Open-Source AI vs. Closed-Source AI: What’s the Difference?

What Are Open-Source AI Models?

Open-source AI models are publicly and freely accessible systems that developers can use for various applications and purposes.

GPT-Neo is a great example of an open-source AI model. It’s a similar model to ChatGPT, which can process and generate human-like text. Based on an open-source code, GPT-Neo represents a step towards making advanced AI tools more accessible.

Another great example is OpenAI’s CLIP, which is an open-source AI model that connects images and text to enable tasks like classification and zero-shot learning.

What Are Closed-Source AI Models?

Closed-source models are proprietary systems that keep their code confidential. By restricting access to underlying code, closed-source AI model developers ensure more control over the system.

The most known closed-source AI model is GPT-4, a language model with advanced natural language understanding and generation capabilities.

Another great example is Gemini. It’s an AI model developed by Google as a rival to OpenAI’s GPT-4 and other similar models with its capability to generate human-like text. Its goal is to deliver powerful and versatile AI solutions to users worldwide.

Open-Source vs Closed-Source AI Models: Key Differences

The key differences between open-source and closed-source AI models include:

• Accessibility
• Collaboration
• Transparency
• Cost
• Updates and support
• Security
• Customization

Open AI models have publicly available code that allows anyone to access and modify the model. This approach provides better transparency and collaboration but can lead to fewer updates and weaker data security. While open-source AI models are typically free to use, keep in mind that there might be costs associated with support or advanced features of the model.

Closed-source AI models have a proprietary code that’s restricted to the developing organization, which limits the customizability and collaborative potential. Such an approach leads to low transparency with limited insight into data handling practices, but the updates are typically frequent, and security is slightly better since it’s managed internally. Closed-source AI models almost always involve licensing and access costs.

With many key differences, there are still advantages and disadvantages to each model. Understanding them can help you decide which approach fits your business needs better.

Advantages and Disadvantages of Open-Source AI Models

Open-source AI models have plenty of advantages and a few disadvantages. Their biggest advantage, though, is that you can fine-tune them and get a customized proprietary system. We’ll discuss this more later in the article.

Open-Source AI Advantages:

• Collaborative features — Open-source code that is accessible to the community leads to better collaboration and contribution, which enhances advancements and diverse use of the model.
• Opportunity for innovation — You have the flexibility to modify the model and adapt it to your specific business needs, resulting in a tailored proprietary model that addresses your challenges and needs.
• Transparency — Visibility of algorithms and data handling processes leads to better accountability, which can help you thoroughly evaluate the model’s performance and ethical considerations.
• Cost-efficiency — With open-source models often being free to use, the initial access cost is reduced, which makes open-source AI models more accessible to smaller businesses.
• Support and resources — The community behind open-source AI models often provides rich resources, documentation,  tutorials, and forums that make implementation and troubleshooting easier.

Open-Source AI Disadvantages:

• Security risks — The code's accessibility to the public can also expose vulnerabilities that could be exploited.
• Lack of official support — Lack of formal support can pose challenges in critical situations, so having a troubleshooting plan or working with an AI partner is recommended.
• Additional costs — While access to open-source models is often free, there are other costs associated with deploying and maintaining them yourself.

Advantages and Disadvantages of Closed-Source AI Models

Closed-Source AI Advantages:

• Consistent updates and support — Closed AI systems receive frequent updates and dedicated support from the developers, which ensures reliability.
• Improved security — Keeping the AI model’s code confidential leads to a more controlled environment.
• Streamlined implementation — Closed-source solutions frequently come with comprehensive documentation and a user-friendly interface that speeds up and simplifies the integration process.
• Quality assurance — Developers maintain quality control, which ensures the model meets industry standards.

Closed-Source AI Disadvantages:

• Limited customization — Closed-source AI models have restrictions on modifying and adapting the model to fit specific needs, which leads to less tailored solutions.
• Higher costs — Licensing fees and ongoing costs add up, which often makes closed-source AI models less budget-friendly. As one of our clients put it, using closed-source AI models “adds up to a pretty big sum quickly [when you’re getting loads of users.”
• Lack of transparency — Closed-source AI models provide limited visibility into algorithms and data handling practices, which can raise accountability concerns.
• Dependency on vendor — When relying on a closed-source AI model, you become dependent on the developer’s updates, continued access, and support, which can be risky for your business if the developer’s priorities change.
• Potential for vendor lock-in — Switching from a closed-source AI model to another solution can be difficult and expensive, as closed-source AI technology can create migration barriers.

When Should You Fine-Tune or Use Open-Source AI?

Fine-tuning and using open-source AI models is ideal for companies with custom data, time, resources, and money. Although it requires more work and resources upfront, it leads to better performance in the long run.

Fine-tuning open-source AI models also make sense in two specific cases:

1. When your company requires a massive return on investment (ROI)
2. When your company needs a strategic advantage over competitors

In such cases, open-source AI is a much better choice. It is more flexible, gives you more control, and makes more sense if you have a custom training dataset.

Fine-tuning an open-source AI model is the best option for every organization with big goals that closed-source AI models might not help you achieve.

We primarily fine-tune open-source AI models (like Llama 2) for our clients. We use closed-source models while we collect more data and configure a more suitable open-source AI.

The final result is an open-source AI solution that can outperform and replace the closed-source AI model.

You should fine-tune and use an open-source AI model when you want to:

• Apply a model to a specific industry (like healthcare, banking, or insurance) and help it understand the terminology and context for that industry.
• Improve the model’s accuracy and effectiveness by adjusting it to specific data and tasks.
• Leverage the company’s existing custom data.
• Customize the model to generate specific outputs while matching the company’s workflow, style, and format.

When Should You Fine-Tune or Use Closed-Sourced AI?

Using a closed-source AI model makes the most sense when you:

• Want an easier access
• Experience resource constraints
• Need to quickly implement AI capabilities into your workflow

When it comes to fine-tuning closed-source AI models, it’s an ideal approach for leveraging the model’s existing knowledge while updating it with new training data.

Fine-tuning and using closed-source AI models is ideal when you need quick and reliable access to advanced solutions without extensive resource investment.

However, before making the choice, consider its advantages and disadvantages, as well as your project’s goals, available resources, and specific requirements.

Open-Source AI Tools

In addition to open-source models, various tools, such as frameworks and libraries, can help developers build and refine AI models and data and ensure efficient deployment.

Some of the well-known open-source AI tools include:

• TensorFlow — Google’s comprehensive library for deploying machine learning and deep learning AI models.
• PyTorch — Meta’s framework for building deep learning models.
• Scikit-Learn — Library of classical machine learning algorithms in Python.
• OpenCV — Computer vision library for real-time image processing tasks.
• Apache Spark — Computing system with a machine learning library, ideal for handling large datasets.
• Hugging Face Transformers — Library of language processing tasks with access to pre-trained models.

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