API Comparison Table

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Microsoft Teams transcription via API

TL;DR: Native Microsoft Teams transcription via the Graph API delivers transcripts only after a meeting ends, provides utterance-level (not word-level) timestamps, and degrades sharply on accented or multilingual speech. For any product that routes Teams audio to downstream AI systems, the more reliable architectural pattern is capturing raw audio via a custom WebRTC bot and routing it to a managed STT engine, one that delivers word-level timestamps, accurate multilingual handling, and predictable per-hour costs, none of which the native Graph API provides. Solaria-1 covers real-time streaming and broad language support. Solaria-3 is optimised for European business audio.

Speech-To-Text

Podcast transcription at scale: an API workflow for media platforms

TL;DR: Podcast audio is invisible to search without accurate, word-level transcripts, and transcription quality sets the ceiling for everything downstream, from content discovery to AI-generated show notes. A production-grade async pipeline (decoupled webhook ingestion, pyannoteAI-powered diarization, word-level timestamps) is what separates a searchable audio library from a title-and-description catalog. At 10,000 hours monthly, a managed API costs $2,000–$6,100 depending on plan.

Speech-To-Text

HIPAA-ready meeting assistants for healthcare and therapy sessions

TL;DR: Building a HIPAA-ready meeting assistant requires more than a generic transcription wrapper. Any API that processes Protected Health Information on your behalf must sign a Business Associate Agreement (BAA) before PHI flows to it, and transcription accuracy matters more than most teams expect: word error rate can more than double in noisy, multi-speaker clinical environments compared to controlled recordings, meaning errors compound into every SOAP note and EHR entry downstream. This guide covers the BAA requirements, encryption controls, and unit economics product teams need to evaluate before committing to an audio infrastructure provider for clinical or therapy use cases.

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: