API Comparison Table

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Migrating from Azure Speech to Gladia: a step-by-step switching guide

TL;DR: Migrating from Azure Speech to Gladia removes the overhead of custom training pipelines and fragmented per-feature billing. Azure routes diarization, translation, and sentiment through separate services with separate billing meters. We bundle all audio intelligence into one per-hour rate on Starter and Growth plans. Solaria-3 ranks #1 for real-world European business audio, Solaria-1 covers 100+ languages with native code-switching. Both deliver out-of-the-box accuracy that eliminates custom training for most production audio. Most engineering teams complete the API refactoring in under 24 hours.

Speech-To-Text

How to evaluate a speech-to-text API: a technical buyer's framework

TL;DR: Choosing an STT API on vendor benchmarks alone is how teams end up with transcription that looks fine in staging and breaks on production audio. A rigorous evaluation requires building a test set from your own calls, measuring word error rate (WER) on your specific audio distribution, stress-testing latency under concurrent load, and auditing data retraining terms before signing. This guide gives you a reusable engineering blueprint to run that evaluation end-to-end, the same methodology behind our own open async benchmark, which covers 7 datasets and 74+ hours of audio across 8 providers.

Speech-To-Text

The contact center QA scorecard: what to measure and how transcription feeds it

TL;DR: Manual QA teams review as little as 1% to 2% of contact center calls, leaving the vast majority of interactions unreviewed and exposing systemic compliance risks that sampling never surfaces. Scaling to automated coverage requires transcription accurate enough to power LLM-based scoring without silent failures. If your speech-to-text engine misattributes a speaker or drops a compliance disclosure, every downstream scorecard, CRM entry, and coaching flag is wrong. French CCaaS platform Gravite cut per-call review time from 15 minutes to 1 minute (93% reduction) while automating coverage across their full 50,000 hours of annual call volume on infrastructure built for real-world contact center audio.

Ebook: Ultimate guide to using LLMs with speech recognition

Published on Jan 7, 2025

Large Language Models (LLMs) have enabled businesses to build advanced AI-driven features, but navigating the many available models and optimization techniques isn't always easy.

If you’re looking to combine speech recognition (STT) and LLMs for cutting-edge voice apps, look no further! Our ultimate guide is finally here, and it’s filled with valuable strategies and hands-on insights from our work with hundreds of audio-first companies and extensive interviews with experts in AI note-taking, sales enablement and customer support.

What you'll learn: