Best AI Meeting Assistants in 2026: Seven Tools Compared on Accuracy, CRM Depth and EU Compliance

Key takeaways

• Section 201 of the German Criminal Code makes recording another person's non-publicly spoken word without authorisation punishable by up to three years' imprisonment or a fine, even when you are on the call.
• Zoom's "Zoom Models Only" mode keeps AI processing inside the account's provisioned region, the EU included, and Microsoft says EU Copilot traffic stays inside the EU Data Boundary — though Anthropic subprocessor models are excluded from it. Fireflies, Otter, Fathom and Granola all publish US hosting.
• Otter's privacy policy says it trains its proprietary AI on de-identified audio and transcriptions; Fireflies' policy says it does not use personal information for training.
• Fireflies covers transcription in the plan but meters AI Skills, custom summary sections and CRM autofill in credits, sold from $5 for 50 (Fireflies knowledge base, August 2026).
• None of Fireflies, Otter, Fathom, Granola, Zoom or Microsoft publishes a word error rate against a named public test set, so every transcription accuracy percentage an AI meeting assistant quotes is an unreproducible private benchmark.

Every figure comes from the vendor's own pricing page, documentation or privacy policy, checked August 2026. Prices are annual billing per seat unless stated.

The scope here is capture, transcript quality and the legal footing for recording. Routing the resulting action items into a task system is a different problem, covered in our comparison of AI project management tools and meeting-to-task integrations.

An accuracy figure means nothing without the test set and the scoring rules, and none of the seven publishes either. Treat "99% accurate" as marketing copy.

Word error rate is fragile in three ways. The audio dominates: Whisper large-v3 is commonly reported at around 2.7% WER on LibriSpeech test-clean — read audiobook speech, one speaker, no crosstalk — and materially worse on multi-party meeting corpora such as AMI. The Open ASR Leaderboard publishes both columns for the same model, which is the comparison to look at; a vendor quoting a single accuracy number has told you nothing about which of those two conditions your calls resemble. Normalisation matters — stripping fillers, expanding contractions and canonicalising numbers before scoring moves the result by percentage points. And long audio is often chunked for evaluation, which is not the task of a 55-minute call. The Open ASR Leaderboard added a long-form track in 2026 for that reason.

These products also largely rent the same engines: Granola's security page names Deepgram and AssemblyAI for transcription, and Fathom names Anthropic, OpenAI and Google. You are buying an orchestration layer and a governance posture, not an acoustic model. If accuracy on your audio is decisive, run the same three recordings through each free tier and score them.

Diarisation — deciding who spoke when — fails far more often than word recognition, and it is the error users notice, because it corrupts the action items downstream.

Overlapped speech is the cause. The AMI corpus, the standard benchmark for multi-party business meetings, is built around multi-party conversation with substantial overlapping speech, and diarisation error rates reported on it in the speech-recognition literature run far above the word error rates vendors advertise. No vendor here publishes a diarisation number at all, on AMI or anything else. Misattribution is normal, not exceptional.

Check two things before buying: whether correcting a speaker label once propagates across the transcript, and whether the tool can consume per-participant audio streams. Separate streams sidestep overlap entirely, a structural advantage for platform-native tools over bots capturing one mixed track.

Vendors blur two questions: how many languages the transcriber handles, and how many the summariser writes in. The second list is always shorter.

Fireflies advertises transcription in 100-plus languages on every tier. Fathom's help centre states 38 transcription languages but automatic summary translation in six — Spanish, Portuguese, German, French, Italian and Dutch. Otter's pricing page lists English, Spanish, French, German, Japanese and Chinese. Granola publishes no count. Zoom took meeting summaries to 36 languages in preview in a February 2024 announcement. Teams Premium includes live translation of captions and transcripts.

For an EU team the count is not the test. The test is whether a German meeting yields a German summary and whether your product names survive; custom vocabulary settings materially affect proper-noun accuracy.

All seven capture audio, produce a speaker-labelled transcript and generate a structured summary. They differ in how they get the audio, and that choice is a compliance decision before it is a feature.

A participant bot joins the call — Fireflies, Otter and Fathom by default — or the tool reads the host machine's system audio, as Granola does and Fathom's bot-free mode. Zoom AI Companion and Teams recap run inside the conferencing platform itself. Platform-native tools inherit the conferencing vendor's residency and retention controls; bot-based tools create a second copy of the meeting in a second vendor's cloud under a second retention policy. And the bot is the clearest notice other participants get, so removing it makes your own disclosure more important, not less.

Granola is built around the bot-free model — its product page states it "Uses your computer audio, so doesn't invite a bot" — and runs on macOS, Windows, iOS and Android, which also makes it the only tool here designed for in-person meetings. Fathom offers a choice between bot-free capture, in beta, and a conventional bot.

Under German law the distinction is thinner than it looks: transcripts are typically buffered on the provider's servers, and that temporary storage is itself a recording under Section 201 StGB. Going bot-free does not remove the consent requirement.

Action-item quality comes down to three inspectable things: transcript fidelity, speaker attribution, and whether summaries are template-driven or free-form.

Template control shows up directly in pricing. Fathom's Premium tier adds 15-plus expert summary templates and AI-generated action items. Fireflies exposes 100-plus templates through AI Skills and allows custom summary sections, but custom sections consume AI credits while stock templates do not. Microsoft gates custom summary templates behind a Microsoft 365 Copilot licence, while standard intelligent recap works with either Teams Premium or Copilot.

Then there is where the task goes. Fireflies' Tasks feature is always on and consumes no credits. Granola's Business tier adds MCP alongside Notion, Slack, HubSpot, Zapier, Attio and Affinity — see our Model Context Protocol guide. For conditional routing, Zapier and Make add branching logic the native integrations lack.

Integration counts are close to meaningless. What matters is whether the tool writes structured fields back into your CRM or just attaches a note, and how many seats may do it.

Fireflies is the strongest standalone here: 100-plus integrations, named CRM connectors for Salesforce, HubSpot, Affinity, Redtail and Wealthbox, plus dialers including OpenPhone, Zoom Phone, RingCentral and Aircall. Its CRM autofill is credit-metered, the detail to model before rollout.

Fathom's structure is easy to misread: CRM syncing is capped at three users on Free, Premium and Team, and field sync that auto-updates records only appears on Business at $25 per user per month annually. Otter lists Salesforce, HubSpot and Zapier from Pro with API access on Enterprise only; Granola's integrations, API and MCP access arrive at Business.

If you record German participants, the binding constraint is criminal law, not GDPR. Section 201 StGB makes it an offence to record another person's non-publicly spoken word without authorisation, punishable by up to three years' imprisonment or a fine. It applies whether or not you are a participant, which defeats the assumption that being on the call gives you the right to record it.

The two regimes have different thresholds. For criminal liability, tacit or presumed consent is generally sufficient — a clear announcement nobody objects to typically clears the Section 201 bar. GDPR is stricter: German commentary on transcription software concludes that consent under Article 6(1)(a) is normally the only workable lawful basis, and that it must be explicit, informed, freely given and revocable. In employment, "freely given" is the hard part, because Section 26 BDSG subjects employee consent to scrutiny given the power imbalance. For employers with a works council, a works agreement covering transcription software supplies a basis without individual consent.

The EU AI Act layer

Article 5(1)(f) has prohibited using AI systems to infer emotions of a natural person in the workplace since 2 February 2025, outside medical and safety purposes, with penalties reaching €35 million or 7% of global annual turnover. It is framed around inference from biometric data, so voice-derived emotion scoring applied to employees is the risky case. Microsoft's Copilot documentation states it restricts generative AI from inferring an employee's performance, attitude or emotional state. Article 50's transparency obligations are the second layer — see our guide to the August 2026 EU AI Act deadlines.

Litigation risk is not hypothetical

Otter is the defendant in a consolidated class action in the Northern District of California (In re Otter.AI Privacy Litigation, lead case Brewer v. Otter.ai, Inc., No. 5:25-cv-06911, filed 15 August 2025) asserting federal Wiretap Act and California Invasion of Privacy Act claims. The plaintiff alleges he was recorded on a sales call by another participant's OtterPilot with no account and no chance to decline; the motion to dismiss was pending as of mid-2026. That fact pattern is why your consent script must cover external guests.

Only three of the seven publish an in-region EU processing option. If EU residency is a hard requirement, that narrows the field before you compare features.

Zoom documents three AI deployment models. "Zoom Models Only" keeps processing inside the account's provisioned region — US, EU, Singapore, Saudi Arabia, Australia, India or Canada — with no third-party model providers. A second runs Anthropic models through Amazon Bedrock inside Zoom's trust boundary, available in the US and EU and currently the default for European customers. "Federated AI" spans Anthropic, OpenAI and Perplexity and may involve global processing including the US. Feature coverage widens as you move down that list, so residency and capability trade off explicitly.

Microsoft's Copilot privacy documentation, updated 9 July 2026, states that LLM calls route to the closest data centres but can call into other regions when capacity is tight, and that EU traffic stays within the EU Data Boundary — with the caveat that Anthropic subprocessor models are currently excluded.

The four standalone tools all publish US infrastructure: Otter on AWS in the US with Standard Contractual Clauses, Fathom "housed in the United States", Granola in a US-hosted AWS VPC, and Fireflies on servers in the US "and other countries" under the EU-US Data Privacy Framework.

tl;dv is the only European-headquartered vendor in this comparison, and it states that it offers an EU data residency option, publishes its subprocessors and lets customers disable AI training on meeting content. Those claims come from tl;dv’s own blog rather than a formal trust centre or a third-party audit report, which is weaker evidence than Zoom’s and Microsoft’s published documentation. For an EU business that makes it a reasonable shortlist candidate, but get the residency commitment written into the DPA rather than relying on a marketing page.

This is the sharpest dividing line in the category. Three say no outright; three train on de-identified data with varying defaults.

• Fireflies: "We do not use personal information for AI model training and we contractually prohibit our vendors from using this information for their own model training," plus a zero-data-retention arrangement for meeting content.
• Zoom: does not use customer audio, video, chat or attachments to train Zoom's or its third-party AI models, across all three deployment models.
• Microsoft: prompts, responses and Microsoft Graph data are not used to train foundation LLMs, and Copilot has opted out of Azure OpenAI abuse monitoring.
• Otter: trains "our proprietary AI technology on de-identified audio recordings and on transcriptions (which may contain Personal Information)". Opt-out, not opt-in.
• Fathom: no AI sub-processor may train on user data, but Fathom uses de-identified customer data to improve its own models, with an opt-out in settings.
• Granola: trains on anonymised user data while barring OpenAI and Anthropic from doing so. Individuals opt out in Settings; Enterprise has it disabled by default, org-wide.

Note the pattern: "we don't train on your data" often means only that the sub-processors don't. Check whether the vendor's own models are covered, and treat de-identification sceptically — cadence and accent carry identifying signal.

Retention control is where the compliance story holds or falls apart, and in every one of these products the meaningful controls sit on the top tier.

Fireflies puts custom data retention, private storage, audit logs and SSO with SCIM on Enterprise only, at $39 per seat per month annually; below that, storage is a lifetime cap — 400 minutes team-wide on Free, 8,000 per seat on Pro — rather than a retention policy. Otter gives Enterprise admins custom retention with data removed within 48 hours of the configured period, a workspace toggle to strip audio while keeping transcripts, and central deletion that propagates to individual accounts. Fathom lists custom retention on Enterprise; Granola puts org-wide auto-deletion on its $35 tier. Microsoft governs Copilot interaction data through Purview retention policies alongside the rest of your Microsoft 365 estate.

The question to put to any vendor: when a participant exercises an Article 17 erasure request, what gets deleted, from where, within what window, and does it propagate to summaries, embeddings and CRM notes?

Headline per-seat prices are close enough across the standalone tools to be a wash. The variance is in what is metered on top.

Fireflies charges $10, $19 and $39 per seat per month billed annually for Pro, Business and Enterprise. Transcription, note-taking and recap emails are covered by the plan; AI Skills, Daily Digest, Meeting Prep, custom summary sections and Autofill CRM consume credits. Plans include a one-time allocation that does not expire — 20 on Free and Pro, 30 on Business, 50 on Enterprise — after which packs start at $5 for 50 credits, unused purchased credits do not carry over, and running out triggers an automatic subscription to the lowest credit tier unless you disable it.

Otter is $8.33 Pro and $19.99 Business annually with no overage mechanism: hit the 300-minute Basic or 1,200-minute Pro cap and transcription stops until the cycle resets. Fathom is $15 per user Team and $25 Business annually, two-seat minimum; Granola is $14 Business and $35 Enterprise. Zoom AI Companion adds nothing to a paid Zoom account; Teams Premium is $10 per user per month yearly; Microsoft 365 Copilot Business is $18 promotional through September 2026, reverting to $21, on top of a qualifying base plan. Our pricing breakdown covers the wider category.

tl;dv is the one tool here whose pricing could not be verified at source. Its free tier is unusually generous on volume — unlimited recordings and transcription — but the metering sits elsewhere: AI notes and AI prompts are capped, and free-tier recordings are deleted after a retention window rather than kept. Independent reviews published in 2026 put the paid tiers in the same per-seat band as Fathom and Granola, but tl;dv’s own pricing page returned an error when checked in August 2026, so treat any figure you read for it, here or anywhere else, as unconfirmed until you see it inside the product.

Work the constraints in order: lawful basis first, residency second, integration depth third, price last.

Otter remains a capable individual notetaker, but its combination of default training on de-identified recordings, US-only hosting and active wiretapping litigation makes it the hardest of the seven for an EU business to sign off on. Whatever you pick, write a consent script covering external guests, decide who owns retention, and check whether a works agreement beats individual consent. For the model layer underneath, see our comparison of the leading AI models in 2026.