faster-whisper vs Whisper
Both are alternatives to Deepgram. Here's how they stack up — verified facts, no spin.
Also searched as Whisper vs faster-whisper — same comparison, one verdict.
faster-whisper
TOP PICKWhisper, several times faster, on less memory. The practical default.
faster-whisper reimplements Whisper inference on CTranslate2, delivering roughly a four-fold speedup over the reference implementation with substantially lower memory use, and the same transcription output — these are the same weights, executed better. It supports int8 and float16 quantisation, batching, and word-level timestamps, and runs on both GPU and CPU. For most teams replacing a paid transcription API, this is simply the correct starting point.
Whisper
The model that changed the category. MIT, and free to run forever.
Whisper is OpenAI's speech-recognition model, released openly under MIT — weights included. It handles around a hundred languages, is robust to accents and background noise, and does translation as well as transcription. Releasing it is what collapsed the economics of this category: a model competitive with the commercial APIs became something anyone could download. The reference implementation is slower than the optimised runtimes, but it is the simplest thing that works and the baseline everything else is measured against.
Side by side
| faster-whisper | Whisper | |
|---|---|---|
| Sovereignty Score | 95 | 94 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | Yes |
| License | MIT | MIT |
| Pricing | Free. Hardware you already own; a laptop handles the smaller models. | Free — model weights and code both MIT. |
faster-whisper is Macrostack's recommended Deepgram alternative, so it's our pick here.
faster-whisper
Strengths
- +Several times faster than reference Whisper at equal accuracy
- +Quantisation options let large models fit modest GPUs
- +Runs on CPU when no GPU is available
- +MIT licensed, no per-minute cost, nothing leaves your machine
Trade-offs
- −Batch-oriented; streaming needs extra work to do well
- −Diarization is not included — pair with WhisperX or pyannote
- −Accuracy varies by language more than the managed services do
Whisper
Strengths
- +Genuinely open weights under MIT, not a restricted community licence
- +About a hundred languages, robust to noise and accent
- +Reference implementation — simplest possible starting point
- +Translation to English included
Trade-offs
- −Slower and hungrier than faster-whisper for identical output
- −Weak word-level timestamps without help
- −No speaker diarization
Related alternative guides
Facts verified 2026-08-11. Licenses and pricing change — spotted something out of date? That's a correction we want.