers custom vocabulary with dynamic, per-user, per-language, and per-term weighting capabilities, enabling high precision in specialized domains like medical, financial, and legal transcription.
Named entity recognition (NER) automatically identifies people, organizations, locations, and dates, while hallucination reduction ensures key business data gets accurately transcribed without fabrication. This precision focus addresses a common pain point: generic transcription services that struggle with accents and spelled-out content like "firstname.lastname@gmail.com."
Gladia bundles audio intelligence features as part of the platform rather than charging separately for each capability:
Source: Gladia
Word-level timestamps are included by default, enabling precise subtitle generation and audio navigation. This all-inclusive approach means teams can move from basic transcription to full audio intelligence without additional procurement or surprise costs.
Gladia takes a distinct position on data privacy.
Gladia is headquartered in both Paris and New York City, and defaults to European cloud providers for hosting to respect GDPR constraints. Customers can request other geographies as needed.
The key differentiator is Gladia's approach to model training: for paid customers, audio data is not used to retrain models; and this protection is included in the pricing, not sold as an upsell. Free tier users should note that their data may be used for model improvement. This contrasts sharply with providers who charge customers a premium (or require forfeiting discounts) to opt out of data training.
Gladia holds SOC 2 Type 1 and Type 2 compliance, along with HIPAA compliance for healthcare applications. For Enterprise customers, enhanced data retention options, including zero-day data retention, provide maximum control over sensitive audio data.
Gladia provides clean APIs, language-agnostic SDKs, and well-maintained documentation designed to help developers move quickly from proof-of-concept to production.
The platform integrates with voice agent frameworks like Pipecat and LiveKit, as well as Recall and Meeting BaaS for AI-powered meeting assistants and note-takers, enabling seamless adoption for companies building real-time voice applications.
As a focused startup rather than a large platform provider, Gladia emphasizes personalized customer support.
Enterprise customers get dedicated Slack channels, forward-deployed engineers, and senior technical staff who collaborate directly with customers to help them deploy and innovate jointly; the kind of hands-on, tier-1 engagement that larger providers often can't offer.
Gladia has grown to serve over 300,000 developers and 2,000+ enterprise customers, including Attention, Circleback, Method Financial, and VEED.IO.
The choice between Deepgram and Gladia depends on your specific requirements, what you're building, and your strategic considerations for vendor relationships.
Choose Deepgram if you're developing a comprehensive real-time voice AI application that needs speech-to-text, text-to-speech, and conversational AI capabilities in one integrated platform.
It's particularly well-suited for teams building voice agents who want a single vendor across the entire voice stack, those who need the flexibility of custom model training for specialized domains, and organizations that value having STT, TTS, and LLM orchestration unified under one API.
The Voice Agent API makes it a strong choice for contact centers and customer service applications where you want everything from audio input to spoken response in one place.
Get started with Deepgram here.
Choose Gladia if your primary need is transcription and audio intelligence across a wide range of languages, especially if your conversations involve speakers who switch between languages naturally. It's the better fit for:
Gladia's focused approach delivers best-in-class speech AI without requiring you to adopt an entire voice platform or worry about your provider entering your market space.
Both platforms deliver accurate, low-latency transcription with robust audio intelligence features. The decision ultimately comes down to whether you need the breadth of a full voice AI suite or the depth and focus of a specialized speech AI partner, and whether you want a provider that might expand into your space or one committed to being infrastructure you can build on.
