Open-source annotation tools are widely used in computer vision and machine learning projects, offering advantages such as cost-effectiveness for startups and academic projects. However, they also come with significant downsides, including difficulty scaling annotation projects, weak data security, and limited collaboration features. Open-source tools often require manual configuration and adaptation to meet specific project needs, which can be time-consuming and inefficient compared to commercial solutions that offer automation features, quality control processes, and audit trails. While open-source tools are free to use, the lack of support from a single vendor and the need for community-driven development can make it challenging to find suitable tools for complex projects. In contrast, commercial annotation tools provide a more streamlined experience, scalable solutions, and robust security features, making them a better choice for large-scale projects or those with stringent regulatory requirements.