API Comparison Table

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Pricing

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Transcribe phone calls and push them to HubSpot with Gladia and Zapier

TL;DR: Every wrong name or missed entity in a call transcript silently corrupts your CRM data downstream, and self-hosted pipelines compound this with GPU maintenance overhead and poor accuracy on accented speech. This guide shows you how to route call recordings from Twilio or Aircall through Zapier to our async API, run LLM extraction on the diarized transcript, and push structured deal properties and engagement logs to HubSpot without maintaining custom infrastructure. Multiple customers have the Gladia API layer running in under 24 hours. The remaining pipeline configuration time depends on your Zapier, LLM, and HubSpot setup complexity.

Speech-To-Text

Cutting transcription cost per audio hour for meeting assistants

TL;DR: Transcription cost and accuracy are critical drivers of unit economics for meeting assistant builders. Headline API rates are deceptive because hidden fees for diarization, translation, and billing increments routinely double the effective cost per hour. Self-hosting open-source models introduces GPU underutilization and DevOps overhead that can exceed managed API costs by approximately 3x at early-stage volume. Teams that switch to all-inclusive pricing (diarization, translation, and sentiment bundled at as low as $0.20/hr on our Growth plan) recover meaningful margin without waiting for enterprise-tier volume to justify the conversation.

Speech-To-Text

Scaling real-time STT for high-concurrency voice agents

TL;DR: Scaling real-time voice agents to hundreds of concurrent calls requires moving from stateless CPU-based autoscaling to stateful WebSocket connection management. The failure mode is predictable: architectures that handle test calls at low concurrency struggle when traffic spikes, producing latency spikes and dropped audio. The fix involves scaling on active connection counts, implementing ping/pong heartbeat monitoring to reclaim hanging sessions, and enforcing hard connection limits to protect downstream LLM and TTS layers. Additional capacity comes online without pre-provisioning, so the STT layer is not a fixed ceiling as session counts grow.

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: