AI Note-Taker Buyer's Guide 2026: A Neutral Comparison of Otter, Fireflies, Notta, Fathom and Other Leading Tools

Over the past two years, AI meeting note-takers have gone from a novelty to standard kit. Automatically generating transcripts, summaries and action items after a meeting saves a meaningful amount of cleanup time — some surveys suggest most users save around 4 hours a week. More tools is a good thing, but it also raises a practical question: Otter, Fireflies, Notta, Fathom, tl;dv, Granola, Read AI… which one should you actually pick?

The answer isn't really "which is most popular," but "which fits the way you meet." These tools differ a lot in free allowances, language support, real-time vs. after-the-fact, and privacy design. What works for your colleague may not work for you.

This article takes a neutral look at where each one stands, its free plan and paid starting price, includes a comparison table, and then gives recommendations by use case. It doesn't disparage any single tool — each does something well, and the key is matching it to your needs.

The 30-second version: quick-pick by use case

• Want to try free without being boxed in by an allowance → Fathom, Fireflies and tl;dv all offer near-unlimited recording/transcription on their free plans (each with its own summary or storage cap).
• Mostly English or single-language meetings, and you value after-the-fact summaries → Otter, Fireflies and Fathom are all mature, dependable choices.
• You need Chinese, especially Traditional or Cantonese → Notta is one of the few tools that explicitly lists Cantonese in its official language list; most others focus on Simplified/Mandarin.
• Meetings are cross-lingual and everyone needs to understand each other in the moment → you need an integrated "real-time transcription + real-time translation" solution (such as Traverba); most note-takers translate after the fact.
• You care about privacy and don't want a bot joining every call → note the difference between "bot vs. no bot" and "cloud vs. on-device" (more below).

Let's go through each in turn.

Before choosing a tool, ask yourself 6 questions

Before comparing specs, get clear on what you actually care about. These six dimensions largely determine which tool fits you:

1. Is the free allowance enough? Some cap by "minutes per month" (e.g. Otter 300 minutes, Notta 120 minutes); others loosen recording limits and instead cap "number of AI summaries" or "storage duration" (e.g. Fathom, Fireflies, tl;dv). Light and heavy users experience these very differently.
2. Do you want real-time, or after-the-fact? Most tools are fundamentally about after-the-fact cleanup — you get the full transcript and summary once the meeting ends. If you need captions or translations during the meeting, check carefully whether it truly supports real-time.
3. Language, especially Chinese and Cantonese. "Supports 100+ languages" is a common pitch, but check whether the specific one you need is in the official language list — Mandarin, Traditional and Cantonese are often not the same thing.
4. How does it join your meeting? Some rely on a "meeting bot" that auto-joins Zoom/Teams/Meet and shows up in the attendee list; others capture audio locally on your device and send no bot into the call.
5. Integrations and export. Can it auto-sync to Notion, Slack, Google Docs, CRM? Is the export format (e.g. Markdown) easy to move into the tools your team actually uses?
6. Privacy and data handling. Whose cloud does the meeting audio go to? How long is it kept? Does it meet your company's compliance requirements? This matters especially for sensitive meetings.

The comparison below is much clearer with these six questions in mind.

The leading tools at a glance

The table below summarizes where each one stands, based on public information (specs, prices and language counts reflect publicly available information as of mid-2026; vendors update frequently, so verify against the latest official announcements before adopting):

A note on "number of supported languages": the official language list tells you "supported or not," which isn't the same as "transcription quality." For example, Notta officially claims accuracy of up to 98.86% — a vendor's own self-reported figure — and real-world results vary by language, accent and recording environment, so it's best to test it yourself against your own scenario. Also, the same company sometimes gives inconsistent language counts across different pages (for example, Notta cites both 58 and 104+, the former usually referring to transcription languages).

Where each one genuinely shines

To be fair, these tools became popular because each does something well:

• Otter — deep integration with Zoom/Teams/Google Meet, mature auto-join, English summaries and live captions; a long-established choice for English meetings.
• Fireflies — broad multilingual transcription coverage (100+ languages), plus rich integrations and knowledge-base features, good for teams turning meeting content into searchable material.
• Notta — a language list that's especially friendly to Chinese, and one of the few mainstream tools that lists both Mandarin and Cantonese; its transcription speed and interface are widely praised too.
• Fathom — a genuinely generous free plan (unlimited recording and transcription), often rated the most charitable free option, good for individuals and small teams getting started.
• tl;dv — known for video + timestamped highlights, making it easy to clip and share meeting segments; especially popular with sales and product teams.
• Granola — takes a "no bot" approach, completing the notes you type while listening into a full record locally on your desktop; lightweight and unobtrusive to the meeting.
• Read AI — beyond summaries, it also offers analytics on engagement, sentiment and meeting efficiency, good for teams that want to quantify "meeting quality."

If your meetings are mostly single-language (especially English) and your main need is after-the-fact transcripts and summaries, any of the above is a reasonable choice; the differences come down more to free allowances, integration ecosystem and personal habit.

Chinese and Cantonese support: Hong Kong users, take note

For users in Hong Kong (and Chinese-speaking regions), one dimension deserves a separate look: Chinese, and especially Cantonese, plus Cantonese-English mixing.

There are two layers to distinguish here:

1. "Supports Chinese" doesn't mean "supports Cantonese." Many tools default "Chinese" to Mandarin, sometimes Simplified only. Taking official public language lists as the reference: Otter currently lists Chinese only as Simplified Mandarin; Fireflies lists zh / zh-CN / zh-TW but has no separate Cantonese (zh-HK) entry. By contrast, Notta is one of the few mainstream tools that explicitly lists Cantonese in its official list. These are all language ranges each vendor publishes itself — a neutral statement of fact, not a judgment of quality.

2. Cantonese-English mixing (code-switching) is itself a recognized hard problem. Hong Kong meetings commonly feature sentences that switch between Chinese and English mid-way, like "我想去 Causeway Bay 開會" ("I want to go to Causeway Bay for a meeting"). Academia has long noted this is a tough nut for speech recognition: a CUHK team conducted the first Cantonese-English code-switching speech recognition study back in 2006–2009, when the system achieved roughly 56% accuracy on Chinese characters and about 53% on English words within mixed sentences — these are baseline figures from nearly two decades ago, meant to illustrate how hard the problem is, not the performance of any current commercial tool. Models have improved greatly in recent years, but research (arXiv 2023–2025) still notes that Cantonese-English code-switching speech data has long been scarce — a "low-resource" problem — and general-purpose multilingual models still make mistakes on borrowed words. For example, one study documented that a smaller OpenAI Whisper model once misheard the Cantonese "貼士" (tip) as the English "Tipsy" (while a larger model in the same family recognized it correctly, showing the issue is closely tied to model size and training data).

Why is mixing so hard? Research points out that in Hong Kong Cantonese-English sentences, Cantonese is the matrix language and English is mostly an embedded single word or short phrase; these English fragments are very short, and speech recognition systems struggle to judge in real time that "this little bit is English," so errors creep in.

What this means for you: if your meetings are mostly in English or Mandarin, most of the tools above are good enough; but if you frequently encounter mostly-Cantonese conversations with embedded English, it's worth testing recognition on your own real meeting recordings before formally adopting a tool, and giving priority to solutions that explicitly cover Cantonese in their language list, or that are specifically optimized for Cantonese.

Privacy and data handling: the part that's easy to overlook

One last dimension that's often overlooked yet important for businesses: where does your meeting audio go?

Tools broadly take two stances:

• Bot vs. no bot. Otter, Fathom, Fireflies and others mostly join the call with a "meeting bot" that shows up in the attendee list; tools like Granola take a local route and send no bot into the call. The former is convenient for automation; the latter is less intrusive and more discreet.
• Cloud vs. on-device processing. Most tools do their AI processing in the cloud — convenient, but it means the conversation leaves your device; some tools emphasize doing more processing locally to reduce what's uploaded.

There's no absolute right or wrong — cloud solutions are usually more capable with more integrations, while local/on-device solutions have the edge on privacy and offline use. The key to choosing is how sensitive your meetings are and what compliance requirements your company has. If legal, financial, medical or unreleased business information is involved, it's advisable to review each vendor's data retention policy and compliance certifications (such as SOC 2, GDPR) before deciding.

One additional option: when a meeting is "cross-lingual" rather than "after-the-meeting"

The tools above are, at their core, excellent meeting-record tools — accurately preserving what was said so you can review it afterward. That's genuinely useful for the vast majority of meetings.

But there's one situation they cover less well: multiple languages present during the meeting itself, where everyone needs to understand each other in real time. Here you need more than "a transcript afterward" — you need "real-time transcription + real-time translation" happening at once.

This is exactly where Traverba positions itself. Rather than being yet another after-the-fact note-taker, it combines real-time transcription and real-time translation into one, purpose-built for multilingual meetings and events:

• Real-time — as someone speaks, the captions and translation appear together; no waiting until the meeting ends.
• Everyone in the room can read it — attendees scan a QR code with their phone and instantly see live captions in their own language on their own screen.
• Cantonese-first — specially optimized for Hong Kong's Cantonese-English mixing and local vocabulary, exactly where general-purpose tools are weaker.
• On-device-first processing — speech recognition runs on the device by default, with translation and cloud AI optional; an extra layer of control for sensitive meetings.
• Just as complete afterward — you can export the full transcript and translation after the meeting, so after-the-fact cleanup isn't missing either.

To be clear: Traverba isn't out to replace the tools above. If your meetings are single-language and you only need an after-the-fact summary, Otter, Fireflies, Notta, Fathom and the rest are still great choices. What Traverba fills is the gap of "multiple languages, needing to understand each other in the moment."

How to choose? A decision checklist

Choose a general AI meeting note-taker if you —

• Mostly hold single-language meetings (usually English, or the same language throughout)
• Mainly need after-the-fact transcripts, summaries and action items
• Value automatic integration with Zoom/Teams/Meet, and connections to Notion/Slack/CRM
• → Want to run free without constraints, look at Fathom / Fireflies / tl;dv; want English summaries and a mature ecosystem, look at Otter; want Chinese-friendliness, look at Notta; want no bot and a lightweight local tool, look at Granola; want meeting analytics, look at Read AI.

Prioritize a Cantonese-supporting solution if you —

• Frequently have meetings that are mostly Cantonese with Cantonese-English mixing
• → Choose among tools that explicitly cover Cantonese in their language list, and be sure to test with real recordings.

Consider a real-time-translation solution (such as Traverba) if you —

• Have multiple languages present at meetings or events and need everyone to understand each other in the moment
• Want every person present to see live captions in their own language, not just a record for yourself afterward
• Also want a full exportable transcript and translation afterward

Learn more

There's no "best AI meeting note-taker," only "the tool best suited to your kind of meeting." Think through the six questions above first — free allowance, real-time or after-the-fact, language (especially Cantonese), how it joins, integrations and export, privacy — then cross-reference the comparison table, and you can usually narrow it down to one or two candidates and decide by testing for yourself.

If your focus is multilingual real-time communication: Traverba provides real-time transcription + real-time translation for meetings and events — one speaker, 100+ languages, attendees scan a QR code to see live captions in their own language, and after the meeting you can export the full transcript and translation. To learn about meeting and event solutions, visit traverba.com; for personal real-time translation, you can also download the app for free, available on both Google Play and the App Store.

The positioning, prices, free allowances, language counts and Chinese/Cantonese support of the products mentioned here — Otter, Fireflies, Notta, Fathom, tl;dv, Granola, Read AI — reflect publicly available information as of mid-2026 and may change at any time; all figures should be verified against each company's official announcements. The accuracy figures in this article (such as Notta's self-claimed 98.86%) are vendor self-reported, not independent test results; the 56%/53% for Cantonese-English mixing recognition is a historical baseline from a 2006–2009 academic study and does not represent the performance of current commercial tools. Transcription and translation results vary by language, accent, recording environment and network conditions, so we recommend testing for yourself based on your own situation. All brands and trademarks belong to their respective owners.