Open source vs. closed source in the AI world

Open source vs. closed source in the AI world

Read this blog post to find out what role open source will play in the age of artificial intelligence. Spoiler: Open Source remains strong.

ChatGPT vs. open source models

The discussion about artificial intelligence is characterized by the question of whether open AI models or closed systems are superior. Compared to closed models, open models with free source code offer more data sovereignty and design freedom, such as OpenAI’s ChatGPT, which is accessible for a fee via API interfaces. In the past year, the proportion of open source in AI has increased significantly. Companies like Stability AI, OpenAI (formerly open), Mistral and Hugging Face show that open models are more flexible. These enable the formation of communities and ecosystems that can be further developed by many. In contrast, closed models are less adaptable and offer only limited scope for change. An example of the effectiveness of open models is provided by Metathe Llama 2 models to strengthen the Metaverse ecosystem.


Open source AI for independence and added value

One central advantage of open source is the separation of the training and operating phases of the models, which promotes independence and added value. Companies such as Mistral and Hugging Face offer platforms for open AI models that can be qualitatively superior to closed models due to their adaptability and lower operating costs. This opens up new possibilities that also lead to new hardware concepts. Open source AI not only opens up economic opportunities in the distant future; people can already test and use some open AI models on their own devices today.


OpenAI API vs. self-hosted LLMs: A cost consideration

In the context of this discussion, it is also important to consider cost aspects when using AI models. Self-hosted LLMs require significant resources for deployment, DevOps and ML engineering. Costs can range from $40k to $60k per month on GCP for high performance models like LLaMA-2-70B. In comparison, the use of the OpenAI API with low usage (less than 10,000 requests per day) shows lower costs and enables a faster time to market for prototypes.


Data protection and control: weighing up the pros and cons

Another important factor when deciding between OpenAI and self-hosted models is data protection. Self-hosted solutions offer more control and privacy, while OpenAI APIs can use data to improve services. Overall, there is no clear answer to the question of whether OpenAI or self-hosted models are better. The choice depends on the specific requirements, resources and priorities. Self-hosted models are relevant for data protection and compliance, while OpenAI APIs are recommended for rapid testing and prototyping. One possible strategy is to create a prototype with the OpenAI API and then replace functions with self-hosted models as required. Careful analysis and individual consideration are crucial.


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