After using Sembly for a year, we switched to Fireflies.ai six months ago. The main drivers were cost and the promise of better integration with our Slack workflows. Now that we have enough data, I wanted to share a concrete comparison, especially on accuracy.
For accuracy, Fireflies is noticeably better at capturing specific technical terms and product names common in our SaaS support calls. Sembly would often mangle those into generic words. However, for non-native English speakers on our team, both still struggle at times, with maybe a 10-15% error rate in dense passages. The biggest win is cost – Fireflies’ unlimited transcription tier for a flat fee saved us about 30% compared to Sembly’s per-seat model as our team grew.
Has anyone else made a similar switch? I’m curious if others have found the same trade-offs, or if there are tricks to improve accuracy we might have missed. Also, how do you handle the review process for action items? Fireflies’ workflow feels faster, but I’m still not sure we’re using it to its full potential.
I run ops for a 45-person dev shop, and we've trialed both platforms for our client standup recordings and internal post-mortems over the last 18 months. We currently use Fireflies.ai in production for all scheduled meetings.
* **Real Pricing & The 'Unlimited' Trap**: Fireflies' flat fee is only a win until you hit the participant cap. Their $19/mo 'Business' plan covers 25 attendees per meeting. We had two all-hands calls that breached that, and the transcription was truncated. Sembly's per-seat model scales predictably, but you pay for listeners, not just speakers. At my last shop (80 people), Sembly ran about $9/user/mo on an annual contract.
* **Accuracy Nuance**: Your 10-15% error rate on dense, accented English tracks. We found Fireflies better on tech jargon, as you said, but Sembly consistently outperformed on financial acronyms (EBITDA, ARR) and code syntax mentioned aloud (like `if err != nil`). It's domain-specific. Neither cracked 95% for us in noisy, cross-talk scenarios.
* **Integration 'Ease' vs. Lock-in**: Fireflies pushes you into their Slack/Discord bot workflow, which is fast until you need the data elsewhere. Exporting cleaned transcripts to our wiki required a manual step or using their API, which counts against a separate limit. Sembly's Google Drive sync felt clunky but gave us raw .txt files in a folder we controlled.
* **Post-Meeting Workflow Friction**: The action item review is where both fall short. Fireflies' speed comes from pre-defined "Smart Search" filters, but training them on our internal project names took a month of corrections. Sembly's review UI was slower but allowed team-wide commenting in a shared view, which we lost in the switch.
I'd recommend Fireflies only if your meetings are under 25 people and your jargon is more "SaaS" than "finance" or "code." If you need the raw data exported weekly for an audit trail or your team relies heavily on collaborative note-editing, Sembly's model, while pricier, creates less vendor friction. Tell us your average meeting size and where you store final meeting minutes, and the choice gets clearer.
If it's free, you're the product. If it's expensive, you're still the product.
Your point about the participant cap is critical, and it's a classic case of unit economics being misunderstood. Fireflies' pricing isn't truly a flat fee; it's a fee *per meeting*, capped by attendees. That changes the cost calculation entirely. For a company with frequent large meetings, the effective cost per transcribed minute could skyrocket past Sembly's per-seat model.
On the domain-specific accuracy, that's fascinating. It suggests the underlying speech models are trained on different corpuses. Fireflies likely uses more tech/startup material, while Sembly might have a stronger footing in finance and structured language like code. This makes a pure accuracy percentage somewhat meaningless without defining the audio's domain.
The export lock-in you mentioned is the hidden operational cost. When data mobility is restricted, the switching cost for your next vendor evaluation increases, which often offsets any initial subscription savings. Have you quantified the manual effort required to work around their export limitations?
Your observation about Fireflies handling technical jargon better aligns with what I've seen in the support context. That specific accuracy is a huge time-saver when you're later searching transcripts for bug names or feature flags mentioned in a call.
On the cost front, your 30% saving with the flat fee tracks, but only if your meeting sizes stay consistent. As your team grows, the per-seat model eventually becomes a liability, but Fireflies' participant caps can become a hard ceiling. It's a trade-off between predictable scaling and predictable output.
For the review process, we built a simple checklist into our workflow that might help you. After Fireflies generates the summary, we have a team member skim specifically for action items and due dates flagged in the transcript. We paste those into a dedicated Slack channel using their integration, which creates a lightweight audit trail without needing to live inside the Fireflies app. Have you found any specific features within their workflow, like the topic tracking, that actually stick?
Support is a product, not a department.
Your 10-15% error rate for accented English is the floor. We see spikes to 25% in our incident bridges when people talk fast under pressure. No magic trick fixes that.
For action items, the review step is mandatory. We skip their AI summary entirely. I set a filter to highlight sentences with "we should" and "I'll" in the raw transcript, then copy those lines into a ticket manually. Takes two minutes and it's never wrong.
On cost, that 30% saving disappears if you ever need the transcript as raw text via their API. The pricing is opaque and they charge per export. Sembly's API was cleaner.
slow pipelines make me cranky
Your point about error rates spiking to 25% under pressure is critical. It highlights that the accuracy metric is dynamic, not static, and heavily dependent on audio quality and speaker state. This makes a single "accuracy percentage" for a platform almost useless without the context of the meeting type.
I completely agree about skipping the AI summary. We've had similar success with a simple regex filter on the raw transcript text, though we look for patterns like "btodob" and "bwillb by (mon|tue|wed)" instead. The key is treating the AI output as a structured data source to parse, not a narrative to trust.
On the API cost, you've exposed a major hidden variable. The per-export fee for raw text fundamentally changes the TCO for any automated pipeline. Sembly's cleaner API with predictable per-minute costs was indeed better for that use case. It's a trade-off: you pay for convenience in the UI, but you're penalized for trying to extract your own data programmatically.
Data over dogma