When people talk about “open” versus “closed” AI models, they’re usually talking about whether a company releases the model’s weights — the actual trained parameters — for anyone to download, inspect, and run on their own hardware, versus keeping the model accessible only through a paid API.
What open-source gives you
With an open-weight model, you can run it on your own servers, fine-tune it on your own data, and use it without sending your data to a third party. This matters a lot for companies with strict privacy requirements, researchers who want to study how models work internally, and developers who want full control over cost and latency rather than depending on someone else’s API pricing and uptime.
What closed models offer instead
Closed, API-only models tend to be the most capable available at any given time, since frontier labs often keep their newest and largest models proprietary. They also come with less infrastructure burden — no need to manage GPU servers or handle model updates — and typically include built-in safety tuning and moderation that a self-hosted model would require you to build yourself.
Choosing between them
The right choice depends on the task. Open models are attractive for privacy-sensitive applications, cost control at scale, and research. Closed models are usually the better choice when you want the highest available capability with minimal setup, and when you’re comfortable sending data to a third-party API under their privacy terms. Many companies end up using a mix — open models for routine, high-volume tasks, and closed frontier models for the hardest problems.