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.

If you’re considering how your organization can use this revolutionary technology, one of the choices that have to be made is whether to go with open-source or closed-source (proprietary) tools, models and algorithms.

Why is this decision important? Well, each option offers advantages and disadvantages when it comes to customization, scalability, support and security.

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.

Understanding Open-Source Generative AI

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.

Proponents of open-source software believe that it promotes innovation and collaboration, as developers can build on the work that has been done before them. It also makes it possible to customize and fine-tune existing tools and models for specific or niche applications.

One of the most well-known examples of an open-source generative AI model is Stable Diffusion, one of the most popular text-to-image generators. Another is Meta’s Llama, a language model that serves as an alternative to OpenAI’s closed-source GPT models, like those that power ChatGPT.

Unlike closed-source models, developers can "peek inside" open-source models and understand how they work. They may then be able to find opportunities for improving it or adapting it for new tasks and use cases.

From a security perspective, open-source models, by definition, can be externally audited to ensure that security flaws can be spotted and (hopefully) rectified by the developer community.

On top of this, open-source models are often championed by promoters of ethical AI, as they can generally be considered to be more transparent and understandable than closed-source models.

However, from a business point of view, perhaps the biggest advantage of open-source models is that, theoretically at least, they are essentially free to use. In reality, there will often be expenses involved with setting them up and getting them to work the way you want them to. Sometimes, this support will be available for free from the community, while other times, it might involve contracting with third-party commercial providers.

Understanding Closed-Source Generative AI

Now to closed-source generative AI, also referred to as “proprietary,” which is effectively private property made available for public use because its owners allow it to be licensed.

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 (or give it away) themselves.

There are, though, advantages to this model for the end user. As commercial products, closed-source AI tools have to be accessible and easy to use; otherwise, vendors will have a hard time selling them. In theory, they’ll make them as user-friendly as possible and offer customer and technical support services. One reason that businesses will choose closed-source over open-source tools, despite the additional cost, is that they expect it to be reliably maintained and supported.

There may also be security advantages to choosing closed-source models, as vendors are incentivized to ensure their models don’t leak data or allow unauthorized access. If they do, they risk severe reputational damage or even fines under data protection legislation.

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.

Which Is The Best Fit For Your Business?

Deciding between open and closed-source solutions involves carefully weighing up the specific requirements of your business as well as its strategic goals.

Of course, budget considerations will often be a big factor in any decision. While open-source tools may be free to acquire, working with them could involve significant investment in setup, customization, user training and maintenance. Closed-source, while more expensive, will often include all of the professional support and assistance needed to get started off the shelf. This could make it more cost-effective in the long term for businesses without a large technical staff.

Before making a decision, it’s essential to evaluate the technical expertise in your business and the cost and local availability of third-party support.

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.

It’s also important to audit your security and compliance requirements. In sectors like finance and healthcare, the security protocols and certification offered by closed-source may make it the logical choice.

However, if scalability and interoperability with existing systems are the priority, then open-source might offer a higher level of flexibility. This might mean your organization can implement its AI solutions in a quicker and more agile manner. If innovating and developing a competitive edge are critical elements of your business strategy, then open-source may provide an advantage here.

Overall, there’s no one-size-fits-all answer to the question of open versus closed source. Deciding what’s best for your organization involves a balanced consideration of all of the issues mentioned here.

By carrying out this assessment, however, you’re more likely to end up with the solution that best fits your needs, setting your business up to profit from the opportunities offered by generative AI.