Gladia vs Deepgram: Which Speech-to-Text API Handles Production Reality?

Transcript quality makes or breaks voice APIs. Deepgram and Gladia are both speech-to-text APIs that promise sub-second latency and painless integration.

Deepgram delivers sub-300 ms latency for real-time transcription with a scalable architecture that handles large numbers of concurrent requests.

Gladia claims around 270 ms latency with support for 100+ languages.

This comparison examines how each platform handles production reality: accuracy in messy, accent-rich audio, performance when traffic spikes to thousands of concurrent streams, and the cost predictability that finance teams track each quarter.

Deepgram's batch processing handles hour-long recordings in as little as 12 seconds, while its streaming API is among the fastest available, with consistent sub-300 ms transcript delivery. Custom model training maintains 90%+ accuracy, preventing domain-specific jargon from impacting transcription quality.

While Gladia claims 270 ms latency with 100+ language support, it provides limited performance data for noisy, multi-speaker environments where enterprise voice systems actually operate.

Enterprise deployments require more than fast transcription.

Deepgram provides flexible deployment options (cloud, single-tenant, private VPC, on-premises), SOC 2 Type 2 and HIPAA compliance with signed BAAs, and transparent pricing that scales without surprises. Large enterprises run production voice systems on Deepgram infrastructure because speech accuracy, regulatory compliance, and operational reliability are what determine whether voice AI succeeds or fails at scale.

The comparison table below shows how Deepgram and Gladia perform on the metrics that determine real-world outcomes.

The sections below examine how Deepgram and Gladia perform when code leaves the sandbox and hits production.

Background noise, overlapping speakers, and regional accents challenge even the most robust APIs. Deepgram’s Nova models minimize errors when domain-specific jargon appears, using runtime keyword prompts to push accuracy further.

Gladia's Solaria engine claims comparable baseline accuracy across 100+ languages, but published metrics focus on clean audio.

Noise and accented speech drive WER up for generic models, making training data representativeness and customization critical. Gladia hasn't released numbers showing how accuracy holds when multiple speakers overlap or background chatter spikes.

Speaker diarization identifies who said what in a conversation. Deepgram attaches speaker labels during live streams so analytics platforms can separate agents from customers without post-processing. This feature reduces manual QA time.

Gladia offers diarization as well, but doesn’t document its precision in noisy telephony. For conversation intelligence and clinical documentation, contexts where a misspelled drug name or mis-attributed sentence creates legal risk, Deepgram’s tailored models provide more control and fewer surprises.

Speed determines whether a voice AI agent feels human or robotic. Deepgram delivers transcripts in under 300 ms, fast enough to keep voice agents conversational, and batch-processes an hour of audio in just 12 seconds thanks to a 0.0033× real-time factor.

Gladia advertises sub-300 ms real-time latency for its STT services, with a 270 ms average response time for its Solaria model. The numbers look good, but documentation doesn't explain how throughput scales when applications suddenly need thousands of concurrent streams.

Infrastructure that processes 60 minutes of audio in 12 seconds is unlikely to choke on live traffic.

Budget predictability matters as much as accuracy. Deepgram bills by usage with volume discounts and bundles its Voice Agent API so teams don't get blindsided by downstream LLM charges. Deployment options like multi-tenant cloud, dedicated single-tenant stacks, private VPC, or entirely on-premises installations offer just the right balance of flexibility and security.

Gladia also offers cloud and on-premises options, and pricing starts at a free tier that includes basic STT functionality like batch and real-time transcription. All higher-tier features like larger file sizes and concurrency limits, additional language support, custom vocabulary are gated behind custom pricing.

For certifications, Deepgram brings SOC 2 Type 2 and HIPAA eligibility with signed BAAs, plus configurable data-retention defaults for privacy-sensitive workflows.

Gladia comes with HIPAA-compliant STT and claims SOC 2 Type 1 & 2. It also claims that the platform is pursuing ISO 27001.

Deepgram serves organizations building voice-enabled products and operating voice-heavy workflows at scale. Here are a few of the companies that can benefit from Deepgram’s voice AI capabilities:

Deepgram’s STT API is perfect for AI-native companies building conversation intelligence platforms, voice agents, and transcription products that need speech recognition that can scale with their customer base.

Deepgram processes audio at speeds over 40x real-time in batch mode and delivers strong accuracy on diverse audio. This headroom combined with straightforward, usage-based pricing means predictable spend, fewer pager alerts, and a roadmap that isn't constantly blocked by infrastructure rewrites.

Organizations running large-scale voice operations walk a fine line between compliance and customer experience. Speech recognition failures create regulatory risk when transcripts miss critical information, but slow processing frustrates customers waiting for responses. Deepgram's models can handle telephony audio with sub-300ms latency while maintaining the accuracy enterprises need for audit trails and quality assurance.

Beyond speed, accuracy matters just as much when separating speakers and capturing domain-specific language. Deepgram’s advanced speaker diarization automatically tags who said what in each conversation, eliminating manual review time for QA teams. This, coupled with keyword prompting that captures jargon, helps transcripts come out exactly as spoken.

Gladia fits teams building multilingual voice products where language coverage matters more than enterprise-grade infrastructure. Startups use Gladia when demonstrating multilingual voice capabilities quickly without custom model training, and media companies batch-process podcast archives through their straightforward pay-per-minute API. The approach makes sense when audio is public-facing and generic accuracy levels are acceptable.

Deepgram sets a high standard for enterprise speech-to-text API solutions, offering impressive accuracy in real-world conditions while maintaining low latency.

The platform’s scalability can handle thousands of simultaneous requests, while the transparent pricing model and flexible deployment options help businesses predict and manage costs effectively. This makes Deepgram the ideal choice for enterprise-scale operations.

Deepgram's infrastructure is built with B2B2B models in mind, providing the backbone for enterprises creating voice-enabled products.

Production deployments require more than impressive demos. They need consistent accuracy across diverse audio conditions, predictable latency under load, and transparent economics that scale with business growth. Deepgram delivers on all three fundamentals, backed by enterprise customers processing millions of voice interactions daily.

Ready to see the difference in your own operations? Sign up for a free Deepgram console account and get $200 in credits to test production-grade speech recognition on your actual audio.

Deepgram delivers 90%+ accuracy out-of-the-box on production audio. Custom training with domain-specific samples reduces error rates by another 20-30%, a gap generic engines can't close. Gladia advertises strong baseline results, but public benchmarks use clean data. Teams need to measure WER on actual calls, since accuracy drops in real-world noise for every provider.

Deepgram returns first partials in under 300ms while handling thousands of concurrent streams. Gladia's Solaria model quotes 270ms interruption latency, dropping below 100ms in WebSocket mode for certain workloads. This difference is generally imperceptible to users.

When streaming audio over WebSockets or HTTPS, you can swap endpoints and update authentication finishes within a sprint. Larger deployments need one to two weeks to validate custom vocabularies and regression-test analytics pipelines. Because there are no proprietary SDKs, your team can phase traffic gradually.

Deepgram is SOC 2 Type 2 certified, signs Business Associate Agreements for HIPAA workloads, and provides the audit trails healthcare and finance teams require. The platform encrypts data in transit and at rest as a standard, but on-premises or single-tenant cloud deployment keeps sensitive voice data within security perimeters.