Open-source versus Closed-source Language Models: What is the best choice?

You increasingly hear the distinction between open-source and closed-source language models. But what exactly do these terms mean, and when should you choose which type of language model? In this article, we delve deeper into the characteristics, advantages, and disadvantages of both, so you can make an informed decision for your organisation.

Open-source language models, such as LLaMA and Mistral, offer complete transparency by making their source code publicly available. This means that everyone has access to the basic algorithm, making 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 an organisation's own IT and data environment. This is particularly attractive for organisations with strict security guidelines, such as banks and insurers, because all data remains in-house and does not have to be shared with external tech parties.

Moreover, open-source models are often considerably cheaper to use. As they are freely available and can be run locally, companies with high data traffic, such as customer service departments, can benefit from lower costs.

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

Another advantage of closed-source models is that security is often well-guaranteed, even in the cloud, thanks to dedicated teams who work daily on the maintenance and security of the model. This lowers the technical barrier for organisations that want to quickly benefit from 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 is dependent on cloud infrastructure.

If your organisation works with highly sensitive data and wants to minimise its dependence on external tech parties, an open-source language model offers a secure and cost-efficient solution. These models are flexible, adaptable, and can be run on-premise, which is ideal for companies with strict security requirements.

In contrast, 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 quickly start using language models without much technical complexity, but you will have greater reliance on tech parties and IT providers.

The choice between an open-source and closed-source language model depends heavily on the specific needs of your organisation. If you deal with sensitive data and strict security guidelines, then open-source is probably the best choice. For simpler 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 organisation a significant head start in an increasingly digitised world.

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