There's something missing from the open vs. closed models debate that has been bothering me.
The better analogy， to me， is managed vs. self-hosted infrastructure for AI.
Maybe a company wants to use an open weight model because they want to do additional training on top， bringing their data and domain knowledge.
This requires software and services to do additional training （e.g. Tinker and friends） as well as to serve the model （e.g. SGLang）. Not all of this software is open source today！
Once you have successfully trained a model with your enterprise data and expertise， you now need to deploy and serve it for customers. You can partner with an inference company to run the software and hardware. But if ownership was your primary concern， you still want to control the hardware and storage， and you now also need to run infra and secure GPU capacity.
There are other valid reasons to be open. In particular， the entire industry benefits when companies training models release data or research about their work. It also allows capitalism and free markets to do their thing， increasing competition and ultimately providing better options for customers. So we should all encourage openness.
The reason I prefer the managed vs. self-hosted infra framing is that we can learn from the past decade of cloud infrastructure. It's important and healthy to have both， and a great self-hosted alternative ultimately pushes the managed versions to innovate.
The decision to run infra then comes down to more standard business reasons： attracting talent， the cost and maintenance of the hardware， and the importance of uptime and reliability to the business.