Choosing the Right LLM: 2024 Comparison of Open-Source Vs Closed-Source LLMs

Businesses face increasing pressure to incorporate artificial intelligence into their products. When considering large language models (LLMs) for this purpose, the choice often comes down to open-source versus closed-source options.

LLMs are advanced machine-learning models designed to understand and generate human-like text based on a given prompt. This guide will help you weigh the advantages and disadvantages of open-source and closed-source LLMs. It covers availability, cost, intellectual property rights, security measures, etc. Additionally, you'll get an overview of the current landscape in both fields, ethical considerations, and key open-source LLMs.

Given the rapid evolution of licensing and technical compatibility in this area, consulting with technical and legal experts is crucial before finalizing your decision.

The Current Landscape of LLMs

Organizations like Databricks, Stanford, and the German nonprofit LAION are working to democratize LLM access, in contrast to proprietary models like ChatGPT. This effort has sparked debate over whether AI models should be freely available or protected by copyright, raising ethical and security concerns regarding open-source LLMs.

The release of GPT-4, which included a technical report but withheld details about the model's architecture, hardware, or training methods, intensified the open-source debate. Critics argue that this lack of transparency gives an illusion of openness. OpenAI, once an advocate for open-source, now cites safety concerns for keeping models closed.

Open-source AI has significantly contributed to AI advancements, with many popular LLMs built on open-source architectures like Transformers. However, companies' shift towards proprietary commercial models has raised concerns about transparency and accessibility, increasing the popularity of open-source alternatives.

The future ecosystem will likely feature diverse options with varying levels of openness to strike the right balance. Research enabled by open-source models is crucial, though smaller open-source LLMs, such as Vicuna, may not be as advanced as proprietary solutions like ChatGPT for specific applications.

Open Source LLM and AI Models

Open-source LLMs are language models with publicly accessible source code that anyone can freely use, modify, and distribute. These models promote collaboration, transparency, and community engagement. Developers, researchers, and enthusiasts can actively contribute to their development, enhancement, and customization. Their open-source nature fosters greater innovation, knowledge sharing, and collective progress.

Closed Source LLM and AI Models

Closed Source LLMs are language models with source code that is not publicly accessible. Developed and maintained by organizations or companies, these models are kept proprietary and closed to the public. Typically offered as commercial products, their use often requires licenses or subscriptions. The specifics of their architecture, training data, and algorithms are generally not disclosed to the public.

Open Source vs. Closed Source Debate

Advancements in deep learning architectures, particularly transformers, along with the availability of massive datasets like Google Books and Common Crawl, fueled significant progress in large language models (LLMs). By 2018, OpenAI's Generative Pre-trained Transformer (GPT-2) demonstrated impressive text generation capabilities, attracting widespread attention. GPT-2 is often considered a landmark in LLM development due to its public release and capabilities.

However, GPT-2 was not fully open-source. OpenAI opted for a controlled release due to concerns about potential misuse, sparking a debate about the merits of open-source versus closed-source approaches in LLM development.

Does it Matter if LLMs are Open or Closed Source?

Yes, because the approach to LLMs—whether open or closed—affects these three crucial factors:

1. Innovation Speed & Customization

2. Accessibility & Cost

3. Data Security

These factors influence which model is best suited for your business.

Let's explore each in detail and how they vary between open-source and closed-source models.

1. Innovation Speed & Customization

Open-source LLMs allow for greater customization and have the potential for faster innovation.

Open Source LLMs

• Customization: Companies can tailor and fine-tune the model to meet their needs.

• Rapid Innovation: Open access allows businesses to innovate quickly, integrating technology with other systems without waiting for vendor updates.

• Community Support: Developers share advancements, accelerating the innovation process.

Closed Source LLMs

• Limited Customization: Customization options are often restricted.

• Sophisticated Solutions: Developed with significant resources, offering cutting-edge performance or unique functionalities.

• Vendor Reliance: Innovation can be slower due to dependence on vendor updates.

2. Accessibility & Cost

Both open-source and closed-source models vary in cost and accessibility.

For instance, ChatGPT-4 (closed source) costs around $10 per million token input and $30 per million token output, whereas Llama-3-70-B (open source) costs about 60 cents per million token input and 70 cents per million token output, making it approximately 10 times cheaper with minimal performance difference.

Open Source LLMs

• Lower Costs: Models like Llama-3-70-B offer significant cost savings.

• Wide Accessibility: Lower costs make advanced AI capabilities accessible to more developers.

• Customization Investment: Developers may need to invest in customization and maintenance.

Closed Source LLMs

• Licensing Fees: Often come with higher costs, including ongoing fees for updates and support.

• Support Services: These fees may be justified by the support and maintenance provided by the vendor, ensuring effectiveness and security.

3. Data Security

If data security is a top priority, an open-source LLM or a self-built closed-source model within your infrastructure is preferable.

Open Source LLMs

• Enhanced Control: Deploying on a private cloud offers greater control over security measures and data privacy.

• Tailored Security: Organizations can implement customized security protocols.

• Transparency: Allows for thorough audits and continuous security improvements.

Closed Source LLMs

• Vendor-Managed Security: Provides peace of mind for companies without extensive IT resources.

• Compliance Certifications: Vendors may offer necessary certifications for regulatory adherence.

• Limited Visibility: Companies have less insight into potential vulnerabilities and must rely on the vendor for security updates.

Comparison Chart: Open-source vs Closed-source LLMs

Evaluating the Business Impact of Open Source and Closed Source LLMs

Innovation, accessibility, and security are crucial, but you must also consider scalability, cost, integration, and customization to fit your needs best.

Below, we provide easy-to-read tables to help you understand how each model can impact your business across these factors.

1. Scalability and Cost

Consider both short-term and long-term use of the LLM. Will a closed model's use cases become insufficient as you grow? Can you handle the costs associated with scaling an open-source model?

2. Integration and Customization

Evaluate your team's technical capabilities and the importance of tailored solutions. Does your workflow demand bespoke AI features that open-source models can uniquely provide, or do you prefer a streamlined, ready-to-use solution that minimizes technical overhead?

Why we think Open Source LLM’s are best

Universities have a rich tradition of sharing research and code, which naturally extends to AI and LLMs. This open collaboration ethos is rooted in the success of open-source movements like Linux, which showcase the power of community-driven development. Inspired by this, researchers and developers have embraced open-source approaches for LLMs.

Numerous research groups and independent developers significantly contribute to the open-source LLM ecosystem. This collaborative effort is broadening the variety of available models, such as OpenAI GPT-J, Meta AI Llama, EleutherAI Jurassic-1 Jumbo, and the Hugging Face Transformers. A vibrant community of developers and companies continually enhances the open-source LLM landscape.

This field rapidly evolves, with new models being developed and released frequently. The Hugging Face Transformers Library provides access to over 100 pre-trained models, and numerous independent projects consistently launch new open-source LLMs.

Recent reports indicate a growing preference for open-source LLMs. According to data from a16z.com, 41% of enterprises interviewed plan to increase their use of open-source models over closed ones. Another 41% will switch to open-source models if their performance matches that of closed models, while only 18% do not intend to boost their use of open-source LLMs.

If these projections hold, we could witness a significant shift in industry trends. The market, which was dominated by closed-source models with an 80%–90% share in 2023, may soon see a more balanced distribution between open and closed models.

Conclusion

In conclusion, the choice between open-source and closed-source large language models (LLMs) is a pivotal decision that impacts innovation, cost, scalability, security, and integration within a business. Open-source LLMs offer unparalleled customization, community-driven innovation, and cost-effectiveness, making them an attractive option for organizations with the technical expertise to harness their potential. They also align with the collaborative spirit of academic and research institutions, contributing to the democratization of AI technology.

On the other hand, closed-source LLMs provide ready-made solutions with robust support and maintenance, which can be invaluable for businesses seeking streamlined implementation and vendor-managed security. While they come at a higher cost and offer limited customization, they are often more accessible for companies lacking the resources to manage open-source models.

As the LLM landscape continues to evolve, with increasing contributions from both open-source communities and commercial entities, the future will likely see a more balanced ecosystem. The ongoing development of open-source models, coupled with growing enterprise interest, suggests a potential shift toward broader adoption of open-source LLMs, provided they can meet performance expectations. Ultimately, the best choice depends on a company’s specific needs, technical capabilities, and long-term goals, emphasizing the importance of careful evaluation and consultation with experts before deciding.

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