Navigating the Generative AI Divide: Open-Source Vs. Closed source solutions

AI creation tools have quickly become transformative in many businesses, with their power to generate words, images, videos, sounds, and even computer code, augment human skills, and automate everyday work.

If you are considering how your organization can use this revolutionary technology, one of the choices you must make is whether to use open source or closed source (proprietary) tools, models and algorithms.

Why is this decision important? Well, each option offers pros and cons in terms of customization, scalability, support, and security.

In this article, we’ll explore the key differences and pros and cons of each approach, and explain the factors to consider when deciding which one is right for your organization.

Understanding Open-Source Generative AI

With genetic 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 it promotes innovation and collaboration because developers can build on work that has been done before them. It also makes it possible to adapt and refine existing tools and models for specific or specialized applications.

One of the best-known examples of an open-source 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, such as those that power ChatGPT.

Unlike closed source models, developers can “look inside” open source models and understand how they work. They may then be able to find opportunities to improve it or adapt it to 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 found and (hopefully) fixed by the developer community.

Furthermore, open source models are often supported by ethical AI advocates, as they can generally be considered more transparent and understandable than closed source models.

However, from a business perspective, perhaps the greatest advantage of open source models is that, at least in theory, they are essentially free to use. In fact, there will often be costs involved in setting them up and making them work the way you want them to. Sometimes, this support will be available free of charge from the community, while at other times, it may involve contracts with third party commercial providers.

Understanding Closed-Source Generative AI.

Now for closed source AI, also referred to as “proprietary”, which is essentially private property made available for public use because its owners allow licensing.

Closed-source AI is sometimes thought of as a “black box,” meaning it’s hard to know exactly what’s going on inside it because the only people who know how it works are the people who built it. This is generally for commercial reasons – they make their money by selling it, and if everyone knew how it works, they could recreate it and sell (or give it away) themselves.

There are, however, advantages to this model for the end user. As commercial products, closed source AI tools must be accessible and easy to use. Otherwise, sellers will find it difficult to sell them. In theory, they will make them as user-friendly as possible and offer customer support and technical support services. One reason businesses choose closed source tools over open source tools, despite the added 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 have an incentive to ensure that their models do not leak data or allow unauthorized access. If they do, they risk serious damage to their reputation or even fines under data protection laws.

This means they are dependent on vendors for updates and customization options can be more limited – particularly in niche markets where there is less of a business case for vendors to offer custom builds. GPT-4, Google’s Gemini, Dall-E and Midjourney image models, and Nvidia Jarvis are all examples of closed source AI models.

Which is best for your business?

Deciding between open and closed source solutions involves careful weighing of your business’s specific requirements 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, often includes all the professional support and help needed to get started off the shelf. This could make it more cost-effective in the long run for businesses without a large technical staff.

Before making a decision, it is important 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 don’t have the ability to develop it may find closed source tools to be a better fit.

It is also important to review security and compliance requirements. In fields like finance and healthcare, the security protocols and certification offered by closed source can make it the logical choice.

However, if extensibility and interoperability with existing systems is the priority, then open source can offer a higher level of flexibility. This can mean that your organization can implement AI solutions in a faster and more flexible way. If innovation and developing a competitive advantage are critical elements of your business strategy, then open source can provide an advantage here.

Overall, there is no single answer to the question of open versus closed source. Deciding what is best for your organization involves a balanced consideration of all the issues listed here.

By conducting this assessment, however, you are more likely to arrive at the solution that best suits your needs, setting your business up to take advantage of the opportunities that genetic AI offers.