Generative AI tools are rapidly transforming many businesses with their ability to create words, images, videos, sounds, and even computer code to augment human skills and automate routine tasks.
As you consider how your organization can use this innovative technology, one of the choices you must make is whether to use open source or closed source (proprietary) tools, models, and algorithms. It's about whether you do it.
Why is this decision important? Each option has advantages and disadvantages when it comes to customization, scalability, support, and security.
This article explores the key differences and pros and cons of each approach, and explains the factors you should consider when deciding which one is right for your organization.
Understand open source generative AI
For generative AI models, as with any software, the term “open source” means that the source code is publicly available and anyone can freely examine, modify, and distribute it.
Proponents of open source software believe that it fosters innovation and collaboration because it allows developers to build on work that has already been done. It also enables customization and fine-tuning of existing tools and models for specific or niche applications.
One of the most well-known examples of open source generative AI models is Stable Diffusion, one of the most popular text-to-image generators. The other one is Meta's Llama. This is a language model that serves as an alternative to OpenAI's closed-source GPT model, such as the one that powers ChatGPT.
Unlike a closed-source model, developers can “look under the hood” of an open-source model and understand how it works. Then you might find opportunities to improve it or adapt it to new tasks or use cases.
From a security perspective, open source models, by definition, can be externally audited, ensuring that security flaws are discovered and (hopefully) fixed by the developer community.
In addition to this, open source models are generally considered more transparent and easier to understand than closed source models, so they are often favored by ethical AI advocates.
However, from a business perspective, perhaps the biggest advantage of the open source model is that it is essentially free to use, at least in theory. In reality, they are often expensive to set up and get them working the way you want. This support may be available for free from the community, or may require a contract with a third-party commercial provider.
Understand closed-source generative AI
Next, we'll discuss closed-source generative AI, also known as “proprietary.” This is, in effect, private property made available for public use because the owner has granted a license.
Closed-source AI is sometimes considered a “black box.” This means that the only people who know how an AI works is the person who created it, so it's difficult to know exactly what's going on inside it. This is usually for commercial reasons. They make money by selling it. If everyone knew how it worked, they could recreate it themselves and sell (or distribute) it.
However, there are advantages to this model for end users. As commercial products, closed-source AI tools must be accessible and easy to use. Otherwise, vendors will have a hard time selling them. In theory, you want to make it as easy to use as possible and provide customer and technical support services. One reason why companies choose closed-source tools over open-source tools, despite the added cost, is because they expect the tools to be reliably maintained and supported.
There may also be security benefits to choosing a closed-source model, as vendors are incentivized to ensure that their models do not leak data or allow unauthorized access. If you do so, you risk serious reputational damage and fines under data protection laws.
This means that you are dependent on the vendor for updates, which can make your customization options more limited, especially in niche markets where there is 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 one is best for your business?
Deciding between open-source and closed-source solutions requires careful weighing of the business's specific requirements and its strategic goals.
Of course, budget considerations are often a big factor in any decision. Open source tools may be available for free, but their use can require significant investment in setup, customization, user training, and maintenance. Closed source, although more expensive, often includes all the professional support and assistance needed to get started with commercially available products. This can be more cost-effective in the long run for companies that don't have large technical staff.
Before making a decision, it's important to evaluate your business' technical expertise and the cost and local availability of third-party support.
While open source offers great potential for flexibility and customization, closed-source tools may be a better fit for companies that don't have the ability to adopt open source.
It's also important to audit security and compliance requirements. In fields such as finance and healthcare, leveraging security protocols and authentication provided by closed sources can be a logical choice.
However, if extensibility and interoperability with existing systems are priorities, open source may offer a higher level of flexibility. This could mean that organizations can implement AI solutions in a faster and more agile manner. If innovation and competitive development are key elements of your business strategy, open source may offer an advantage here.
Overall, there is no one-size-fits-all answer to the open source vs. closed source question. Deciding what's best for your organization requires balancing all of the issues discussed here.
However, by performing this assessment, you are more likely to find the solution that best suits your needs and set your business up to benefit from the opportunities that generative AI provides.