Should you use open source or proprietary LLMs?
It depends on the job, and most serious teams use both: open when you need control, customization, privacy, or cost efficiency; proprietary when you need top-end quality on certain tasks or the easiest path to start. No one has won the race, so locking into one provider is the mistake.
1. Cost and performance
Aishwarya Srinivasan marks the DeepSeek V3 release as the inflection point where open models hit a price-to-performance ratio that made them competitive. For high-volume use cases, open models on your own terms can be dramatically cheaper.
2. Control and customization
Open models let you fine-tune and shape the size, the speed, and the behavior. If your use case needs the model tailored to it, open gives you that latitude in a way a closed API does not.
3. Privacy and on-prem
Proprietary models usually retain the data flowing through them to improve themselves. If you need zero data retention or to run on your own hardware, that pushes you toward open weights you control.
4. Where proprietary still wins
Aishwarya is clear-eyed about the gaps. Vision capabilities are still stronger in proprietary models, developer experience is smoother, and for some quality-sensitive tasks the closed frontier models are simply better. That is why even open-source advocates keep them in the mix.
The real answer: use both
Angie Jones’s team at Block does not standardize on one model. Claude for software, a GPT model for reasoning-heavy work, open models where the hardware and use case fit. The strength is in matching the model to the task, not crowning a winner.
Why it matters
The “open vs. proprietary” debate is framed as a fight. In practice it is a portfolio. The teams getting the most out of AI treat models as interchangeable parts and route each task to the one that fits.
From the conversation
This explainer is drawn from these episodes — each carries its full transcript.
