The choice between open-source and closed-source models has emerged as a key debate within generative AI.

With the open-source model, the model's source code is released openly to the general public, making it accessible and available for examination, modification, and redistribution. This approach often involves collaborative development by decentralized, voluntary communities, where individuals contribute to the codebase voluntarily. Additionally, open-source models can be managed by more formal organizations that oversee the development process, providing structure and governance to the collaborative effort.

Conversely, closed-source involves keeping a model's source code confidential and not disclosing it to the public. In this model, the source code is considered the intellectual property of the organization or individual that developed it, and it is not made publicly available for inspection or modification.

When deciding between the two, critical considerations related to transparency, customization, collaboration, intellectual property, security, and support arise. Additionally, factors such as the specific use case, organizational requirements, and project goals play a pivotal role. Open-source projects thrive on transparency, collaboration, and community-driven innovation, while closed-source models prioritize IP protection, source code control, and revenue generation through licensing or sales.