Open Source vs Closed Source AI
The Recipe You Can Read vs The Recipe That's a Trade Secret
6 min read
The Recipe You Can Read vs The Recipe That's a Trade Secret
Maggi's recipe is a trade secret. A home chef who posts their recipe on YouTube for anyone to use and modify — that's open source.
Closed source AI (GPT-4, Claude, Gemini) keeps its weights and training process proprietary — you access it via API only. Open source AI (Llama, Mistral, Gemma) releases the actual model weights — you can download, run, modify, and build on them freely. Open source is often nearly as capable, completely free to run, and keeps your data private. The tradeoff: you need your own hardware or cloud setup.
In Plain English
Closed source AI is accessed via API — you pay per use and never see the model. Open source AI releases the model weights freely — you can run it yourself, modify it, and keep your data entirely private. Both approaches have strong models today.
The Technical Picture
Open source models release weights under permissive licences (Meta's Llama Community Licence, Apache 2.0, MIT). This enables local inference, custom fine-tuning, and private deployment. Closed source models are accessed only via vendor APIs, offering convenience and cutting-edge performance at the cost of data sovereignty and vendor lock-in.
Real-World Examples
- Meta's Llama 3 is open source — downloadable and runnable on your own hardware
- Mistral, Gemma, Phi, and Falcon are popular open source alternatives
- Many enterprises choose open source for data privacy — no data leaves their servers
Open source AI means you own the model. Closed source AI means you rent access to it.