Open-source large language models (LLMs) like LLaMA have become viable for production use cases, offering significant cost savings compared to proprietary models like GPT-3. While they may lag behind in terms of output quality, instruction following, and function templates, open-source LLMs can still provide good performance for many applications, especially when fine-tuned for specific tasks. The use of hybrid approaches combining both open source and proprietary LLMs can offer a cost-performance tradeoff. As the community continues to innovate and address limitations, open LLMs are becoming increasingly attractive options for developers and businesses, particularly in scenarios where cost-effectiveness is key.