What Labelbox does
Labelbox is an AI data platform for cataloging datasets, building annotation workflows, using model-assisted labels, evaluating models, and engaging expert labeling services.
Labelbox connects data management, annotation, model-assisted labeling, evaluation, and professional data services. It supports images, text, conversational data, audio, documents, video, geospatial and medical tiled imagery, and multimodal formats. Ontologies, roles, workflows, consensus, review, model runs, embeddings, APIs, and the Alignerr expert network let organizations combine internal experts with external contributors.
Billing uses Labelbox Units whose consumption varies by modality and product. Official limits list 500 free LBUs each month and Starter at a fixed $0.10 per LBU; Enterprise is negotiated. Catalog storage can recur monthly, Annotate and Model actions have different one-time conversions, and Foundry inference, labeling services, SSO, or HIPAA support may add charges. Model a real corpus—including video frames and PDF pages—and confirm current plan, minimums, overages, services, and export terms.
A model-generated pre-label can anchor annotators toward the same wrong answer, while consensus can obscure minority or specialist interpretations. Confirm rights for every asset and public dataset, minimize faces, voices, health data and identifiers, isolate restricted projects, and define how external workers may access content. Calibrate with blinded ground truth, measure agreement by class and demographic slice, review skips and edits, compensate specialists appropriately, preserve label lineage, and require internal domain experts to accept the final dataset.
How Labelbox works
Teams register authorized data rows from cloud storage or uploads, define an ontology and multi-stage workflow, and assign tasks to internal or external labelers. Annotate captures human judgments; model predictions or Foundry can pre-label data; Catalog supports search and curation; Model stores runs and metrics, while exports return labels, consensus, reviews, and provenance to training pipelines.
Connect authorized multimodal data
Register cloud assets or upload permitted data with provenance, licenses, consent, PII classification, retention, deletion, and access boundaries. Estimate recurring and action-based LBU use.
Define ontology, roles, and review
Projects translate labels and ambiguity into tasks, consensus, review, escalation, and assignments for internal or external experts. Permissions prevent unrelated workforces from browsing sensitive data.
Combine human and model proposals
Foundry or imported predictions can pre-label data while people correct outputs. Blinded comparisons, gold items, disagreement, rare classes, and demographic slices expose anchoring and model bias.
Release traceable, audited labels
Exports preserve schemas, judgments, reviews, and model lineage. Internal domain owners sample accepted work, reconcile deletes, monitor drift, and test downstream model quality and harms.
How to set up Labelbox
Map data rights and sensitivity
Document collection and training permission, licenses, consent, PII, biometric or health data, retention, deletion, locality, and external-workforce restrictions.
Estimate plan and LBU use
Run representative image, text, document, video, and model actions through current LBU rules; include recurring Catalog use, Foundry inference, services, SSO, and support.
Build ontology and workflow
Define labels, examples, ambiguity, skips, consensus, review stages, roles, assignments, escalation, and access boundaries before importing production data.
Calibrate humans and models
Use blinded expert gold items, duplicate tasks, pre-label on and off comparisons, rare cases, and subgroup slices to measure anchoring, agreement, and accuracy.
Export with continuous QA
Validate schemas and lineage, sample accepted labels, reconcile deletes, monitor worker and model drift, and test downstream performance and bias after every dataset revision.
Labelbox FAQs
Is Labelbox free?
Its official limits document lists 500 free LBUs each month. Once exhausted, additions are restricted until the next period unless the account upgrades.
How much is Labelbox Starter?
Official limits list Starter at $0.10 per LBU. Consumption differs by Catalog, Annotate, Model, modality, frames and pages; verify current checkout.
What costs extra?
Foundry inference, expert labeling services, SSO, HIPAA support, and other Enterprise capabilities can be separate from base LBU consumption.
Can model predictions replace human labeling?
No. Pre-labels improve speed but may anchor people and replicate model bias. Compare against blinded human work and expert-reviewed ground truth.
Who owns responsibility for dataset rights?
The organization supplying data must establish collection, license, consent, privacy, and downstream training rights and configure access and deletion accordingly.
Listing reviewed 2026-07-15. Product details and pricing can change; verify important terms on the provider's website.
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