Closed AI models are accessed only through a company's app or API, with the model weights kept private. Open-weight (often called open-source) models release their weights so anyone can download, run and modify them under a licence. Closed models are often easiest and strongest; open models offer more control, privacy and customisation, at the cost of more technical work.
The basic difference
Every AI model is defined by its weights: the billions of numbers learned during training.
- Closed models keep weights private. You use them through the provider's app or API. Examples include the flagship models behind major commercial assistants.
- Open-weight models publish the weights, so developers can download and run them on their own servers or even laptops. Some are released under permissive licences; others have restrictions.
Strictly, many "open-source" AI models are open-weight: the weights are available, but the training data and full training code may not be. The distinction matters for transparency debates.
Pros and cons
| Closed models | Open-weight models | |
|---|---|---|
| Ease of use | Very easy via app or API | Needs technical setup |
| Performance | Often leading on the hardest tasks | Strong and improving; smaller models less capable |
| Data privacy | Data goes to the provider (subject to their policies) | Can run fully on your own infrastructure |
| Cost | Pay per use or subscription | Pay for your own hardware or hosting |
| Customisation | Limited to what the provider allows | Full control, including fine-tuning |
| Reliability | Provider handles updates and uptime | You manage updates and uptime |
| Transparency | Limited | Greater, though training data may still be private |
How organisations choose
- Startups and individuals often begin with closed APIs for speed and quality.
- Organisations with sensitive data, such as hospitals, banks or governments, may prefer open-weight models running on their own servers.
- High-volume, simple tasks can be cheaper on small open models.
- Many teams use both, choosing per task.
Why students should care
Understanding the model landscape is part of AI literacy, and practical experience with both kinds is valuable for developers. In our generative AI development course, students build with commercial APIs and also run a small open-weight model locally to understand the trade-offs.
Frequently asked questions
Can I run an AI model on my own laptop?
Yes, smaller open-weight models can run on modern laptops, though more slowly and with less capability than the largest models.
Are open models less safe?
Open models allow anyone to modify them, which raises misuse concerns, but they also enable independent scrutiny. This is an active debate.
Is open-source AI free?
The weights may be free to download, but running models costs money for hardware or hosting.
