Open vs Closed Source AI: Explore the Differences and their Importance

Generative artificial intelligence (AI) tools have permeated the digital landscape, revolutionizing industries such as marketing, healthcare, and finance. These tools can do about anything, from writing essays and generating artificial videos to analyzing vast amounts of data and automating routine tasks.

AI tools have significantly increased efficiency and productivity, leading to cost savings and improved decision-making processes. If you are looking to use gen AI for either personal or professional reasons, then it comes down to choosing the right tool that aligns with your specific needs and goals. Consider factors such as ease of use, compatibility, licensing, and more. 

Recently, after the release of Meta’s latest flagship LLM Llama 3.1, there has been a lot of buzz going on around open-source AI models and how they differ from closed-source ones. When it comes to licensing and software distribution, you will find two types of gen AI models- open-source and closed-source.

In this article, we will look into the difference between open vs closed source AI models, as well as understand why they are important. But first, let’s understand what exactly are open-source and closed-source models. 

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Open Source AI

Open source AI refers to AI software whose source code is freely available for anyone to view, modify, and distribute. It promotes teamwork, makes AI available to all people equally, and helps in rapid advancement.

Transparency is a fundamental aspect of the open-source philosophy. It emphasizes community engagement and the idea that if we share code and knowledge everyone will benefit from collective improvements. 

Popular open-source AI projects include TensorFlow, PyTorch, and Hugging Face. The most recent example of an open-source generative AI is Meta AI’s Llama 3.1. 

Open-source AI tools have their pros and cons. On one hand, they are usually free to use (not always) and promote transparency and collaboration among developers. On the other hand, they lack privacy and the same level of security as proprietary software. 

Closed Source AI

Closed source AI refers to artificial intelligence software or systems where the source code is proprietary and not made publicly available. These resources belong to and are controlled by a company or organization. 

These tools are often closed source to restrict access for security motives or to maintain a competitive edge. The code, algorithms, and data utilized in these closed-source AI projects typically are not shared publicly which can result in slower innovation within this area of technology.

Some of the most popular closed-source AI tools include OpenAI’s GPT-4 and Dall-E, and Google’s Gemini. 

Similarly, closed-source AI also has its pros and cons. One advantage of closed-source AI tools is that they offer more advanced features, security, and privacy due to the proprietary nature of their development. They offer dedicated customer support and resources to the users as well. However, a major drawback is the lack of transparency and accountability, as users have limited visibility into how the algorithms work and make decisions. 

Open vs Closed Source AI: Key Differences

These are some of the most prominent differences between open and closed-source AI tools and models:

Parameter	Open Source AI	Closed Source AI
Source Code Accessibility	Freely available for anyone to view, modify, and distribute	Not available to the public; controlled by the vendor
Cost	Often free or low-cost	Requires purchasing licenses or subscriptions
Transparency	High transparency; users can inspect and audit the code	Low transparency; internal workings are hidden
Customization	High; users can modify the code to suit specific needs	Limited; modifications are restricted by the vendor
Collaboration	Encourages community collaboration and contributions	Development is controlled by the organization
Support	Community-driven support; formal support may be limited	Professional customer support provided by the vendor
Security	Innovation is driven by the company’s resources and goals	Controlled by the vendor; security patches provided
Innovation	Rapid development and innovation through community input	The vendor ensures compliance with industry standards
Integration	May require additional effort for integration	Often designed to integrate seamlessly with other products from the vendor
Learning Resource	Valuable educational resource for learning and experimentation	Limited educational insight into internal mechanisms
Vendor Lock-in	Minimal; users are free to adapt or switch technologies	High; dependent on vendor’s technology and ecosystem
Scalability	Varies by project; community contributions can enhance scalability	Generally robust, with vendor ensuring scalability
Updates and Maintenance	Community-driven; frequency and quality can vary	Regular updates and maintenance provided by the vendor
Licensing	Generally permissive licenses (e.g., MIT, Apache)	Strict licensing terms; usage governed by vendor’s EULA
Compliance and Legal	May require careful review for compliance with industry standards	Vendor ensures compliance with industry standards
Adoption Barriers	Lower barriers due to cost and accessibility	Higher barriers due to cost and licensing requirements
Performance	Can be highly optimized through community input and customization	Typically optimized for performance by the vendor
User Community	Large, diverse community contributing to development and support	Limited to users and customers of the vendor

The Bottom Line

To sum up, open-source AI refers to AI systems, software, models, and data released under an open-source license, allowing anyone to use, modify, and share them. It encourages creativity and collaboration through its community-driven development model. Open-source AI also encourages accessibility and education, eliminating entry barriers while encouraging learning and experimentation. Its advantages include transparency, trust, customization, and flexibility. 

Whereas, closed-source AI refers to AI systems, software, models, and data that are proprietary and not open to the public. It offers support and dependability, including dedicated customer service, frequent updates, bug patches, and maintenance.