In the fast-paced world of AI, the term “open” is often used, but not all open models are created equal. For CIOs and IT decision makers, understanding the distinction between open-weight and open-source AI models is crucial. Misunderstanding the nuances between open-weight and open-source models can lead to compliance missteps, integration headaches, or missed innovation opportunities. CIOs must ensure strategic alignment and risk mitigation by establishing a clear AI model governance framework that distinguishes between these two model types.
Open-weight vs Open-source
Open-weight models provide access to pre-trained weights, allowing for fine-tuning and deployment. However, they often lack transparency in architecture, training data, and source code–limiting the ability to fully understand, audit, or modify the model. This can lead to unforeseen risks, including compliance issues and security vulnerabilities. On the other hand, open-source models offer full transparency and control, enabling organizations to inspect, audit, …