Open-source versus Closed-source language models: Which is the best choice?

You're increasingly hearing the distinction between open-source and closed-source language models. But what exactly do these terms mean, and when do you choose which type of language model? In this article, we'll dive deeper into the features, advantages, and disadvantages of both, so you can make an informed choice for your organization.

What are Open-Source Language Models?

Open-source language models, such as LLama and Mistral, provide full transparency by making their source code publicly available. This means that everyone has access to the basic algorithm, which makes it possible to open the “black box” and adapt the model to specific needs. One of the major advantages of open-source is that these models can be run on-premise, within your own IT and data environment. This is particularly attractive for organizations with strict security guidelines, such as banks and insurers, because all data remains indoors and does not have to be shared with external tech parties.

In addition, open-source models are often considerably cheaper to use. Since they are freely available and can be run locally, companies that have a lot of data traffic, such as customer service departments, can benefit from lower costs.

What are Closed-source language models?

In contrast, closed-source language models, such as OpenAI's ChatGPT and Google's Gemini, are closed systems. The algorithm remains a black box, and users have no access to the underlying code. However, this makes them very accessible; via an API or a chat interface, you can easily use the model without in-depth technical knowledge.

Another advantage of closed-source models is that safety is often well guaranteed, even in the cloud, thanks to dedicated teams that maintain and secure the model every day. This lowers the technical barrier for organizations that want to quickly take advantage of the power of language models without having to invest in a technical team.

The disadvantage of closed source, however, is that your data is always indirectly shared with a tech party and that the model depends on cloud infrastructure.

Open-source versus Closed-source: Which do you choose?

When your organization works with highly sensitive data and wants to minimize your dependence on external tech parties, an open-source language model offers a safe and cost-effective solution. These models are flexible, adaptable and can be run on-premise, ideal for companies with stringent security requirements.

On the other hand, closed-source models are easier to use and often offer the latest features developed by specialists. This makes them an excellent choice for companies that want to get started quickly with language models without much technical complexity, but you have to deal with a greater dependence on tech parties and IT providers.

Conclusion

The choice between an open-source and closed-source language model depends heavily on the specific needs of your organization. If you're dealing with sensitive data and strict security guidelines, open-source is probably the best choice. For easier use and access to the latest technologies, closed source is more attractive. Both options have their own advantages, and the right choice can give your organization a significant advantage in the increasingly digitizing world.

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