Artificial intelligence (AI) is a new form of how business is done, which is based on Large Language Models (LLMs). They are the ones that equip machines to understand, develop, and speak like human beings. The LLMs are currently used in chatbots, virtual assistants, data analysis, customer services, and much more.

It is observed that by 2025, generative AI will be used to aid the operations in businesses, and 67% of all businesses around the world will use LLMs in this context. But there is one important question first: do you want an Open-Source or a Closed-Source LLM?

Availability

Open source LLMs are available to anyone to use, as well as modify. You may have a look at the code. It is possible to make any changes in the code according to your requirements.
On the other hand, companies own closed-source LLMs. Their code is not visible or alterable. They can be utilized only in the way the company is willing.

Freedom

You have complete control over the model because it is open source. It will be self-trainable for you. Modifications could be made at any moment. Additionally, features can be added or removed.
Closed source restricts your freedom. The business provides just what you need. You’ll have to wait till they give you an update if you want more.

Fees

Open source LLMs are available for free. There is no such thing as a licence fee. You could have to buy programmers or servers for it. However, you will be able to avoid regular software costs.
Meanwhile, the closed-source models are likely to have monthly payments. They can do it based on the usage frequency of how much you utilize them. The costs can escalate at any time.

Privacy

The open source has more privacy if you are self-hosting the model. You own your data. It will enable you to save sensitive information.
The information shared using closed source is conveyed to the server of another company. You hand over your business data to them. This can be hazardous in an industry such as health or even finance.

Support and Ease of Use

Most of them are more comfortable with closed-source LLMs. They are also persevered with predispositions. There is an assistance team that you can talk to in case something breaks. The open-source models are not officially supported. You will be obliged to rely on forums or the team. It would be restrictive in the event that you develop a problem.

Updates and Performance

The closed-source models can work better off-the-shelf. They are trained using more data. They are tested and optimized by enormous working forces of experts. This should be done under open source models. But when such are adjusted well, they can provide very good outcomes to your work.

Special Add-ons and Features

Unique features can be made with open source. You can create something made just for you. Your team can change the kind of thinking or response. The academic open-sourced LLMs do not permit you to do that. You will just be left with what the company provides. There is no way you can find new approaches to making it work.

Deployment Time

The closed-source models are ready at once. No program has to be installed. You use them on the Internet. The open source models take time to set up. You need servers and people to implement and determine how to use them. Closed source can be faster in the case of a business that is in a hurry.
Laws of Data Ownership
It’s all yours when it comes to open source. You keep your info. Your model does not abandon you. You have to abide by the company’s guidelines for the closed source. They can typically memorise your details.

Conclusion

LLMs are efficient tools. However, the appropriate type to use, that is, open or closed, should be based on the project requirements, talent, budget, and schedule of your project. This decision is not to be made at a mad pace. Consider what kind of control, cost, and support you have now and in the future.

With Chapter247, you can navigate each step of the AI process, as we’re here to help you make the right decision about your LLM and deploy it all the way. Our experts will assist you in the creation of smarter solutions, whether you need to scale up or start small.