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Calcimator

Data Annotation Cost Calculator

Estimate labeling costs for ML datasets by task type, dataset size, and annotator rates.

Inputs

$

Results

Hours Needed

83.3 hrs

Total Cost (incl. QA)

$1,562.44

≈ 12 pairs of sneakers

Cost per Sample$0.16
QA Overhead Cost$312.49
How to Use This Calculator
  1. Enter Dataset Size (samples).
  2. Select Annotation Task Type: Classification, Bounding Box, Segmentation, or Named Entity Recognition.
  3. Adjust Annotator Rate ($/hr) as needed.
  4. Review Hours Needed (hrs) and Total Cost (incl. QA) ($).
  5. Use Cost per Sample ($) and QA Overhead Cost ($) to inform your decision.

How the result changes with Dataset Size (samples)

Dataset Size (samples)Hours NeededTotal Cost (incl. QA)
1,000,0018,333.3 hrs$156,250.13
3,500,00129,166.7 hrs$546,875.25
6,500,00054,166.7 hrs$1,015,625.06
9,000,00075,000 hrs$1,406,250.00

What each input means

Dataset Size (samples)
Total number of data samples to annotate
Annotation Task Type
More complex tasks (segmentation) take significantly longer per sample
Annotator Rate ($/hr)
Hourly rate for data annotators (varies by region and skill)

How this is calculated

Worked example, using the default values

  1. Identify Input Parameters
    Dataset Size (samples) = 10000, Annotation Task Type = 1, Annotator Rate ($/hr) = 15 = 3 input(s) provided
  2. Calculate Hours Needed
    Hours Needed
    83.33 = 83.33
  3. Calculate Total Cost
    Total Cost
    1562.44 = $1,562.44
  4. Calculate Cost per Sample
    Cost per Sample
    0.1562 = $0.156
  5. Calculate QA Overhead Cost
    QA Overhead Cost
    312.49 = $312.49

Engine last updated . Checked against 2 independently-derived tests — how we verify calculators.

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