Accuracy is not a single number: it depends on what you throw at it
Every vendor quotes a 95% or 98% word error rate in clean, studio conditions. But your audio is never clean. Accents, background noise, crosstalk, domain jargon (think ICD-10 codes in medical or RFC numbers in software), and speaker overlap all drag accuracy down hard. Real-world measurements are more sobering: accented speech typically lands in the low-to-mid 80s for accuracy, noisy in-room recordings fall further, and a four-way conversation with interruptions can drop below 75%. Before you commit, test each candidate with a sample of your own worst-case audio — a couple of speakers with heavy accents over a handset call — not with a crisp podcast clip. Accuracy is a function of your input, not the demo reel.The three transcription lanes and who they fit
Do not put a meeting recorder and an interview transcriber in the same bucket, because they optimize different things:- Meeting assistants sit in on your calls, join the calendar, generate transcripts plus automatic summaries/action items, and price per seat. Ideal for teams whose productivity problem is "what did we agree in that meeting."
- Interview & media transcribers handle longer audio/video, support dozens of languages, and output timestamps, speaker labels, and export to subtitles. Better for journalists, researchers, and creators.
- Raw transcription APIs return text/JSON from any audio at very low cost, but give you no summaries or meeting management. Best when you need automation inside your own product.
File a meeting, get a transcript: the bare workflow that works
Whatever tool you pick, this is the minimum loop that actually saves time, not just records it:- Transcribe or record the call automatically (join the calendar or upload the file; do not make someone press record).
- Spot-check timestamps and speaker labels after transcript generation; fix anything that will be quoted later.
- Extract action items and owners, and push them into your task manager while the context is fresh.
- Search the archive by keyword later — this is where a transcript earns its keep over a raw recording.
- Redact or restrict access to anything containing PII, client secrets, or internal numbers.
Transcription tools compared: real names, real pricing
| Platform / Tool | Key Features | Pricing |
|---|---|---|
| Otter.ai | Live meeting transcription, speaker ID, summaries/action items, joins Google/Teams/Zoom | Free: 300 min/mo; Pro ~$16.99/mo, Business ~$30/mo per user (annual) |
| Rev | AI and human transcription, captions, many languages, timestamps | AI as low as $0.25/min; human ~$1.50–$3/min; monthly plans from ~$13.99/mo |
| Descript | Transcript editing, overdub/studio audio, captions, cloning tools | Free: 1 hr/mo; Hobbyist ~$12/mo, Creator ~$24/mo (annual) |
| Whisper (OpenAI) | Open-source/local/API transcription, strong multilingual, no summaries | Free via open source (your compute); API from ~$0.006/min for whisper-1 |
| Fireflies.ai | Meeting recorder for calendar platforms, transcripts, Smart Search, workflows | Free: 800 min/workspace; Pro ~$18/mo per user, Business ~$29/mo (annual) |
| Happy Scribe | AI + human transcripts, subtitles, several languages | AI ~$0.20/min, human from ~$2/min (~$120/hr); subscriptions from ~$17/mo |
Privacy is the hidden cost nobody budgets for
Transcription uploads your most sensitive conversations to a third party, and only some vendors let you control that. Before you transcribe a board meeting, a therapy note, a legal consultation, or a client contract call, check four things: where the audio is processed (region), whether the vendor trains models on your data (opt out explicitly), retention policies (do they delete audio after transcription), and enterprise-level encryption and SSO. If audio cannot legally leave your jurisdiction, local or self-hosted Whisper is the only defensible route. Most teams discover this after a compliance scare, not before, which is why privacy should be a first-order criterion for hospitals, law firms, and finance teams.When transcription is really a meeting-productivity decision
If what you are transcribing is internal meetings, the tool choice is upstream of transcription: it is a decision about how your team manages follow-ups, decisions, and accountability. A transcript without a decision log is still mostly a record people stop reading. That is why meeting transcription belongs inside the broader conversation about AI meeting transcription and workflow automation, and it is why reviewing your whole AI transcription landscape before buying beats impulse-subscribing to whichever bot joined your first call.Do not over-tool: transcription is one layer of your stack
The transcript layer plugs into a larger AI toolkit. The searchability it gives you, the summaries, and the export to other systems matter only when your wider workflow is healthy. Whether you are an individual keeping a searchable archive or a team routing follow-ups into a task manager, the same principle applies: choose the leanest transcription layer that feeds your existing AI tool stack instead of a rival to it. Cross-site, teammates often find that the free online productivity tools list and the roundup together give a clearer picture of the free first step than any single vendor's pricing page.For more, check out: .
Frequently asked questions
Why is my transcription wrong on names and technical terms, and how do I fix it?
Generic models have no context for your business's proper nouns, product names, or industry jargon, so they fail precisely where you care most. Fix it with custom vocabulary: Otter lets you add custom terms, Rev supports glossary/name lists, and Fireflies allows custom word boost/blocking. Feeding your team roster and common product terms at setup dramatically cuts the number of names you have to edit manually afterward.
Is there a truly free way to transcribe recordings without a subscription?
Yes. OpenAI's Whisper runs fully free and locally via open-source packages on your own computer (no per-minute cost, total privacy), and its free-tier SaaS options like Otter (300 min/mo) or the Whisper API's tiny per-minute rate are near-free for light use. If you want no cloud at all, a local Whisper install plus a simple editor is the cheapest path to searchable transcripts.
How do I keep meeting transcripts private when my team joins from different countries?
Check where each vendor processes audio and whether regional processing is available; some offer EU-only data centers and enterprise data-residency options. For cross-border, high-sensitivity calls, use a local/self-hosted model or an enterprise plan with regional processing, and avoid sending recordings to consumer-tier cloud tools unless you have written confirmation they do not train on your audio.
Should I keep the raw audio after transcription or delete it?
Keep raw audio only if you retain the right to and the transcript's accuracy might be challenged later (legal, medical, financial). For most internal meetings, delete the raw audio after a verified transcript to shrink storage and reduce the data you expose; keep the transcript with timestamps so you can always revisit the source line without holding the whole file.
What determines the difference between meeting tools like Otter/Fireflies and generic APIs like Whisper?
It is the layer above raw text. Otter and Fireflies add meeting context, calendar integration, summaries, action items, and search optimized for conversations. Whisper returns raw text and nothing else. If you only need transcripts, the cheap raw layer wins; if you need to act on meetings (summaries, task routing), the extra ~$15–30/mo per user buys the middle layer that saves the actual hours.
For a Chinese-language walkthrough of picking between these same lanes for a Mandarin-speaking team, our guide to recommended AI tools in Chinese covers the same use-case-first logic.