The question is what you need beyond the transcript
All three produce accurate transcripts of clean audio. Where they separate is everything around the transcript: whether it arrives in real time, whether speakers are labelled, whether the service handles poor audio gracefully, and whether you or a vendor operates it.
✓ Quick answer
- Best for real-time streaming: Deepgram — lowest latency of the three
- Best feature set on top of transcription: AssemblyAI — speaker labels, summarisation, topic detection
- Best cost and control: Whisper — free, open weights, runs offline
- Best for sensitive audio: Whisper self-hosted — nothing leaves your infrastructure
- Best accuracy on difficult audio: The paid APIs, generally — accents, overlap and background noise are where they earn their price
Whisper
OpenAI's Whisper is open source and free to run. On clean audio in a well-supported language it is genuinely excellent — competitive with paid services and, for many use cases, indistinguishable from them.
The advantages beyond cost are control and privacy. Whisper runs entirely on your own hardware, which means audio never leaves your infrastructure — decisive for legal recordings, medical audio, or anything under a data-residency requirement. It also runs offline, which the API-based alternatives cannot.
The gaps: no speaker labelling out of the box, no real-time streaming in the base implementation, and you own the infrastructure and the GPU cost. There are community projects addressing each of these, but each is another thing to run and maintain.
AssemblyAI
AssemblyAI is the most feature-complete of the three. Speaker labelling, chapter detection, summarisation, sentiment, topic extraction and content moderation are available as part of the transcription call rather than as separate downstream processing.
For applications where the transcript is an input to something else — meeting summaries, call analytics, content indexing — that integration saves meaningful engineering work. Accuracy on difficult audio is strong, and the API is well documented.
Best for: teams building products on top of transcription rather than teams who just need text out of audio.
Deepgram
Deepgram optimises for speed. Its real-time streaming latency is the lowest of the three, which matters for live captioning, voice interfaces and any application where a delay between speech and text is user-visible.
It also handles domain-specific vocabulary well through custom model training — useful for industries with heavy jargon where general models mis-transcribe technical terms consistently. For high-volume call centre transcription, that combination of speed and domain tuning is the reason teams choose it.
Best for: real-time applications, high-volume streaming, and domains with specialised vocabulary.
Which to choose
| If you need… | Use | Why |
|---|
| Live captions or a voice interface | Deepgram | Lowest streaming latency |
| Speaker labels and summaries | AssemblyAI | Built into the transcription call |
| Zero per-minute cost at volume | Whisper | Free; you pay in compute and ops |
| Audio that cannot leave your servers | Whisper | Fully self-hosted, works offline |
| Accuracy on accents and noisy audio | AssemblyAI or Deepgram | Where the paid services earn their price |
| Heavy industry-specific vocabulary | Deepgram | Custom model training on your terms |
Cost reality
Whisper being free is real but incomplete. At low volume, a paid API is almost always cheaper than the engineering time to deploy and maintain a Whisper service. The crossover comes at sustained volume — once you are transcribing enough hours per month that per-minute pricing becomes a meaningful line item, self-hosting starts paying for itself. Work out where that crossover sits for your actual volume before assuming free means cheaper.
Frequently asked questions
Is Whisper as accurate as paid transcription APIs?
On clean audio in well-supported languages, close enough that the difference rarely matters. The paid services pull ahead on difficult audio — strong accents, overlapping speakers, background noise, and specialised vocabulary. If your audio is consistently clean, Whisper is competitive.
Can Whisper do real-time transcription?
Not in the base implementation — Whisper processes complete audio files. Community projects add streaming behaviour by chunking audio, but latency and accuracy both suffer compared to services built for streaming. For genuine real-time requirements, Deepgram or AssemblyAI are the appropriate choice.
Which handles multiple speakers best?
AssemblyAI, with speaker labelling built into the standard transcription response. Deepgram also supports it. Whisper does not out of the box — you would pair it with a separate diarisation model, which works but adds a component to build and maintain.