Quick Verdict
Otter and Fireflies both send a bot into your video calls to transcribe and summarize the conversation, but they're aimed at slightly different jobs. Otter's live transcription view, watching text appear in real time during the call, is genuinely useful for anyone who wants to follow along or search back mid-meeting. Fireflies leans harder into sales and revenue teams, with direct integrations into Salesforce and HubSpot that push conversation data straight into the CRM records that actually get used afterward.
Pros
- ✅ Otter's live transcription view updates in real time during the call
- ✅ Fireflies integrates directly with Salesforce, HubSpot, and Slack
- ✅ Both generate an automatic summary and action items right after a meeting ends
Cons
- ❌ Having a bot auto-join meetings requires granting calendar access, which some teams are wary of
- ❌ Free tiers on both cap transcription minutes per month
What Each Tool Does
Otter.ai joins meetings automatically (via its OtterPilot feature) and transcribes speech to text in real time, letting participants read along or jump back to a specific point in the conversation without waiting until the meeting ends. Its summary and action-item extraction happen right after the call, and its strongest use case is straightforward: better meeting notes with less manual effort.
Fireflies takes a similar transcription approach but built its product further around sales and revenue teams from the start. Its "Fred" bot joins calls the same way, but the platform's conversation intelligence features, tracking talk-to-listen ratio, keyword mentions, and deal-relevant topics, feed directly into CRM records, which matters more for a sales team reviewing call performance than for a general meeting note-taker.
Pricing
| Plan | Otter.ai | Fireflies.ai |
|---|---|---|
| Free | Limited monthly transcription minutes | Limited monthly transcription minutes |
| Individual/Pro | ~$16.99/month | ~$18/month |
| Business/Team | ~$30/user/month | ~$29/user/month |
How the transcription actually works
Both tools rely on automatic speech recognition combined with speaker diarization, the process of figuring out who said what by clustering segments of audio by voice characteristics rather than relying on video feeds. That's why speaker labeling gets noticeably less reliable in meetings with more than five or six participants, or when two people share similar vocal pitch. The diarization model simply has more voices to tell apart with less audio per speaker to learn from. Otter's real-time transcription streams this process live, updating the on-screen text within a second or two of someone speaking, while Fireflies processes more of its analysis, particularly the sales-specific conversation intelligence layer, after the call ends rather than live.
Summary and action-item extraction on both platforms runs a language model over the finished transcript rather than the raw audio, which is why summary quality tracks transcription quality closely. A meeting with garbled transcription because of poor audio produces a garbled or incomplete summary too, regardless of how good the underlying summarization model is.
Hands-on notes from testing
In testing during June 2026, we ran both tools across a mix of meeting types: a two-person one-on-one, a six-person team standup with some crosstalk, and a sales call with a customer whose accent was noticeable but clear. Both handled the one-on-one nearly flawlessly. The team standup is where the gap opened up: Otter's live view stayed readable through the crosstalk, though speaker labels occasionally swapped for a sentence or two when two people spoke over each other. Fireflies caught up on speaker attribution by the time the post-meeting summary generated, though some labels still needed a human review pass to fully correct. On the sales call, Fireflies' talk-to-listen ratio tracking and keyword flagging worked as advertised and would genuinely save a sales manager time reviewing calls manually, a feature Otter doesn't attempt to replicate.
Where each one comes up short
Otter's weakness is depth beyond transcription. Once you have the transcript and a summary, there's not much more the platform does with that data: no scoring, no trend tracking across meetings, no CRM-native workflow. For a general team that's fine, but it's a real gap for anyone hoping to extract more analytical value over time. Fireflies' interface is more cluttered as a direct result of packing in that extra analytical layer, and teams that just want clean meeting notes without sales-specific metrics in the way may find it noisier than necessary. Both tools also share a structural weak point: heavy crosstalk or low-quality audio, a bad microphone or a noisy room, degrades transcription accuracy meaningfully on either platform, and neither has solved that better than the other.
Which One Should You Use?
Choose Otter if your priority is straightforward meeting transcription and summaries across general team use, engineering standups, all-hands meetings, one-on-ones, without needing the data to feed anywhere beyond your notes app. Choose Fireflies if you're on a sales or revenue team and want conversation intelligence flowing directly into your CRM rather than living in a separate transcription app, since the CRM sync alone can save a rep real time on manual data entry after every call.
Frequently Asked Questions
Do these bots need to join every meeting automatically?
You can configure both to join automatically based on calendar events, or invite them manually to specific meetings only, depending on your privacy preferences.
Is transcription accuracy similar between the two?
Both perform well on clear audio with standard accents and degrade similarly with heavy crosstalk or poor audio quality, which is a limitation across most meeting transcription tools, not unique to either.
Which one is better for a sales team specifically?
Fireflies, mainly because of its deeper CRM integrations and conversation intelligence features built around sales-specific metrics like talk-to-listen ratio.
Does either tool work well for meetings in languages other than English?
Both support multiple languages, but accuracy is noticeably better in English than in less widely spoken languages, and heavily accented non-native English speech produces more transcription errors on both platforms than native speech does.
Can I edit the transcript after a meeting?
Yes, both let you correct misheard words or reassign a mislabeled speaker after the fact. Doing that periodically for recurring meetings can meaningfully improve accuracy on Otter specifically, which builds a rough voice profile from repeated corrections over time.
Final Verdict
Both tools solve the same basic problem well: better notes with less manual work. The deciding factor is what happens to that data afterward. General teams will likely prefer Otter's simplicity, while sales teams will get more value from Fireflies' CRM-native workflow.