Growing Popularity of Open-Source LLMs
More and more companies are opting for open-source solutions when using Large Language Models (LLMs). According to a recent study by Databricks, 76 percent of companies that use LLMs are already utilizing open-source models—often alongside proprietary solutions like OpenAI's GPT.
Source: https://www.databricks.com/resources/ebook/state-of-data-ai
Advantages of Open-Source LLMs Over Proprietary Solutions
Open-source LLMs offer companies significant advantages compared to closed systems like OpenAI. The biggest advantage is flexibility: companies can customize the models to their specific requirements and industries, which is especially important for firms in niche markets or with sensitive data.
Data Sovereignty and Security as Key Benefits
Another central advantage of open-source LLMs is increased data sovereignty. Unlike cloud-based services like OpenAI, where data leaves the company, open-source models can be operated on the company's own infrastructure. This ensures compliance with data protection policies and enables the secure processing of highly sensitive information.
Cost Advantages and Long-Term Savings
The use of open-source LLMs offers significant cost advantages, especially for companies with intensive use and high customization requirements. While APIs from providers like OpenAI are initially cost-effective and easy to set up, fees increase rapidly with regular and extensive use. Open-source models require higher initial investments in hardware, infrastructure, and expertise but offer long-term flexibility and full control over the solution—without dependence on license or usage fees from API providers.
Moreover, this independence allows for targeted optimizations that meet the company's specific requirements, as well as more secure management of sensitive data. Particularly for data-intensive or security-critical applications, the use of open-source LLMs is often a cost-effective and sustainable choice, as it creates predictable cost structures and ensures complete control over the system.
Independence from External Providers
Another advantage of open-source LLMs is independence from large providers. Companies are no longer subject to price changes or potential service restrictions from external AI providers. This independence creates planning security and reduces potential risks for business-critical applications. Given the constant release of new open-source LLMs, it makes sense to build infrastructure in a way that allows LLMs to be configurable and updated to optimally benefit from advancements.
Open-Source LLMs in Regulated Industries
Highly regulated industries such as the financial sector are also pioneers in adopting open-source LLMs, which underscores confidence in their security and compliance capabilities. An example is the use of open-source LLMs to analyze financial market data and assist in detecting fraud or money laundering activities. Here, open-source models can be specifically trained on internal data and industry-specific requirements without having to share sensitive information with external API providers.
Through this control, financial companies can ensure that their systems operate in compliance with GDPR and BaFin regulations while also being flexible enough to adapt to new legal requirements. Thus, the technology becomes a valuable support in ensuring both operational efficiency and regulatory security.
Challenges in Implementing Open-Source LLMs
However, switching to open-source LLMs is not without challenges. Companies need to invest in expertise and suitable infrastructure. Fortunately, there are increasing solutions that facilitate this transition. For instance, models like Llama-3 can now also be used via cloud services like Azure AI or AWS, allowing for a gradual transition.
Conclusion: Open-Source as the Future of Corporate AI
Experts agree that the future of AI in companies will be significantly shaped by open-source technologies. This development promises not only more innovation and competitiveness but also a more transparent and democratic AI landscape, where companies retain full control over their data and applications.
