Even in their relative infancy, Large Language Models (LLMs) have revolutionized the way organizations approach their work. Boosting productivity and excelling at tasks such as question-answering, sentiment analysis, text synthesis and summarization, LLMs can be applied to a growing number of use cases that enable companies to provide superior products and services – while saving time and money. As LLM-powered applications become more integral to a company’s ability to keep pace in an increasingly competitive landscape, a crucial decision faced by organizations is choosing between an open-source or closed-source model for their projects.
In this post, we provide a comprehensive comparison of open-source and closed-source LLMs, detailing the differences between them, their respective pros and cons, and, most importantly, how to determine which is best for your organization.
Understanding LLMs
LLMs are deep learning models that have been trained on vast amounts of textual data to learn the relationships and patterns within human languages. As a result of this, they’re able to predict the next word (or, more accurately, the next token, i.e., ¾ of a word) in sequence, enabling them to understand and, subsequently, generate text. This grants LLMs a range of powerful natural language understanding (NLU) and processing (NLP) capabilities including question-answering (QA), machine translation and document analysis among others that have led to the development of increasingly powerful AI- applications.
Although LLMs have their roots in the 1960s, with the development of the Eliza model, and have been the subject of consistent research since the late 1990s, it was the introduction of the Transformer Architecture in 2018 that ushered in the sophisticated LLMs we have today. Google developed BERT soon afterwards, which improved on the Transformer by adding bidirectional representations (reading sequences from left-to-right and right-to-left), which allows models to be trained on larger datasets in less time and with greater stability. This innovation then influenced OpenAI’s Generative Pre-Trained Transformer (GPT) used in popular applications such as ChatGPT.
Open Source LLMs
Open-source LLMs are characterized by their source code being publicly available, allowing anyone to use, modify, and distribute them. Though usually initially developed by a small team – or even an individual – open-source models are improved upon collaboratively by their communities, facilitating innovation and increased understanding.
Use cases for open-source LLMs include:
- Customized Solutions: projects requiring tailored solutions or for the organization to retain complete control of the model. Consequently, they are well-suited for solutions that use private data, as companies can still track the flow of data within the system.
- Educational and Research: their accessibility and transparency make them ideal for academic and research purposes, where practitioners can deeply explore their inner workings and push their capabilities.
Examples of popular open-source LLMs include:
- LlaMA series (2, 3)
- Mistral series (7, 7x8B, etc.)
- Falcon 180B
- Grok AI
- MPT series (7B, 30B)
- HuggingFace Bloom
The Pros and Cons of Open-Source LLMs
Let us explore the benefits and drawbacks of open-source language models.
Pros
- Control: organizations can retain greater control over their models, enabling the use of sensitive data without fear of how it could be used by vendors and, consequently, running into non-compliance issues.
- Transparency: similarly, open-source LLMs provide greater transparency, giving companies greater insight into how a model works and arrives at its outputs. Additionally, the accessibility of an open-source model’s code enables thorough security audits to ensure potential security or ethical issues can be identified and remediated quickly.
- Community Support and Collaboration: the global communities of widely-used open-source LLMs, composed of researchers and developers, are very engaged and contribute to their maintenance and development.
- Cost-Effectiveness: open-source LLMs are free to use, on a small scale, at least, which significantly reduces the barriers to entry for AI adoption.
Cons
- Less Secure: because open-source LLMs are collectively overseen, they can be subject to less stringent security testing and fewer updates than closed models, for which there is clearer ownership and responsibility.
- Stability: as they receive less consistent maintenance and have less vigorous QA standards, open-source LLMs can exhibit instability. Their developers also tend to have fewer resources to make models as robust as possible.
- Integration Challenges: open-source LLMs often offer fewer integrations with existing tools and platforms, causing a lack of standardized APIs and other compatibility issues.
Closed Source LLMs
Closed-source LLMs are proprietary models developed and maintained by private vendors. In contrast to open-source models, their source code is not publicly accessible, and their usage typically requires a licensing fee.
Use cases for open-source LLMs include:
- Commercial and Enterprise Solutions: companies often prefer closed-source models because of their stability, security, and vendor support – all of which are for enterprise-level applications.
- Industry-Specific Applications: closed-source models can be optimized for specific industries, such as healthcare or finance, or specific tasks, such as conversational analysis or educational support, to offer specialized functionality that boosts productivity and efficiency in a particular field.
Examples of popular closed-source LLMs include:
- OpenAI GPT series (3.5, 4, 4-o, o1)
- Google Gemini
- Anthropic Claude series (2, 3, 3.5)
- Command R
- Nebula
Pros and Cons of Closed-Source LLMs
Let us now turn our attention to assessing the advantages and disadvantages of closed-source language models.
Pros
- More Performant and Robust: closed LLMs are developed and supported by a dedicated team of experts with the considerable computation resources required to build and support large models with vast capabilities. They are also governed by policies and controls that ensure they are extensively tested and refined to make them as secure and stable as possible.
- Proprietary Innovations: similarly, as closed-source LLM vendors possess greater computational and personnel resources, their models often feature cutting-edge capabilities and optimizations that are not yet available in open-source alternatives.
- Simpler Integration: closed LLMs usually require minimal configuration -with many even “plug-and-play”. This considerably lowers the barriers to adoption by making AI technology available to companies that lack the required in-house, technical knowledge.
- Consistency and Support: vendors provide dedicated support for their models, ensuring consistent performance and reliable troubleshooting – including detailed API documentation. Developers regularly release updates for their closed-sources models, for enhanced performance and to fix bugs and security vulnerabilities.
Cons
- Cost: companies are typically required to pay a licensing fee to access a closed-source LLM, adding to initial setup costs.
- Data privacy: many closed-source LLM vendors could use the data you enter into their models for future training and research purposes, as stipulated in their privacy agreements. This raises privacy concerns for companies as they won’t be able to account for the location and security of sensitive data – leading to non-compliance with data privacy legislation.
- Limited flexibility and transparency: limited access to the model’s architecture and training data makes them less suitable for experimentation and research. This also makes it challenging for users to fully understand why the model generates certain output and how to improve it.
Open Source vs Closed Source LLMs: A Comparative Analysis
With a better understanding of both types of models, let us compare them across several aspects to help you determine which is the best fit for your project.
Cost Comparison
Although open-source LLMs can be more cost-effective, as you avoid licensing fees, their total cost of ownership increases when you factor in the need for in-house personnel for its setup and maintenance, as well as additional infrastructure, whether on-premise or cloud-based. Closed-source models, while incurring ongoing usage costs, don’t require infrastructure upgrades and typically come with comprehensive support and maintenance, reducing operational overhead while providing predictable expenses.
Flexibility and Customization
Open-source LLMs are more flexible, which allows for extensive customization for specific use cases. Their greater transparency and control also better enable the use of private data. Closed-source LLMs are less transparent, by definition, and while some offer some fine-tuning capabilities, they are less customizable than open-source models.
Security Considerations
Closed source models are typically more secure as only a few authorized individuals have access to the codebase and they are regularly updated. The code for open-source models, in contrast, is publicly available, so malicious actors can better identify vulnerabilities. Also, security updates are contributed by the model’s development community, so they’re less frequent and effective than those for closed-source models.
Performance and Support
Closed-source models are usually more performant, as they’re backed by vendors with more resources at their disposal. They also come with dedicated support, which is especially useful for organizations without the expertise to maintain the model. Conversely, open-source models tend to be stable, as they’re subject to less compliance and safeguards. Meanwhile, support is provided by the model’s community through documentation, forums, etc., and is less structured than direct vendor support.
Conclusion
Ultimately, choosing between an open-source or closed-source LLM for your organization’s digital transformation projects depends on your particular needs and available resources. While open-source models offer the flexibility and cost savings ideal for smaller tailored projects and research, closed models provide the reliability and support vital for production-grade applications. It’s important to assess your requirements carefully to select the type of model that will best support your organization’s objectives and align with your digital roadmap.
