Real-Time vs Post-Meeting Transcription: Which Should Your API Use?

The two models

Real-time transcription delivers transcript segments as speech is detected — often with less than one second of latency. The transcript grows incrementally during the meeting.

Post-meeting transcription processes the complete audio recording after the meeting ends, typically delivering the full transcript within 30–90 seconds.

Both are valid. The right choice depends on what you're building.

When real-time is necessary

Real-time transcription is essential when you need to act on what's being said *during* the meeting:

• Live captions shown to participants
• Real-time keyword alerts to a sales manager watching live
• In-meeting prompts ("you haven't mentioned pricing yet")
• Live translation

The tradeoff: real-time transcription is less accurate than post-meeting transcription. The model sees less audio context, which means it handles overlapping speech, accents, and domain-specific vocabulary worse.

When post-meeting is better

For most meeting intelligence use cases, post-meeting transcription is the right choice:

• Meeting summaries emailed after the call
• CRM logging after the conversation ends
• Action item extraction
• Compliance recording
• Coaching review

Post-meeting transcription sees the full audio, produces higher accuracy, and can run speaker diarization across the entire recording for more consistent speaker labels.

Gregnote's model

Gregnote uses post-meeting transcription delivered via webhook. The bot captures the complete audio, transcription runs on the full recording, and the diarised transcript arrives in your webhook payload typically within 30–60 seconds of the meeting ending.

For use cases that need real-time captions, the native captioning in Google Meet and Teams is usually the better choice — it's integrated with the platform and visible to all participants without any external tooling.