Navigating The Generative AI Divide: Open-Source Vs. Closed-Source Solutions

Generative AI tools have quickly become transformative to many businesses, with their power to create words, pictures, video, sounds and even computer code, augment human skills and automate routine work.

In this article, we’ll explore the key differences as well as the pros and cons of each approach, as well as explain the factors that need to be considered when deciding which is right for your organization.With generative AI models, as with other software, the term “open-source” means that the source code is publicly available, and anyone is free to examine, modify and distribute it.

From a security perspective, open-source models, by definition, can be externally audited to ensure that security flaws can be spotted and rectified by the developer community. Closed-source AI is sometimes seen as a “black box,” – meaning it’s difficult to know exactly what’s going on inside it because the only people who know how it works are those who made it. This is generally for commercial reasons – they make their money by selling it, and if everyone knew how it works, they’d be able to recreate it and sell it themselves.

This does mean they are dependent on vendors for updates, and customization options may be more limited – particularly in niche markets where there’s less business case for vendors to offer custom versions. GPT-4, Google’s Gemini, the image models Dall-E and Midjourney, and Nvidia Jarvis are all examples of closed-source generative AI models.Deciding between open and closed-source solutions involves carefully weighing up the specific requirements of your business as well as its strategic goals.

Open-source offers great potential for flexibility and customization, but businesses that lack the capability to deploy it might find closed-source tools to be a better fit.

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