Open-source vs. сlosed-source LLMs

Some say the future belongs to open-source large language models, where anyone can access and improve the technology. Others argue that keeping these models private is the only way to ensure safety, quality, and ethical use. Both sides have strong points, and the choice between open and closed isn't just about models about trust, innovation, and who gets to shape the future of AI. 

Both closed and open-source models are  currently available to enterprises  implementing generative AI. So, which approach is better? Let's take a closer look at both.

What are closed-source LLMs?

Closed-source LLMs are large language models with code, training data, and model weights that are not publicly available. These models are typically developed and maintained by private companies that keep the inner workings of the technology proprietary, so only the company that built it has complete control. 

Instead of letting users obtain and modify with the closed-source model, companies usually offer controlled access through paid services or APIs. This means specialists can use the closed models for their projects, but only on the companies' terms and within their limits.

Why do companies, instead of handing their users the keys, let them only use their model through APIs or platforms, often for a price? Well, training LLMs is expensive. Companies spend millions of dollars, leverage massive computing power, and conduct years of research to make powerful models. By keeping their work closed, companies can protect their investment, control how the models are used, and build a business around them.

Pros of closed-source LLMs

Closed source models are easy to access

Most closed-source large language models are designed to be easy to use. As mentioned, businesses or individuals can start using them quickly through simple interfaces like APIs or external platforms. There's no need for expensive hardware or technical expertise, which lowers the barrier to entry.

Better safety measures

Since the developers control access, they can add safeguards to prevent harmful or unethical use. For example, they can block certain types of content or filter out offensive language. Companies can also build filters and safeguards to reduce harmful outputs like misinformation or biased results. 

Consistent performance

Businesses put in a lot of effort to make sure they work consistently. Updates and optimizations are centrally managed. This means users don't have to worry about outdated versions, which is common for open-source tools. So, the model stays up-to-date with dedicated support from the creators and without extra effort from users.

Cons of closed-source LLMs

Lack of transparency

Since the inner workings are hidden, users can’t see how the model makes decisions or understand its potential biases. Transparency is often crucial in industries like healthcare or legal, so this is a big concern for researchers.

Vendor lock-In

Switching to another tool can be a headache once the business builds its workflow around a closed-source LLM. Because it is hard to switch to another provider after years of reliance on a closed-source LLM since the company's whole workflow was built around a specific system.

Limited customization

Unlike open-source projects, which can be adapted to specific needs, closed models often have rigid functionalities. Users are usually stuck with what the company provides. Customizing these models is generally impossible unless the company explicitly allows it via API configurations or specialized agreements.

What are open-source LLMs?

Open-source models are the tools that are shared freely with the world so anyone can use, study, and improve them. Unlike closed-source models, open-source LLMs let users see how they’re built, tweak them for their needs, and even contribute to improving them. In other words, the source code in such models is available to the public.

Open-source software is all about freedom and creativity. They give researchers the tools to explore new ideas, empower small businesses to build with AI, and allow developers to create solutions that meet their specific needs. Open-source models let developers with limited resources leverage advanced AI capabilities.

What makes the companies that build such models keep them open? For many organizations, the decision to go open-source is rooted in the belief that knowledge and technology should be accessible to everyone. Creators, especially in academia or non-profits, often aim to advance AI as a field. Keeping their models open ensures that anyone can study, use, or improve them. It's about creating a space to accelerate innovation globally and encourage collaboration across industries.

Open-source solutions thrive on community input. When thousands of people can inspect the code, test the model, and suggest improvements, it gets faster than if only one company worked on it. Because of this global collaboration, open projects often grow in unexpected and exciting ways.

Pros of open-source models

Cost-free access

Open-source models like GPT-NeoX or BLOOM are free, which means that there is no need for expensive licensing fees. Startups with limited budgets can use these models to prototype AI applications without upfront costs.

Full customization

Open-source models can be fine-tuned or altered to meet unique needs. For instance, a healthcare company could adapt a model to process medical data or translate terminology into plain language. Such flexibility makes it possible to align the model with niche use cases.

Transparency in development

Projects like BLOOM provide full access to the training data and methodologies. This transparency allows researchers to analyze potential biases or limitations. It also gives organizations an opportunity to trust the model’s outputs and adjust them as needed.

Strong community support

Popular projects often have active developer communities, contributing tutorials and necessarytools. Such ecosystem can make implementation easier and faster for organizations leveraging these models.

Cons of open-source models

High technical barrier

Deploying open-source models requires expertise in AI and software engineering. For example, running a model like LLaMA might involve setting up servers, optimizing GPU usage, and understanding frameworks like PyTorch or TensorFlow.

Limited support options

Open-source projects do not include dedicated support teams. When problems arise, the only resources are forums, GitHub, or online communities. For example, if a bug occurs during deployment, finding a solution may take significant time.

Security and misuse risks

Open access to model code increases the risk of misuse. For example, malicious actors have used fine-tuned versions of open models to generate spam, deepfakes, or misleading content. This is a challenge for developers to maintain ethical usage.

Inconsistent quality

Not all open-source projects maintain the same level of reliability or polish. Some may lack thorough documentation or have incomplete features. Less popular models might have outdated codebases or untested functionality, which can potentially lead to implementation challenges.

Examples of both types of LLMs

Open-source LLMs

LLaMA (Large Language Model Meta AI)

LLaMA by Meta is known for being lightweight and efficient. It doesn't require the massive computing power that some other models demand, which makes it accessible for smaller teams and individual developers. Because it's open-source, developers can fine-tune it for specific use cases like chatbots, summarization tools, or even research in niche fields.

BLOOM (BigScience Large Open-science Open-access Multilingual Language Model) 

BLOOM is an open-source model that is all about inclusivity. It is a large-scale, multilingual language model developed as part of the BigScience project, a collaborative effort involving over 1,000 researchers from 60 countries. It supports 46 human languages and 13 programming languages, which is an excellent option for building tools for global audiences or underserved linguistic communities. Plus, being open-source means anyone can adapt it to their needs.

GPT-J / GPT-NeoX

These EleutherAI models aim to provide open alternatives to proprietary models like GPT-3, making high-quality language models available to the broader AI community. They’re smaller in scale but still capable of tasks like writing text, summarizing content, and answering questions. As far as features go, GPT-J is similar to ChatGPT, although it doesn't act as a chatbot; it only predicts text.

GPT-NeoX is a library/framework for training large-scale transformer models, including GPT-like architectures. It is not a single model but a set of tools and optimizations. GPT-NeoX-20B is a specific large-scale language model trained using the GPT-NeoX framework. Unlike proprietary models like GPT-4, GPT-NeoX-20B is freely available for researchers and developers. 

Falcon

Falcon by Technology Innovation Institute (TII) is a family of LLMs gaining popularity for its ability to compete with larger closed-source models in terms of quality. Falcon has been praised for its performance, often rivaling closed-source models while remaining free. It’s designed to be high-quality and versatile, making it popular among those who want the best of both worlds: openness and performance.

Closed-source LLMs

GPT-4

GPT-4 is among the most advanced and widely used language models available today. It powers various applications, from conversational agents to creative tools for writing, art, and even code. OpenAI offers robust APIs that make integration into apps and workflows easy, and the model itself is incredibly versatile.

Claude

Claude by Anthropic focuses heavily on safety and alignment, designed to minimize harmful or biased outputs. It's perfect for industries where ethical considerations are critical, like healthcare, education, or financial services. Claude excels at understanding and generating human-like text, making it useful for text generation, question answering, data analysis, etc.

PaLM 2

Used in Google’s bot Bard AI, PaLM 2 is a versatile and powerful language model optimized for a variety of applications, including creative writing, coding, and search. It integrates seamlessly into existing systems and is an attractive option for companies already using Google’s services.

Key differences between open-source and closed-source large language models

Cost

Open-source models
Free to access and use, but may require investment in infrastructure that, for example, includes cloud servers, GPUs, etc., and technical expertise to manage and maintain.
Closed-source models
Operate on a subscription or pay-per-use model. Costs are predictable for not frequent use but can escalate for high-volume applications. Infrastructure is handled by the vendor.

Customization

Open-source models
Fully customizable. Users can fine-tune the model, retrain on specific datasets, or modify the code to suit unique needs.
Closed-source models
Limited to options provided by the vendor, such as fine-tuning via APIs. Modifications are constrained within the vendor’s ecosystem.

Transparency

Open-source models
Fully transparent systems. Source code, architecture, and sometimes datasets are openly available.
Closed-source models
Operate as a black box. Users cannot inspect or modify the internal workings as a source code in such models is not available to the public

Performance

Open-source models
Potentially high performance, but results depend on the user’s ability to optimize and scale the model effectively.
Closed-source models
Designed for consistency and reliability, it offers high-quality performance out of the box.

Ease of use

Open-source models
Requires technical expertise for deployment, scaling, and maintenance. Users must manage hosting, data pipelines, and troubleshooting.
Closed-source models
No setup or hosting is required. Integration is typically done through APIs or user-friendly platforms.

Scalability

Open-source models
Users manage scalability themselves, requiring advanced setups to handle high traffic or workloads.
Closed-source models
Scalable by default and handled entirely by the provider. Users pay for the level of service they need without worrying about infrastructure.

Support

Open-source models
Community-driven support through forums, GitHub repositories, and documentation. Help can be slower and less reliable in urgent situations.
Closed-source models
Dedicated support from the vendor, including technical assistance, detailed documentation, and service agreements for large customers.

Innovation and ecosystem

Open-source models
Encourages collaborative innovation, with contributions from global developers and researchers. Updates and improvements come from a decentralized community.

Closed-source models
Innovation is centralized and driven by the vendor’s priorities. Users benefit from proprietary achievements but have limited influence on the development process.

Innovation and ecosystem

Open-source models
Encourages collaborative innovation, with contributions from global developers and researchers. Updates and improvements come from a decentralized community.

Closed-source models
Innovation is centralized and driven by the vendor’s priorities. Users benefit from proprietary achievements but have limited influence on the development process.

Control

Open-source models
Full control over the model, including deployment location, data privacy, and security measures. Users can tailor the model to align with specific regulations.
Closed-source models
Control is limited to what the provider allows. The provider manages security, privacy, and updates, but users cannot enforce their standards directly.

Ethics and bias

Open-source models
Users can inspect and address potential biases or ethical concerns within the model. Transparency aids in compliance with regulations.
Closed-source models
Ethical decisions and bias mitigation depend entirely on the vendor. Users have no visibility into how these issues are addressed.

Best for

Open-source models
Niche applications requiring full customization, industries requiring regulatory compliance, or users with sufficient technical expertise for large-scale deployment.
Closed-source models
Commercial applications, businesses needing seamless integration, or users who don't have the technical resources to manage their own infrastructure.

Factors to consider when choosing between open-source and closed-source models

The choice between open-source and closed-source large language models can significantly impact AI projects. On the one hand, open-source models provide flexibility and transparency. They’re freely available; users can download and modify them to fit specific needs. Such openness encourages innovation, enabling startups, researchers, and machine learning developers to experiment and build AI solutions without the high costs associated with proprietary systems. They’re especially appealing for projects requiring unique functionalities.

Closed-source LLMs, on the other hand, offer a more polished, fully packaged experience. Built and maintained by industry giants like OpenAI, Anthropic, or Google, these models often come with robust performance, reliability, and business-level support. They're designed to integrate seamlessly into commercial applications, eliminating the need for users to manage infrastructure or customise code. Thanks to the immense resources poured into their development, closed-source models also tend to have access to cutting-edge advancements.

So how does a company decide which path to take? It depends on what matters most - control and flexibility, or simplicity and support. When it comes to choosing between open-source and close-source language models, there’s no universal answer. It all depends on what’s needed and what’s realistic. Both options have their strong points and challenges, and the right choice comes down to priorities.