Open-source Large Language Models (LLMs) have become increasingly attractive to enterprises due to their flexibility and applicability across several use cases. However, adopting these models isn't without challenges. Understanding the disadvantages of open source large language models is crucial for AI developers and technology leaders aiming to implement them effectively and securely. Five critical risks associated with open-source LLMs include limited resources and professional support, security vulnerabilities, quality control and hallucination risks, compliance and regulatory challenges, and implementation and integration difficulties. To mitigate these risks, enterprises can invest in specialized talent, implement evaluation and monitoring tools, develop internal support mechanisms, adopt best practices, conduct regular code reviews, monitor dependencies closely, establish security protocols, implement rigorous evaluation and testing methods, maintain detailed documentation, perform regular audits, start with pilot projects, invest in training, leverage specialized tools, and plan for scaling. Leveraging platforms like Galileo can also enhance model reliability and security while ensuring alignment with industry standards. By proactively planning for these resource limitations, understanding potential security risks, addressing quality control issues, navigating compliance challenges, and implementing effective implementation strategies, enterprises can better leverage open-source LLMs while minimizing operational risks.