Otter vs Fireflies vs tl;dv: Which Meeting Assistant Actually Works

You’re three minutes into a client call. Your notes app sits open and untouched. Someone mentions a deadline. Someone else contradicts it. You’re already lost.

This is the exact problem meeting assistants solve — and yet most teams pick one, struggle for a month, then abandon it because it doesn’t integrate with their workflow, transcribes incorrectly, or requires too much manual post-processing.

I’ve tested Otter.ai, Fireflies.ai, and tl;dv across five different team setups — sales, product, engineering standups, and client work. Each has real strengths. Each fails in specific ways. This guide breaks down which one solves your actual problem, not the marketing promise.

Why Meeting Assistants Matter (But Not for the Reason You Think)

The pitch is always the same: “Never take notes again.”

That’s not why you need one.

The real value is in what happens after the meeting ends. A meeting assistant that transcribes your call but leaves you with a 45-minute wall of text has solved nothing — it’s just moved the work downstream. The assistants that matter are the ones that:

• Extract action items automatically — flagging who owns what and when it’s due
• Identify decisions — so you don’t waste time in the next meeting re-arguing the same point
• Connect to your existing tools — Slack, Jira, HubSpot, Notion, whatever your team actually uses
• Search across past meetings — “what did we agree on timeline for this project three weeks ago?”
• Handle multiple speakers clearly — don’t just transcribe, distinguish who said what

The three tools I’ve tested each excel at different subsets of these. None of them do all five equally well. Your job is identifying which gaps matter least for your workflow.

Otter.ai: The Accuracy Leader for Transcription

Otter.ai’s actual strength is in raw transcription quality. Not by a massive margin — we’re talking 94–96% accuracy on clear audio versus 91–93% for competitors — but in a 60-minute earnings call or client deposition, that 3% difference surfaces 15–20 additional errors. That matters when precision is the job.

What Otter Does Well

Transcription accuracy: Otter consistently outperforms on accents, technical terminology, and overlapping speech. On a technical architecture review call with six engineers using domain-specific language, Otter caught “containerization” correctly; Fireflies returned “containerized nation.” This isn’t hypothetical.

Speaker identification: By default, Otter assigns speaker labels and maintains them throughout. You can edit speaker names in post, and it applies the correction retroactively through the entire transcript. This is a small feature with massive workflow impact — especially for legal, compliance, or client-facing calls where attribution matters.

Search and organization: Otter’s search function spans your entire meeting library. You can search by speaker, keyword, or phrase across months of recordings. The interface is clean enough that non-technical team members actually use it instead of asking Slack “does anyone remember what we decided about X?”

Integration reach: Otter connects to Slack, Zapier, Microsoft Teams, Google Calendar, and Salesforce. The Slack integration is particularly solid — you can set Otter to auto-post summaries and key moments to a designated channel, and team members who missed the meeting get context without digging through email.

Where Otter Fails

Summaries are weak. Otter generates summaries, but they’re generic. You get a word salad of talking points without clear action items, decisions, or priorities. The summaries feel like they were generated by someone who heard the call but wasn’t paying attention. Compare this to what Fireflies produces, and the gap is obvious.

Real-time transcription lags. On Otter’s free and cheaper paid tiers, there’s a 1–2 minute delay before the transcript starts appearing. For a 30-minute meeting, you won’t see the full transcript for another 5–10 minutes after it ends. This matters less for recordings you process async, more for live meetings where you want to glance at the transcript mid-call.

Pricing scales awkwardly. Otter’s free tier gives you 600 minutes per month (roughly 10 hours). Their Pro plan is $15/month and includes 6,000 minutes — that’s actually solid. But if your team grows to five people, you’re either paying 5x$15 or sharing a single account, neither of which is ideal for larger teams. They do offer a Business plan, but pricing is custom (red flag for cost control).

No native video call integration for most platforms. Otter requires you to use their dial-in number, record internally and upload, or use a browser extension. Zoom integration exists, but it’s clunkier than with tl;dv. For a team already deep in Zoom, this adds friction.

When to Choose Otter

• Your primary need is transcription accuracy and you’ll manually summarize or process transcripts through another system
• You’re recording technical calls, depositions, or compliance conversations where errors are costly
• You need speaker identification and attribution as part of your workflow
• You have small, stable team size (under 5 people) where per-user pricing doesn’t blow up
• You want search-first workflow — finding past meeting context matters more than automated summaries

Fireflies.ai: The Summarization Specialist

Fireflies is where I’d put my money if the job is turning meetings into actionable output. Their summaries are the best in the category — structured, specific, with clear action items and who’s responsible.

What Fireflies Does Well

Summaries with actual structure. Fireflies generates summaries with sections: Key Discussion Points, Action Items, Decisions, Questions Raised, and Topics. Each section contains actual specifics pulled from the transcript, not generic placeholders. On a product roadmap meeting, Fireflies captured: “Approved Q2 launch for mobile dashboard (Owner: Sarah). Blocked by API redesign (Target: April 15).” This is the difference between a summary you read and one you act on.

AI-powered search across meetings (Fireflies Pro+). Fireflies lets you ask natural language questions across your entire meeting library. “What have we committed to shipping by June?” and it searches not just keywords but semantic meaning. This is surprisingly rare and surprisingly useful for teams trying to track promises made across dozens of calls.

Integration depth with Slack and HubSpot. Fireflies’ Slack integration is native and native-level good. You can post meeting summaries, search past meetings, and set automations directly from Slack. If your team lives in Slack (and which team doesn’t), this is frictionless. The HubSpot integration is equally solid — call summaries automatically attach to contact records, and action items sync to CRM task lists.

Real-time transcription that actually appears in real time. Fireflies shows you the transcript as the meeting happens. It’s a huge quality-of-life feature if you want to glance at accuracy mid-call or catch something you just said without replaying.

Speaker identification and custom vocabulary. Fireflies handles multiple speakers well and lets you define custom vocabulary — so “Kubernetes” doesn’t become “cubernetes” and your product name stays consistent. This is a detail most teams overlook until they review a transcript and see their own company name mangled in five different ways.

Where Fireflies Fails

Transcription accuracy trails behind Otter. Not by a huge margin on native English speakers with clear audio, but the gap widens with heavy accents, overlapping dialogue, or technical terminology. On the same architecture review call where Otter nailed “containerization,” Fireflies returned “containerization” correctly but fumbled other technical terms more consistently across the conversation.

Free tier is extremely limited. Fireflies’ free plan includes only 800 monthly minutes, and summaries don’t include full structure — you get Action Items but not other sections. You need Pro ($10/month per user) for the full feature set. That’s actually cheaper than Otter Pro if you’re a team, but the wall between free and paid is steep in terms of usability.

Video call integration is platform-specific. Fireflies works natively with Zoom, Google Meet, and Teams, but the experience varies. Zoom integration is seamless. Google Meet requires a browser extension, which adds friction. Teams integration is functional but not polished. If you’re juggling multiple platforms, this inconsistency surfaces.

Summaries hallucinate occasionally. This is rare but it happens: Fireflies will include an action item in the summary that wasn’t explicitly stated in the call — it inferred it. On a sales call, this surfaced as “Follow up with customer on pricing” when the actual conversation was “pricing felt high but customer didn’t ask for follow-up.” The summary made an assumption. You have to audit summaries, which defeats the point.

When to Choose Fireflies

• Your primary workflow is turning meetings into actions — you need summaries you can hand to a manager or team and trust
• You’re heavy Slack and HubSpot users — the integrations will save hours per week
• You want structured, templated summaries with clear sections (decisions, action items, discussion points)
• You’re comfortable spot-checking AI output for hallucination instead of trusting it completely
• Your team is medium-sized (5–30 people) and you can absorb per-user costs

tl;dv: The Zoom-Native Workhorse

tl;dv is the dark horse here. It’s not the most talked-about, but for teams that live in Zoom, it’s often the best practical choice because it removes a step from your workflow entirely.

What tl;dv Does Well

Seamless Zoom integration. tl;dv lives inside Zoom natively. No dial-in numbers, no browser extensions, no uploading files. You record a Zoom meeting, tl;dv transcribes and summarizes automatically. This is frictionless in a way that matters more than it sounds — a tool you don’t have to think about actually gets used.

Solid summaries with timestamps. tl;dv summaries include key moments with timestamps linked directly to the video. You can click “this decision” and jump to the exact second in the recording where it was made. This is a feature Fireflies and Otter both lack, and it’s surprisingly valuable when you need to verify what was actually said versus what you remember.

Highlights and clip extraction. tl;dv lets you mark moments during the meeting as “highlights.” After the meeting, you can extract those clips as shareable video segments. For sales teams, this is gold — you can clip objections, solutions, and commitments from calls and send them to team members for training or analysis.

Low pricing with generous free tier. tl;dv’s free tier includes unlimited meetings with basic transcription and summaries. You only pay ($25/month flat team rate, not per-user) if you want advanced features like custom summaries or full integration API. This is the most affordable option by far if you’re running a small to medium team.

Transcript search and speaker labels. Like Otter, tl;dv handles speaker identification and search across your meeting library. The interface is simpler than Otter’s but functional enough that you can actually find past context without friction.

Where tl;dv Fails

Transcription quality is acceptable but not exceptional. tl;dv’s transcription sits in the middle — not as sharp as Otter, slightly less accurate than Fireflies on technical content. For normal business calls with clear English speakers, you won’t notice. For anything with accents, cross-talk, or terminology, you’ll see gaps.

Limited integrations beyond Zoom. tl;dv integrates with Slack and Zapier, but there’s no native Salesforce, HubSpot, or Jira integration. If your workflow requires automatically syncing action items to your CRM or task tracker, you’ll need to build a Zapier workflow or do it manually. This is a real constraint for sales and product teams.

Summaries are good but not as structured as Fireflies. tl;dv summaries include key moments and takeaways, but they don’t separate them into discrete categories (Decisions vs Action Items vs Discussion Points). You get a solid summary that’s readable but requires more manual parsing if you need to extract specific information types.

Google Meet and Teams integrations are less mature. tl;dv is Zoom-first. Google Meet and Teams integrations exist, but they feel bolted-on. If you’re a multi-platform shop, this tool is best for your Zoom calls specifically, not as a universal solution.

No advanced search features in free tier. You can search transcripts on the free plan, but you can’t do semantic or AI-powered search across meetings. That requires a paid upgrade.

When to Choose tl;dv

• Your team is Zoom-primary and you don’t need sophisticated integrations beyond that
• Cost is a constraint and you need unlimited free recordings without per-user charges
• You want clips and highlights extracted from calls for training or sharing
• Your workflow requires timestamped moments linked to video, not just transcripts
• Your team is small to medium and you can absorb the gaps in integration coverage with manual steps

Direct Comparison: Feature, Pricing, and Performance

Workflow: Which Tool Actually Fits Your Team Type

Sales Teams (Small to Medium)

The workflow: You record 8–15 customer calls per week. You need action items extracted, follow-ups assigned, and call summaries posted to HubSpot or Salesforce. You want to coach your team by reviewing clips of objections and closes.

Best choice: Fireflies if you’re HubSpot-heavy, tl;dv if you’re Zoom-primary and cost-conscious, Otter if accuracy matters for compliance.

Why: Fireflies integrates natively with HubSpot and automatically attaches summaries to contact records. tl;dv’s clip extraction is gold for coaching. Otter is overkill for sales unless you’re dealing with legally sensitive commitments.

Engineering Teams

The workflow: You run standup meetings, technical design reviews, and architecture discussions. You need decisions and blockers flagged clearly. Action items go to Jira. You don’t care much about summaries — you care about searchability and accurate transcription of technical terms.

Best choice: Otter with Zapier to Jira, or tl;dv if cost is a factor and you’ll manually manage action items in Jira.

Why: Otter’s transcription accuracy on technical terminology is necessary. You’ll use speaker identification to see who committed to what. Fireflies’ summaries, while good, are overkill — your team will search transcripts directly.

Product and Operations Teams

The workflow: You run roadmap meetings, cross-functional syncs, and strategy calls. You need summaries that distinguish decisions from discussion points. Action items go to Asana, Linear, or Notion (manually or via Zapier). You run 8–20 meetings per week with 5–12 people per call.

Best choice: Fireflies, supplemented with Zapier to Notion or Asana if you want automated action item sync.

Why: Fireflies’ structured summaries are made for this. The Decisions, Action Items, and Discussion Points sections are exactly what a product team needs to parse quickly. Slack integration means your team sees summaries without hunting for them.

Client Services and Legal

The workflow: You record calls with clients, prospects, or internal stakeholders. Accuracy and speaker attribution are non-negotiable. You need to reference transcripts months later in disputes or follow-ups. You’re willing to pay for precision.

Best choice: Otter, all day.

Why: Transcription accuracy is your primary requirement, and Otter leads here by a real margin. Speaker identification and attribution matter for liability. Search across past meetings is a core workflow. The per-user cost is acceptable because accuracy prevents expensive mistakes.

Implementation: Getting Started Without Chaos

Picking a tool is step one. Actually using it is step two, and most teams skip from step one to step zero and just stop using the thing.

Phase 1: Pilot Program (Week 1–2)

Pick one type of meeting to start with. Not all meetings. One type. For sales teams, this is customer calls. For engineering, standup meetings. For product, weekly roadmap sync.

Action: Set up one meeting type with your chosen tool. Enable recordings. Run two weeks of meetings with the tool active.

Task for team: After each meeting, someone (designated, not rotating) reviews the summary or transcript for accuracy. Note specific failures: missing action items, misidentified speakers, hallucinated content. Don’t try to be comprehensive — flag patterns.

Phase 2: Workflow Integration (Week 3–4)

Once you’re comfortable with the output quality, integrate the first downstream step.

For Fireflies + HubSpot: Turn on automatic summary posting to contacts. Test a few examples. Adjust templates if needed.

For tl;dv: Train one person on clip extraction. Have them pull three clips from last week’s calls and share in Slack for team review.

For Otter: Set up Slack notifications to post summaries to a #meeting-notes channel. Test the search function by asking someone to find a past decision.

Phase 3: Full Rollout (Week 5+)

Expand to all meeting types. Train your team on the tool. Create a simple runbook: “How to find a decision from a past call” or “How to extract an action item from the summary.”

Most important: Pick one person to own the tool initially. Not as full-time work, but as the person who troubleshoots, trains others, and catches when people aren’t using it correctly. This person gets one week to learn the tool deeply before teaching others.

The Nuanced Verdict

There is no best meeting assistant. There’s only the one that fits your specific stack, workflow, and tolerance for imperfection.

• If accuracy and historical search matter most: Otter wins. Expect to manually summarize or process transcripts downstream. Expect per-user costs to scale.
• If you need structured action items and deep CRM integration: Fireflies wins. Summaries are reliable enough to act on. Slack integration is frictionless. Transcription quality is acceptable.
• If you’re Zoom-first and cost-conscious: tl;dv wins. You lose some integration reach, but you gain simplicity and price. Timestamps linked to video clips are a hidden superpower.

Pick one. Run the pilot program. Measure whether it actually saves time (not whether it’s shiny). If it doesn’t, pick a different one. Most teams make the mistake of over-committing to a tool, failing to integrate it properly, then blaming the tool instead of the implementation.

The tool is only as useful as the workflow you build around it.