Data Annotation Cost Calculator

Different labeling tasks require fundamentally different amounts of annotator effort per sample. A classification label is a single click; a segmentation mask requires carefully tracing object boundaries pixel by pixel, which can take ten times longer or more. Since total cost scales with total annotator hours, switching task type at the same dataset size can change the estimate by several multiples.

Most production annotation workflows include a review pass -- a second annotator or reviewer checking a sample of labels for accuracy and consistency, and correcting errors before the dataset ships. This calculator applies a flat 25% addition to base labor cost to represent that overhead; real QA effort varies with task complexity and how rigorous the quality bar is, so treat this as an illustrative planning figure rather than a fixed industry rate.

The two biggest levers are task complexity and annotator rate: choosing the simplest labeling scheme that still meets your model's needs (for example, bounding boxes instead of full segmentation when precise pixel boundaries aren't required) cuts time per sample directly, and sourcing annotators at a lower hourly rate reduces cost proportionally -- though often at some tradeoff in label quality or consistency that the QA overhead only partially offsets.

No -- this estimates only annotator labor and QA review time and cost. It does not include annotation platform or tooling fees, project management overhead, or the cost of an initial pilot round to calibrate task instructions, all of which add to the total cost of standing up a labeling effort in practice.