Listen Labs and Subanana are both AI meeting assistants for recording, transcription, and summaries, compared here on pricing, features, and workflow fit. Listen Labs: Enterprise AI customer research platform that recruits participants, runs AI-moderated interviews, and delivers executive-ready insight reports. Subanana: Hong Kong AI transcription and subtitling platform with meeting transcription, summaries, and multilingual support. They overlap on ai-meeting-assistants, so the right pick depends on team size, budget, and which meeting workflows you automate.
For ai-meeting-assistants workflows, shortlist Listen Labs when replacing or supplementing surveys with scalable ai-led customer interviews matters most, and Subanana when transcribing and summarizing cantonese-english meetings for hong kong teams matters most. Both record across Zoom, Google Meet, and Microsoft Teams; trial each on real meetings before committing.
Enterprise AI customer research platform that recruits participants, runs AI-moderated interviews, and delivers executive-ready insight reports.
AI interviewer running personalized video, audio, and text interviewsAutomated key themes, takeaways, and persona generationExecutive-ready reports with highlight reels and slide decks
Hong Kong AI transcription and subtitling platform with meeting transcription, summaries, and multilingual support.
AI transcription and subtitle generation across 80+ languagesAutomatic speaker identificationMeeting transcription with automated summaries and action items
Listen Labs is a free tier with paid upgrades (freemium); Subanana is a free tier with paid upgrades (freemium). Always confirm current pricing on each vendor's site before buying.
AI interviewer running personalized video, audio, and text interviews
AI transcription and subtitle generation across 80+ languages
Standout feature
Global participant recruitment with bring-your-own-audience option
Strong handling of mixed Cantonese-and-English (code-switching) audio
Team usage
Stimulus testing with videos, images, and Figma prototypes
Meeting transcription with automated summaries and action items
Integrations
Automated key themes, takeaways, and persona generation
Real-time voice transcription with live translation
Languages & capture
Executive-ready reports with highlight reels and slide decks
Automatic speaker identification
Best-fit workflow
Support for 100+ languages with translation and transcription
Subtitle embedding and export to .srt, .docx, .csv, and .txt
Best for
Listen Labs
Choose Listen Labs if you need replacing or supplementing surveys with scalable ai-led customer interviews — strengths include end-to-end automation from recruitment to executive-ready deliverables.
Subanana
Choose Subanana if you need transcribing and summarizing cantonese-english meetings for hong kong teams — strengths include built for the hong kong market with strong cantonese and code-switching support.
Pros & cons
Listen Labs
+ End-to-end automation from recruitment to executive-ready deliverables
+ Large global participant network reduces sourcing effort
- Enterprise positioning may exceed the needs and budgets of small teams
Subanana
+ Built for the Hong Kong market with strong Cantonese and code-switching support
+ Covers both subtitling/content workflows and meeting transcription
- Free allowance is limited before requiring a paid plan
FAQ
Is Listen Labs or Subanana better for AI meeting notes?
It depends on your workflow. Listen Labs is strong for replacing or supplementing surveys with scalable ai-led customer interviews, while Subanana is strong for transcribing and summarizing cantonese-english meetings for hong kong teams. Both transcribe and summarize meetings.
How do Listen Labs and Subanana compare on price?
Listen Labs is a free tier with paid upgrades and Subanana is a free tier with paid upgrades. Check each vendor's pricing page for the latest plans and free-tier limits.
Can I use both Listen Labs and Subanana?
Yes. Many teams run more than one meeting assistant when the workflows are complementary and the budget is justified.