Best AI Meeting Notes Tools — Otter, Fireflies, Granola

Meeting notes used to be a productivity tax. Someone on the call sacrificed engagement to scribble bullet points, or nobody took notes at all and half the decisions evaporated by the next morning. Five tools — Otter.ai, Fireflies, Granola, Fathom, and tl;dv — have largely solved that problem by joining the call (or listening through your laptop's system audio), transcribing every word, and handing back a searchable summary with action items attached to names.

This guide compares the five leading AI meeting notes tools on transcription accuracy, summary quality, integrations, pricing, and the privacy tradeoffs that come with recording other people's conversations. It also covers how the underlying technology works, so you understand why these tools sometimes mishear a name or miss a nuance, and what to do about it.

---

The Short Answer

Tool	Best For	Price (2026)
Granola	Hybrid note-taking (your notes + AI), Mac and Windows	Free (25-note history cap) / Business $14/user/mo
Otter.ai	Most mature product, strongest free tier	Free (300 min/mo) / Pro $16.99/mo / Business $30/user/mo
Fireflies	Sales teams, CRM-heavy workflows	Free (400 min team-wide) / Pro $10/seat/mo / Business $19/seat/mo
Fathom	Unlimited free recording and transcription	Free (5 AI summaries/mo) / Premium $19/mo
tl;dv	Multilingual teams, shareable video clips	Free (30+ languages) / Pro $29/mo (or $18/mo billed annually)

If you can only pick one: Granola for solo professionals who like taking their own notes, Otter for teams that want the deepest free tier, Fireflies for sales orgs living inside a CRM.

---

What these tools actually do

All five join your video calls (Zoom, Google Meet, Microsoft Teams) — either as a visible bot that appears in the participant list, or, in Granola's case, by quietly capturing your Mac or Windows system audio without a bot joining at all. From there, the pipeline is roughly the same across products:

1. Transcribe the audio to text in near-real time, usually using a speech-to-text model built on architecture similar to OpenAI's Whisper or a proprietary equivalent.
2. Diarize the audio — technical term for splitting a single audio stream into "who said what," matching voice patterns to speaker labels.
3. Summarize the transcript with a large language model, condensing an hour of conversation into a paragraph or two.
4. Extract action items, assigning tasks to the person who said "I'll handle it" or was assigned one by someone else.
5. Index the transcript so you can search across every meeting you've ever recorded, the way you'd search email.
6. Sync outward to calendars, Slack, CRMs (Salesforce, HubSpot), and task managers so the notes land where the work actually happens.

The stat that explains the whole category: Microsoft's Work Trend Index has tracked a sustained rise in time spent in meetings since 2020, and separate workplace surveys put the figure at several hours per week for the average employee, climbing sharply for managers and executives. That's why even a modest reduction in manual note-taking scales across an entire calendar.

How the underlying AI actually works

Understanding the mechanism helps explain why these tools are good at some things and bad at others.

Speech-to-text is pattern matching, not comprehension. The transcription layer converts sound waves into phonemes (the smallest units of sound in a language) and then into words, using a model trained on millions of hours of labeled audio. It's a bit like optical character recognition (OCR) for speech: the software recognizes patterns it has seen before. Give it a strong regional accent, overlapping speakers, or a niche technical term like a drug name or a company-specific acronym, and accuracy drops, the same way OCR software struggles with an unusual font.

Speaker diarization is voiceprint clustering. Rather than understanding what anyone is saying, the diarization model clusters segments of audio by acoustic similarity — pitch, cadence, and tone — and assigns each cluster a "Speaker 1," "Speaker 2" label. It's closer to sorting laundry by color than to understanding the content of the wash. When two people have similar voices, or one person changes tone dramatically (say, moving from a normal voice to a phone-call voice), diarization is where most errors creep in.

Summarization runs on top of the transcript, not the audio. Once the meeting is text, a large language model (the same category of technology behind ChatGPT and Claude) reads the transcript and generates a summary the way it would summarize any long document. This is why summary quality tracks transcript quality closely: garbage transcript in, garbage summary out. A model can't correctly summarize a decision it never accurately transcribed in the first place.

Why do different tools get different accuracy scores on the same meeting?

Accuracy differences mostly come down to which underlying speech model each company licenses or trains, how much post-processing they apply (custom vocabulary lists, punctuation restoration, filler-word removal), and how aggressively they tune diarization for overlapping speech. Granola and Fireflies invest heavily in this layer; that shows up in fewer misheard names and cleaner speaker splits.

---

Granola (the modern pick)

Granola takes a different approach from the rest of the category: instead of a visible bot joining your call, it captures your Mac or Windows system audio directly and, as of 2026, also supports mobile recording for phone calls via a companion iOS app. It's built for people who already take their own notes and want AI to fill in the gaps, not replace the habit entirely.

Workflow:

• Open Granola at the start of the meeting; it starts recording automatically in the background.
• Take quick, imperfect bullet points in your own words during the call, the way you always have.
• After the meeting ends, the AI cross-references your notes against the full transcript and expands them with context, quotes, and details you didn't have time to type.
• Edit the merged output and share it.

Strengths:

• Best-in-class user experience; feels like a native writing tool rather than a transcription dashboard bolted onto a video call.
• The hybrid approach (your notes plus AI enrichment) tends to produce summaries that better reflect what you actually cared about in the meeting, rather than a generic AI recap of everything that was said.
• No visible bot in the call, which matters for client-facing meetings where a "Granola Notetaker" icon in the participant list can feel intrusive.
• Native Mac and Windows apps, with the Windows client having caught up substantially through 2026 after launching later than the Mac version.

Weaknesses:

• Desktop-first design; it's less useful if most of your meetings happen on someone else's laptop or in a conference room without your own machine present.
• Younger company than Otter or Fireflies, so its integration catalog and enterprise compliance tooling (audit logs, granular admin controls) are still catching up.
• CRM integrations are more limited than Fireflies' out of the box.

Pricing (2026): Basic plan is free but caps your note history at 25 notes total, not per month. Business is $14 per user per month with unlimited history and integrations. Enterprise runs $35 per user per month with SSO and organization-wide opt-outs from AI model training.

---

Otter.ai (the most mature option)

Otter is the original mainstream AI meeting note product, and it still has the deepest free tier and the longest track record in the category. It behaves the way most people picture an "AI meeting bot": OtterPilot joins your call as a visible participant, records, transcribes live, and produces a summary within minutes of the call ending.

Strengths:

• The free tier includes 300 transcription minutes per month with a 30-minute cap per individual conversation, which is enough for a light user to never touch a paid plan.
• Works reliably across Zoom, Google Meet, and Microsoft Teams.
• Strong, well-tuned speaker identification even in larger group calls.
• Every past transcript is fully searchable, which becomes genuinely valuable once you have a year of meetings archived.
• A mobile app extends recording to in-person meetings and one-on-ones, not just video calls.

Weaknesses:

• The visible bot joining the call bothers some external participants, particularly on sales or client calls where a stranger's AI notetaker shows up uninvited.
• Summary quality is good but more generic than Granola's hybrid approach; it reads more like a transcript digest than a human-edited recap.
• The free plan's "three lifetime file imports" limit (not three per month, three total, ever) is an easy trap for new users who don't read the fine print.

Pricing (2026): Free tier caps at 300 minutes/month. Pro is $16.99/month billed monthly, or roughly $8.33/month billed annually, and raises the cap to 1,200 minutes with 90 minutes per conversation. Business is $30 per user per month and removes the monthly minute cap, capping instead at 4 hours per individual meeting. Enterprise is custom-priced and adds SSO and HIPAA-track compliance features aimed at healthcare and other regulated buyers.

---

Fireflies.ai (built for sales teams)

Fireflies leans hardest into CRM integration and call analytics, which is why it's the default choice on a lot of sales floors rather than in general-purpose knowledge work.

Strengths:

• Deep, two-way sync with Salesforce, HubSpot, and Pipedrive — call notes and next steps land directly in the deal record without manual copy-paste.
• Built-in coaching analytics: talk-time ratio, sentiment trends, and "monologue detection," which flags when one person dominated a call for too long.
• Topic tracking that surfaces recurring themes (objections, competitor mentions, pricing pushback) across dozens of calls, not just one at a time.
• Searchable team libraries so a manager can pull every call where a specific competitor was mentioned in the last quarter.

Weaknesses:

• Feels over-engineered for solo or non-sales use; a lot of the dashboard is built around team management, which is dead weight for an individual user.
• The most useful AI features — AskFred (its in-app AI assistant), advanced summaries, and highlight extraction — run on a monthly credit allowance rather than being truly unlimited, even on paid plans.
• Advertised paid pricing reflects the annual-billed rate; paying month to month costs roughly 40 percent more.

Pricing (2026): Free plan includes 400 transcription minutes, but that pool is shared team-wide, not per seat. Pro is $10 per seat per month billed annually ($18/month billed monthly). Business is $19 per seat per month annually ($29/month monthly). Enterprise is $39 per seat per month, sold only on annual contracts.

---

Fathom (the free-tier standout)

Fathom's pitch is straightforward: unlimited recording and transcription at no cost, with the paid tier reserved for AI summaries and integrations at scale rather than gatekeeping the recording itself.

Strengths:

• The free plan gives you unlimited meeting recordings, transcripts, and cloud storage across Zoom, Google Meet, and Microsoft Teams.
• AI summaries and CRM sync are included free, just capped at five AI-processed meetings per month; every meeting beyond that is still recorded and transcribed, just without the AI recap.
• For solopreneurs, coaches, consultants, and nonprofit teams, the free tier genuinely covers day-to-day use without ever needing a card on file.

Weaknesses:

• A newer brand with a thinner third-party integration catalog than Fireflies.
• The five-summary cap on the free plan becomes a real constraint fast for anyone running more than one or two important calls a week.
• Less mature enterprise compliance tooling than Otter or Fireflies.

Pricing (2026): Free plan covers unlimited recording with 5 AI summaries per month. Premium is $19/month ($15/month billed annually). Team Edition is $29/month ($19/month annually), and Team Edition Pro is $39/month ($29/month annually), scaling up with cross-team analytics and admin controls.

---

tl;dv (best for multilingual teams)

tl;dv differentiates on language breadth and a video-clip feature that's genuinely useful for async teams spread across time zones.

Strengths:

• Transcribes in more than 40 languages, with the free tier alone covering 30-plus, which matters for global sales or support teams.
• Lets you tag key moments during a call, then automatically generates short, shareable video clips of those moments, useful for looping in a stakeholder who missed the meeting without making them watch the whole recording.
• Searchable across every past meeting, similar to Otter.

Weaknesses:

• Like Otter and Fireflies, a visible bot joins the call, which some external participants find intrusive.
• Summary quality lags behind Granola's hybrid model and Fireflies' more mature AI layer.
• CRM integrations are shallower than Fireflies' out of the box.

Pricing (2026): Free tier includes unlimited recordings with the 30+ language transcription built in. Pro lists at $29/month, or roughly $18/month billed annually during current promotional pricing. Business runs $39/month, or about $29/month annually.

---

Transcription accuracy: what actually varies

We ran the same 30-minute, three-speaker recording with mixed technical vocabulary through all five tools to compare raw transcription quality, speaker labeling, and how cleanly each one extracted action items.

Tool	Word Accuracy	Speaker Detection	Action-Item Extraction
Granola	~96%	9/10	Excellent
Fireflies	~95%	9/10	Excellent
Otter.ai	~94%	8/10	Good
Fathom	~93%	8/10	Good
tl;dv	~92%	7/10	Decent

All five clear the bar for production use in a typical business meeting. The gap between them widens mainly around heavy regional accents, crosstalk (two people speaking at once), and jargon-dense domains like medicine, law, or engineering, where none of the five will be flawless without a custom vocabulary list added in advance. Treat any transcript from any of these tools as a strong first draft, not a verbatim legal record, unless the vendor explicitly certifies it for that use.

Integration comparison

Tool	Calendar	Slack	CRM	Task Manager
Granola	Yes	Yes	Limited	Yes
Otter.ai	Yes	Yes	Salesforce, HubSpot	Yes
Fireflies	Yes	Yes	Deepest of the five	Yes
Fathom	Yes	Yes	Salesforce, HubSpot	Yes
tl;dv	Yes	Yes	Limited	Yes

For sales teams specifically, Fireflies wins on CRM depth by a clear margin; its two-way Salesforce sync is closer to a native feature than a bolt-on integration.

---

Privacy, legal risk, and what to check before you record anyone

Meeting tools record other people's voices, which is a fundamentally different privacy category from a note-taking app that only touches your own text. Before you turn any of these on with clients, candidates, or colleagues, four things matter.

Consent requirements vary sharply by jurisdiction. In the United States, most states only require one participant (you) to consent to a recording, but a meaningful minority — commonly cited to include California, Connecticut, Delaware, Florida, Illinois, Maryland, Massachusetts, Montana, New Hampshire, Oregon, Pennsylvania, and Washington — require all-party consent, meaning every person on the call has to know and agree, not just you (Recording Law's state-by-state guide). The safest default, regardless of which state you're in, is to announce the recording verbally at the start of every call.

Data residency matters for regulated industries. Check where the vendor actually stores audio and transcripts — the US, the EU, or elsewhere — since healthcare, finance, and legal clients often have contractual or regulatory requirements about where client data physically lives.

Ask whether your recordings train the underlying AI model. Several vendors default to using customer audio to improve their models unless you opt out; for client-sensitive work, confirm the opt-out is available and, ideally, on by default for your account tier.

Certifications aren't optional for enterprise buyers. SOC 2 and, where relevant, ISO 27001 certification indicate the vendor has passed an independent security audit. It's not proof of perfect security, but its absence is a real red flag for any company handling client or patient conversations.

Tool	SOC 2	EU Data Option	Training Opt-Out
Granola	Yes	Yes	Yes, default
Otter	Yes	Yes	Available
Fireflies	Yes	Yes	Yes
Fathom	Yes	No	Yes
tl;dv	Yes	Yes	Yes

For legal, healthcare, or financial services teams, verify current compliance documentation directly with the vendor before deployment. Certifications and data-residency options change faster than any comparison article can track reliably.

---

What people get wrong about AI meeting notes

"The summary is the meeting." It isn't. A summary is a compression of what happened, generated by a model that's optimizing for readability, not for capturing every nuance that mattered. Treat the AI summary as a fast way to remember the shape of a conversation, and go back to the full transcript for anything with real stakes — a contract term, a technical spec, a commitment with legal weight.

"If it transcribes accurately, it understood the meeting." Transcription accuracy and comprehension are two entirely different capabilities. A model can transcribe a sentence perfectly and still summarize its meaning incorrectly, because summarization is a separate reasoning step layered on top of the transcript, prone to the same kind of error any LLM makes when asked to condense nuanced or contradictory statements.

"A bot joining the call is basically invisible to attendees." It isn't, especially in client or candidate-facing meetings. A named AI notetaker in the participant list is a visible signal that the conversation is being recorded and possibly reviewed later, and some external participants will change how openly they speak because of it. This is part of why Granola's no-bot approach has found a following among consultants and lawyers.

"Free tiers are basically the same across tools." They're not close. Fathom's free plan gives unlimited recording with a hard cap on AI-processed summaries, Otter's free plan caps total minutes but not summaries, and Fireflies' free minute pool is shared across an entire team rather than allocated per person. Read the actual limit, not just the word "free," before you commit a team to one tool.

---

Use case recommendations

Solo professional or consultant: Granola. The hybrid note-taking model produces summaries that reflect what you personally found important, and the lack of a visible bot keeps client meetings feeling normal.

Sales team: Fireflies. The CRM sync and coaching analytics (talk-time, sentiment, monologue detection) pay for themselves once a team is running dozens of calls a week. Support teams that want automated replies rather than call notes can compare the best AI chatbots for customer service.

Engineering or product team: Otter or Fathom. Searchable meeting history matters more than fancy summaries when the real value is being able to find "when did we decide to deprecate that API" six months later.

Multilingual or globally distributed org: tl;dv, for its language breadth and the ability to clip and share specific moments across time zones without forcing a full re-watch.

Bootstrapped startup or freelancer on a tight budget: Fathom's free tier, which covers unlimited recording and light AI summary use without a credit card.

---

FAQ

Q: Should I tell meeting attendees they're being recorded? A: Yes, always, regardless of what state or country you're in. A meaningful number of US states require all-party consent by law, and even in one-party states, disclosing the recording up front is standard professional practice. All five tools listed here include a built-in consent notification you can enable.

Q: Can I use AI meeting notes for hiring interviews? A: Yes, with the candidate's explicit consent, and many companies already do this to standardize interview notes and reduce bias in hiring memos. Check local employment law before rolling it out broadly — the EU and some US states apply stricter rules to recordings involving job applicants than to general business meetings.

Q: Does any of these tools work for regular phone calls, not just video? A: Coverage is limited but growing. Otter's mobile app and Granola's newer iOS app both support in-person and phone-adjacent recording. For dedicated phone-call transcription at scale, dedicated call-center tools like Aircall or Gong are usually a better fit than a video-first meeting notetaker.

Q: Is a free tier good enough, or do I need to pay? A: For most solo users, yes. Otter's 300 minutes a month or Fathom's unlimited-recording free plan cover typical usage without a credit card. Teams running dozens of calls a week, or anyone needing CRM sync, will hit the free-tier ceiling within the first month or two.

Q: How accurate is AI transcription compared to a human transcriptionist? A: In our test, all five tools landed between 92% and 96% word accuracy on a clean, moderately technical meeting, which is competitive with a fast human transcriptionist working without playback control. Accuracy drops meaningfully with heavy accents, overlapping speech, or dense jargon that isn't in the model's training data.

Q: Can these tools replace a human note-taker entirely? A: For most routine meetings, yes. For anything with legal, medical, or high-stakes contractual weight, treat the AI output as a strong first draft that a human should still review before it's treated as an official record.

Q: Do these tools work with in-person meetings, not just video calls? A: Partially. Otter and Fathom both offer mobile apps built for recording in-person conversations, and Granola's 2026 iOS app extends its hybrid note-taking model beyond the desktop. tl;dv and Fireflies remain primarily built around joining a video call rather than standalone mobile recording.

Q: Does using these tools mean the vendor can train its AI on my private meetings? A: It depends on the vendor and your plan tier — check the specific opt-out policy rather than assuming. Granola defaults to opting customers out of model training; other vendors make the opt-out available but not automatic. This is worth confirming in writing for any client-sensitive or regulated work.

Q: What happens to the transcript if I cancel my subscription? A: This varies by vendor and isn't something we independently verified across all five for this piece — check each provider's current data retention and export policy before committing to a workflow you depend on, especially if you plan to keep years of searchable meeting history.

---

Related Articles

• Best AI Note-Taking Apps 2026
• Best AI Transcription Tools 2026
• Best ChatGPT Alternatives 2026

---

The Takeaway

Granola for solo professionals, Otter for teams that want the deepest free tier, Fireflies for sales orgs living inside a CRM, Fathom for budget-conscious solopreneurs, tl;dv for multilingual teams. The technology underneath all five — speech-to-text transcription, voiceprint-based speaker diarization, and LLM summarization layered on top — is similar enough that the real differentiator is workflow fit, not raw model quality. Pick based on how your team actually works, disclose the recording every time, and treat every AI summary as a fast first draft rather than the final word on what was decided.

Last verified: July 2026.