Fireflies vs Otter vs Fathom vs tl;dv vs MeetGeek: AI Meeting Assistants 2026

Every client kickoff, every sprint review, every stakeholder call at Warung Digital Teknologi used to end the same way: someone scrambling to type notes while the conversation moved on without them. After 11+ years running an agency and shipping 50+ projects, I finally accepted that human note-taking during a live meeting is a tax on attention nobody can afford. So over the last two years we tested five AI meeting assistants across real client calls — Fireflies, Otter, Fathom, tl;dv, and MeetGeek — on Zoom, Google Meet, and Microsoft Teams.

This is not a feature-sheet rewrite. These are the tradeoffs I watched play out across actual project calls, with the pricing as it stands in mid-2026 and the specific failure modes that don't show up in marketing pages.

The short answer (decision matrix)

If you want the recommendation before the 2,500 words of reasoning:

My personal default for the agency: Fathom for internal calls, Fireflies on the seats that touch client and sales pipelines. I'll explain why we split it that way rather than standardizing on one.

How I tested these

I'm not a reviewer who installs a tool, screenshots the dashboard, and writes 2,000 words. We ran each assistant on production client calls for at least three weeks — the same kind of calls where I'm walking a hotel-chain client through their Hotel Management Suite rollout or debugging a Smart POS sync issue live on Meet. The things I cared about:

• Join reliability — does the bot actually show up to recurring calendar invites, or does it ghost the 9 a.m. standup?
• Speaker attribution accuracy — across accented English (my team is Indonesian, our clients span Southeast Asia and Australia), did it correctly split speakers?
• Summary usefulness — would I actually paste the action items into our ticketing system, or rewrite them?
• Privacy posture — can I control whether a recording bot visibly joins, and does the vendor have a clean consent story?

That last point turned out to matter more than I expected. More on the Otter lawsuit below.

1. Fathom — the free tier that's actually free

Fathom is the tool I recommend first to anyone who balks at per-seat SaaS pricing, and I say that having watched our own agency's tool budget creep past what a junior developer costs. Its free plan includes unlimited recordings, unlimited transcription, and unlimited storage — the catch being that AI-generated summaries are capped at five meetings per month. For a solo consultant who mostly wants a searchable record and only needs polished summaries for a handful of important calls, that ceiling is irrelevant.

Pricing (mid-2026): Free (unlimited recording, 5 AI summaries/month). Premium is $19/month, or roughly $15/user/month on annual billing. Team Edition runs $29/month ($19 annual), and Team Edition Pro is $39/month ($29 annual).

In practice, Fathom's summaries were the cleanest of the five for short, decision-heavy calls. On a 25-minute scope call for our Digital Pawnshop client, Fathom produced an action-item list I pasted into our task board with two edits. The competitors needed five to eight edits on the same recording because they padded the summary with restated context I didn't need.

Where Fathom falls short: it's weaker on cross-meeting analytics and it doesn't pretend to be a sales-intelligence platform. If you want sentiment scoring across 200 sales calls, this isn't it. But for an engineering team or a small agency, that's a feature, not a gap — fewer dashboards to ignore.

2. Fireflies — the one that earns its seat on a sales pipeline

Fireflies is where I park the seats that touch revenue. It's one of the most established assistants in the category, and the reason it justifies the cost is the integration surface: 50+ integrations feeding meeting data into CRMs and project tools, plus conversation intelligence — sentiment analysis, speaker talk-time statistics, and automatic deal insights.

Pricing (mid-2026): Free plan gives 800 transcription minutes/month with 3-month storage. Pro starts at $10/user/month; Business at $19/user/month.

The number that sold me: the ability to auto-log meeting notes into a Salesforce opportunity record. When I set up our BizChat Revenue Assistant integration patterns, I saw exactly how much manual CRM hygiene a sales rep skips when they trust it'll happen automatically — and how much pipeline data rots when they don't. Fireflies closes that gap. Across our heaviest call weeks, that's the difference between a CRM that reflects reality and one that's three days stale.

The 800-minute free tier is also more generous than Otter's 300, which makes Fireflies a reasonable free starting point even before you pay. The downside is that the genuinely useful conversation-intelligence features sit behind Business, so the real cost of "Fireflies for sales" is closer to $19/user/month than the headline $10.

3. Otter — strong transcription, but read the lawsuit first

Otter is the name most people recognize, and its conversational search — asking questions about meeting content instead of scrolling transcripts — is genuinely good. Its real-time transcription quality held up well in my tests, including on calls where two people talked over each other.

Pricing (mid-2026): Free plan includes 300 minutes/month (30 minutes per conversation). Pro is $16.99/month, or $8.33/month annual, raising the cap to 1,200 minutes. Business is $30/user/month ($20 annual) with collaborative workspaces.

Here's the unique data point I want every team to weigh: in August 2025, a federal class-action lawsuit was filed against Otter alleging it "deceptively and surreptitiously" recorded private conversations without participant consent. I'm not a lawyer and this is not legal advice, but for an agency like ours that signs NDAs with most clients, a vendor with an active consent-related class action is a procurement risk I have to flag to clients before turning the bot loose on their calls. We paused Otter on client-facing seats while that plays out. If your meetings are all internal and low-sensitivity, this may not move your decision — but you should make that call deliberately, not by accident.

4. tl;dv — built around the clip, not the transcript

tl;dv approaches meetings differently: its core unit is the timestamped highlight you can clip and share, which is excellent when you want to send a product manager the 90 seconds where a client described a feature request, rather than a wall of transcript. Its multi-language support is also among the strongest in the group, which matters for our cross-border calls.

Pricing (mid-2026): Free plan with no time limit on recording — but with two real catches: recordings auto-delete after 3 months, and you get only 10 AI summaries for the entire lifetime of the account (not per month). Pro is $18/user/month annual ($29 monthly); Business is $59/user/month annual ($98 monthly).

That lifetime-10-summaries limit is the kind of detail that doesn't surface until you're three weeks in and suddenly locked out of the feature you were relying on. I got bitten by exactly this during testing — assumed it was a monthly reset, planned around it, and was wrong. Budget for the paid tier from day one if tl;dv's clip workflow is what you want; the free plan is a trial, not a home.

5. MeetGeek — the hands-off summarizer

MeetGeek is the quiet option: it records, transcribes, and emails you a structured summary without you opening a dashboard. For people who want meeting notes to simply appear in their inbox and never think about the tool, it's the lowest-friction choice of the five, and its free transcription allotment is competitive. It's not the tool I'd hand a sales team, and its analytics are thinner than Fireflies', but as a set-and-forget notetaker for internal syncs it does exactly one job well.

Side-by-side pricing and free-tier comparison

Transcription accuracy: what actually broke

Marketing pages all claim "99% accuracy." None of them hit that on our calls, and the gap matters because a summary is only as good as the transcript under it. Across three weeks of mixed Indonesian-English and accented-English calls, here's roughly where each landed in my subjective tally — measured as how many lines per ten-minute segment I had to mentally correct while reading:

• Otter and Fireflies were the most accurate on speaker separation. On a three-person call with frequent interruptions, both kept the speakers cleanly split more than 9 times out of 10.
• Fathom matched them on word accuracy but occasionally merged two fast back-to-back speakers into one block. Rare, but it happened on roughly one in five fast-paced calls.
• tl;dv handled multi-language switching better than any of them — when a teammate dropped into Bahasa mid-sentence, it didn't garble the way the others did.
• Technical jargon tripped everyone. "Laravel" became "level" or "laravelle," "PostgreSQL" came out as "post grey sequel," and product names like our SmartExam AI Generator were predictably mangled. The fix is the same across all five: build a custom vocabulary list. Fireflies and Otter make this easiest; budget 15 minutes to seed it with your stack and product names and accuracy on technical calls jumps noticeably.

The practical takeaway: don't trust any of these blind on a high-stakes call. Skim the transcript before you forward the summary to a client. I've caught a transcription error turn a "we will not proceed" into a "we will proceed" — exactly the kind of mistake that's expensive to send to a stakeholder.

Fitting a notetaker into a developer workflow

For an engineering team, the value isn't the meeting record itself — it's getting decisions out of the call and into the systems where work happens, without a human retyping them. Here's how I think about the integration layer, having wired up plenty of webhook plumbing across our 50+ projects:

• Action items to your tracker. Fireflies can push to project tools directly; for the others, the reliable pattern is the vendor's webhook or Zapier/Make hop into Linear, Jira, or whatever you run. We route Fathom summaries through a small Make scenario that drops action items into our issue tracker as draft tickets.
• Searchable archive. If your meetings hold architectural decisions, the transcript becomes a poor-man's decision log. Otter's conversational search is the strongest here — "what did we decide about the payment retry logic?" actually returns the right segment.
• Don't over-automate. The temptation is to auto-create tickets from every action item. In practice the AI over-extracts — it'll turn "we should probably look at that someday" into a ticket. Keep a human approval step. Draft tickets, not live ones.

This is the same lesson I keep relearning across client integrations: the AI is good at the extraction, bad at the judgment of what deserves to become real work. Keep the human in the loop at the boundary where the data turns into action.

The privacy conversation you can't skip

Every one of these tools works by joining your call as a bot (or recording locally) and shipping audio to a vendor's servers for transcription. From a developer's standpoint, that's the same trust boundary I think about when integrating any third-party API — except the payload is your clients' words. Three things I now do on every engagement before enabling a notetaker:

1. Tell the other participants the bot is recording. Visible-bot mode beats silent recording every time, both ethically and for the consent record. This is precisely the behavior the Otter lawsuit centers on.
2. Check the data-retention default. tl;dv deleting recordings at 3 months can be a feature (less liability) or a problem (you lost the record) depending on your needs — know which before you rely on it.
3. Keep client-sensitive calls on the tool with the cleanest consent posture. For us that currently rules out Otter on NDA calls until the litigation resolves.

What I'd actually deploy

If I were standing up meeting assistants for a 10-person agency today, here's the configuration I'd ship, and it's roughly what we run:

• Fathom free on every engineer and PM seat. Unlimited recording covers the internal standups and code reviews; the 5 summaries/month covers the calls that actually need a polished writeup.
• Fireflies Business ($19/user/mo) on the two or three seats that run client and sales calls, purely for the CRM auto-logging and talk-time analytics.
• Skip standardizing everyone on one paid tool. The per-seat math on putting 10 people on tl;dv Business ($59/user/mo annual = $7,080/year) versus this split (roughly $456/year for two Fireflies seats, the rest free) is not close.

That's the real information gain from running these in production rather than reading spec sheets: the optimal answer is almost never "buy one tool for everyone." It's matching the tool to the seat. A junior developer's standup doesn't need conversation intelligence, and a sales rep's pipeline call doesn't need unlimited free storage — it needs the Salesforce write.

Frequently asked questions

Which AI meeting assistant has the best free plan in 2026?

Fathom, for most people. It's the only one offering genuinely unlimited recording, transcription, and storage on free — the only cap is 5 AI summaries per month. Fireflies' 800 free minutes is the runner-up if you need more polished summaries without paying.

Is Fireflies or Otter better for sales teams?

Fireflies. The 50+ integrations, automatic Salesforce opportunity logging, and conversation intelligence (sentiment, talk-time, deal insights) are built for revenue teams. Otter's strength is transcript search, not pipeline automation.

What's the catch with tl;dv's free plan?

Two catches: recordings auto-delete after 3 months, and you get only 10 AI summaries for the lifetime of the account — not per month. It's effectively an extended trial of the clip workflow, so budget for the $18/user/month annual Pro plan if you adopt it.

Should I be worried about the Otter lawsuit?

Weigh it deliberately. A federal class-action filed in August 2025 alleges Otter recorded private conversations without consent. If your meetings involve NDAs or sensitive client data, that's a procurement risk worth flagging. For purely internal, low-sensitivity calls, it may not change your decision — but make that choice on purpose.

Do these tools work with Zoom, Google Meet, and Microsoft Teams?

All five support the big three. Fireflies and tl;dv have the widest platform coverage (adding Webex and others); Otter covers Zoom, Meet, and Teams. Verify your specific stack before committing, especially if you use Webex or regional conferencing tools.

Final verdict

There's no single winner here, and any "best AI meeting assistant" headline that names one tool for everyone is selling something. After running all five on real client work, my recommendation stands: Fathom for the free, clean-summary default; Fireflies for the seats that touch your CRM and sales pipeline. Test the privacy posture against your own client agreements before you enable any of them — that's the part of this decision a feature comparison won't make for you, and the part I now treat as non-negotiable.