Annotation and labeling of raw data, such as images and videos, are crucial yet labor-intensive steps in developing machine learning (ML) models, significantly impacting their performance. Organizations across various sectors rely on these models to analyze patterns and interpret trends from visual datasets. The decision to either outsource the annotation process or keep it in-house involves weighing factors such as cost, quality control, and data security. In-house annotation offers advantages like closer oversight and better IP protection but can be costly and resource-intensive. Conversely, outsourcing can be more cost-effective and flexible, especially when working with experienced providers, though it comes with its own risks, such as possible quality issues and less control. The article provides an in-depth comparison of the two approaches and offers best practices for working with outsourced providers, emphasizing the importance of quality assurance and performance tracking tools, such as Encord, to enhance the annotation process and improve ML outcomes.