API Comparison Table

Heading 1

Heading 2

Heading 3

Heading 4

Heading 5
Heading 6

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.

Block quote

Ordered list

  1. Item 1
  2. Item 2
  3. Item 3

Unordered list

Text link

Bold text

Emphasis

Superscript

Subscript

Pricing
Get started
Get started

Read more

Speech-To-Text

Diarization error rate (DER) explained

TL;DR: Your transcription model might achieve a 5% Word Error Rate, but your meeting summaries can still be completely unreliable if Diarization Error Rate (DER) spikes. DER is the metric that determines whether your system correctly identifies who spoke each word, measured as the sum of three error types: Missed Speech, False Alarm, and Speaker Confusion. For production multi-speaker pipelines, a DER below 15% is the threshold for reliable speaker-labeled analytics; below 10% is the target for clean audio with controlled conditions, such as high-quality meeting assistant output. Accurate speaker attribution directly determines the reliability of downstream LLM summaries and CRM data. Our async pipeline, powered by pyannoteAI's Precision-2 model, delivers up to 3x lower DER than alternatives on conversational speech.

Speech-To-Text

Latency benchmarks for streaming speech-to-text (TTLB and P99)

TL;DR: A voice agent with a 150ms average STT latency sounds fast in a slide deck, but if its P99 spikes to 1.2 seconds, one in every hundred conversational turns breaks. This piece maps the full end-to-end streaming latency budget (network Round Trip Time (RTT), audio buffering, model inference, Voice Activity Detection (VAD) endpointing), explains why P99 and TTLB are the metrics that matter most for production user experience, and shows how to build a reproducible test harness. We cover how our Solaria-1 model delivers first partials under 103ms and final transcripts around 300ms, backed by an open, reproducible benchmark methodology.

Speech-To-Text

Multi-tenant, white-label speech-to-text for platforms

TL;DR: Building a compliant multi-tenant STT layer requires strict data isolation at the key level, granular cost attribution per tenant, and contractual infrastructure guarantees that flow through to your own SLAs. Self-hosting open-source models introduces DevOps overhead, scaling unpredictability, and the absence of built-in tenant isolation features that a managed API provides by default. Managed infrastructure with per-client keys, certified data handling, and all-inclusive pricing removes most of that build cost ($0.20–$0.61/hr with diarization, translation, and entity recognition included, compared with $240K–$480K/yr in dedicated engineering to self-host) but the isolation and attribution architecture still has to be designed correctly regardless of which vendor provides it.

Gladia selected to participate in the 2024 AWS Generative AI Accelerator

Published on Sep 18, 2024
Gladia selected to participate in the 2024 AWS Generative AI Accelerator

We’re proud to announce that Gladia has been selected for the second cohort of the AWS Generative AI Accelerator, a global program offering top early-stage startups that are using generative AI to solve complex challenges, learn go-to-market strategies, and access to mentorship and AWS credits.

This opportunity will help Gladia build, train, test, and launch products such as agent assistance for contact center platforms, sales enablement tools and AI meeting assistants, and enable voice-first platforms to deliver more value to their users across borders.

“The new generation of startups is at the forefront of a transformative new wave, pushing the boundaries of what’s possible with artificial intelligence while bringing exciting new solutions to market,” said Jon Jones, Vice President of Go-to-Market at AWS and executive sponsor of the program.
“Expanding the cohort for our Generative AI Accelerator is a testament to the potential we see for startups to usher in new innovations for customers in an increasingly AI-driven world. AWS is committed to fostering groundbreaking technologies and supporting visionary founders on their journey to solve the world’s biggest challenges.”

Gladia is one of 80 global startups from around the world selected for the program, and we’ll attend and showcase our solutions to potential investors, customers, partners, and AWS leaders in December at re:Invent 2024 in Las Vegas.

For more information on the Generative AI Accelerator, visit AWS Generative AI Accelerator.

About Gladia

Gladia provides a speech-to-text and audio intelligence API for building virtual meeting and note-taking apps, call center platforms, and media products, providing transcription, translation, and insights powered by best-in-class ASR, LLMs, and GenAI models.

Follow us on X and LinkedIn.

Contact us

280
Your request has been registered
A problem occurred while submitting the form.

Read more