API vs Local Deployment | OpenAI Whisper

Local Deployment

Cost per minute

$0 (after hardware investment)

Setup complexity

Install Python, model, and GPU drivers

Data privacy

Audio never leaves your infrastructure

Model options

All open source models (tiny to large-v3-turbo)

Streaming

Requires custom implementation

Speaker diarization

Requires third party tools (pyannote, etc.)

Fine tuning

Fully supported (MIT license)

Scaling

You manage scaling and load balancing

OpenAI API

Cost per minute

$0.003 to $0.006 per minute

Setup complexity

Get API key and make HTTP requests

Data privacy

Audio sent to OpenAI servers

Model options

whisper-1, gpt-4o-mini-transcribe, gpt-4o-transcribe

Streaming

Built in with gpt-4o-transcribe models

Speaker diarization

Built in with gpt-4o-transcribe-diarize

Fine tuning

Not available

Scaling

Automatic, handled by OpenAI

Estimated monthly cost by volume (API at $0.006/min)

The breakeven point

If you process fewer than 200 hours of audio per month, the API is likely cheaper than renting a GPU instance. Above 500 hours per month, local deployment saves significant money. If you own your own GPU hardware, the breakeven comes even sooner since the marginal cost of local inference is just electricity.

The gpt-4o-transcribe models are not open source

While the original Whisper models (tiny through large-v3-turbo) are fully open source under MIT, the newer gpt-4o-transcribe and gpt-4o-mini-transcribe models are proprietary and only available through the API. These newer models offer better accuracy and features like streaming and diarization, but they lock you into the OpenAI API.