Learn how to choose between open source and closed source LLMs for your generative AI project. Find Pros and Cons for both & business fit.

Open Source vs Closed Source LLMs: Which One Should You Choose for Your Generative AI Project?

Key Takeaways:

  • Generative AI is powered by Large Language Models (LLMs) that can create novel content from text prompts.
  • Closed-source LLMs via APIs offer easy access and high performance but lack control and customization.
  • Open-source LLMs offer high control and customization but require high expertise and investment.
  • The choice between closed-source and open-source LLMs depends on various factors such as project scope, budget, timeline, expertise, and goals.

Generative AI is the branch of artificial intelligence that can create novel content such as text, images, code, and music. One of the key components of generative AI is the Large Language Model (LLM), a neural network that can learn from massive amounts of text and generate coherent and diverse responses to human prompts.

But not all LLMs are created equal. Depending on your project goals, budget, and expertise, you may have to choose between using a closed-source LLM via an API or building upon an open-source LLM. In this article, we will compare the benefits and drawbacks of both options and help you make an informed decision.

Closed-source LLMs are those that are developed and maintained by private companies such as OpenAI, Google, and Microsoft. These LLMs are often state-of-the-art and offer easy access via an API. Some examples of closed-source LLMs are GPT-4, LaMDA, and ChatGPT.

The main advantages of using a closed-source LLM via an API are:

  • Low initial costs: You don’t have to invest in training your own model from scratch or setting up your own infrastructure.
  • Fast time to market: You can quickly prototype and test your application with minimal coding and prompt engineering.
  • High performance: You can leverage the power and quality of the latest and largest models available.

However, there are also some significant drawbacks of using a closed-source LLM via an API, such as:

  • Lack of control: You have no access to the model architecture, training data, or updates. You have to rely on the provider’s choices and policies, which may not suit your needs or preferences.
  • Lack of customization: You have limited options to fine-tune the model for your specific domain or task. You may also face challenges in filtering out harmful or inappropriate content generated by the model.
  • Lack of differentiation: You may face competition from other users who are using the same model and API. You may have difficulty in creating a unique value proposition for your application.

Open-source LLMs are those that are publicly available and can be modified by anyone. These LLMs are often developed by academic or community-driven initiatives such as Hugging Face, Meta, and UC Berkeley. Some examples of open-source LLMs are BLOOM, LLaMA 2, and Vicuna.

The main advantages of using an open-source LLM are:

  • High control: You have full access to the model architecture, training data, and updates. You can adjust the model to your needs and preferences, as well as perform counter-deployments if needed.
  • High customization: You can fine-tune the model for your specific domain or task, as well as apply content filters or other enhancements to improve the quality and safety of the generated content.
  • High differentiation: You can create a unique value proposition for your application by leveraging the model’s features and capabilities.

However, there are also some significant drawbacks of using an open-source LLM, such as:

  • High expertise: You need to have deep learning and machine learning expertise to fine-tune and deploy the model. You also need to have prompt engineering and RAG skills to interact with the model effectively.
  • High investment: You need to invest in training your own model from scratch or building upon a pre-trained model. You also need to set up your own infrastructure and manage the scalability and security issues.
  • High risk: You may face legal or ethical issues related to the use of open-source models or data. You may also face technical or operational challenges related to the maintenance and reliability of the model.

So, which option should you choose for your generative AI project? There is no definitive answer, as it depends on various factors such as your project scope, budget, timeline, expertise, and goals. However, here are some general guidelines to help you decide:

  • If you want to create a simple proof-of-concept or a prototype with low costs and fast results, you may opt for a closed-source LLM via an API.
  • If you want to create a complex or customized application with high control and differentiation, you may opt for an open-source LLM.
  • If you want to create a hybrid solution that combines the best of both worlds, you may opt for a combination of both options.

We hope this article has helped you understand the benefits and drawbacks of open source vs closed source LLMs for generative AI. If you need more guidance or assistance, feel free to contact us. We are a team of experienced and qualified business consultants who can help you with your generative AI project.