Organizations involved in machine learning (ML) and computer vision (CV) projects historically needed to develop their own data annotation tools, but now a variety of off-the-shelf solutions are available, such as Encord. This shift allows companies, regardless of size, to choose between building a custom tool in-house or purchasing a ready-made one. Building internally can be costly and time-consuming, often taking 9 to 18 months and requiring significant resources, whereas buying an existing solution can be more cost-effective and quicker to deploy. Encord, for example, offers a versatile platform that enhances labeling accuracy and efficiency, supports API integrations, and provides features such as active learning pipelines and quality control, which are crucial for improving model performance. The decision to build or buy depends on factors like budget, timeline, and the specific needs of the annotation project, but many find purchasing an off-the-shelf tool like Encord saves time and resources while increasing productivity and scalability.