Build vs buy: A decision framework for data labeling tools

Eric Landau's guide examines the complex decision-making process behind building versus buying data labeling tools, emphasizing the often underestimated costs and challenges of in-house development. Initially, teams might drift into building their own tools through incremental steps, but the real expenses come post-launch, including maintenance, feature requests, and the opportunity cost of engineers being diverted from core product development. The document highlights potential pitfalls such as technical debt, key-person risk, and the limitations of open-source tools, which can lead to issues like inconsistent data annotations that impact model performance. While building in-house may be justified for unique workflows or small, stable projects, the guide suggests that buying a platform often offers advantages in scalability, compliance, and maintaining engineering focus on product enhancements. It also underscores the importance of evaluating these choices with tools like Encord's build vs. buy calculator to account for long-term costs and operational impacts.

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