Labelbox is an API-first data labeling and training-data platform for AI teams. See 2026 pricing by Labelbox Units, key features, pros and cons, and…
Category
AI Infrastructure & MLOps
Pricing
Freemium, from Free (up to 500 LBUs/month); Starter from $0.10/LBU
Verified
Not yet
Last updated
July 18, 2026
Founded
2018
Headquarters
San Francisco, California, United States
Free PlanWeb AppAPIAIFreemium
Overview
Labelbox is an API-first data engine platform that helps AI teams create, curate, and evaluate the labeled training data used to build machine learning and generative AI models, spanning images, video, text, and specialized data like LiDAR and medical imaging.
The platform is built around connected modules, Catalog for data curation, Annotate for labeling, and Model for model-assisted labeling and evaluation, with an optional managed annotator workforce called Boost for teams that need outsourced labeling capacity.
Key Features
Labelbox supports configurable labeling ontologies, review and consensus workflows, and model-assisted pre-labeling to speed up annotation across computer vision, NLP, and multi-modal data types.
A comprehensive API and SDK let engineering teams embed data curation and labeling directly into their own MLOps pipelines, while the Model module adds error analysis and side-by-side model evaluation against labeled ground truth.
Pricing
Labelbox prices usage through Labelbox Units (LBUs), which are consumed at different rates by product: data curation is cheap, labeling is the most expensive, and model evaluation falls in between. A free tier includes up to 500 LBUs per month, plus free access for non-commercial academic use.
The Starter plan is pay-as-you-go at $0.10 per LBU with unlimited users and custom workflows included, while the Enterprise tier offers custom-priced contracts with multiple workspaces, HIPAA and security add-ons, and dedicated support.
Key Features
Catalog data curation — Organize, search, and curate large unstructured datasets including images, video, text, and geospatial or LiDAR data.
Configurable labeling ontologies — Define custom label taxonomies and annotation instructions tailored to each project's requirements.
Model-assisted labeling — Use existing models to pre-label data and accelerate human annotation throughput.
Model evaluation and error analysis — Compare model predictions against ground truth to identify failure patterns and prioritize relabeling.
API-first architecture — A full SDK and API let engineering teams embed labeling and curation directly into MLOps pipelines.
Review and consensus workflows — Multi-reviewer consensus scoring and quality assurance queues help maintain labeling accuracy at scale.
Managed workforce (Boost) — Optional access to a managed pool of human annotators for teams without their own labeling workforce.
Usage-based LBU pricing — Pay only for the data curation, labeling, and model evaluation work actually performed, measured in Labelbox Units.
Pros & Cons
Pros
API-first design makes it easy to embed into existing MLOps and data pipelines
Usage-based LBU pricing scales granularly rather than charging flat per-seat fees
Free tier and academic access make it approachable for smaller projects and research
Model module extends the platform beyond labeling into ongoing model evaluation
Well-funded with a mature product used by both startups and large enterprises
Cons
Closed-source and cloud-hosted, unlike self-hostable open-source alternatives such as Label Studio
LBU-based pricing can be harder to predict than flat subscription pricing
Enterprise features like HIPAA compliance and multiple workspaces require custom, quote-only pricing
Best suited to teams with engineering resources to integrate the API rather than casual point-and-click use
Labeling-heavy workloads consume LBUs faster than curation-only usage, which can raise costs quickly
Pricing
Free Free Monthly, up to 500 LBUs
Starter $0.10/LBU Pay-as-you-go
Enterprise Custom pricing Annual contract
Frequently Asked Questions
What is Labelbox used for?
Labelbox is used by AI teams to curate, label, and evaluate the training data behind machine learning and generative AI models, covering images, video, text, and specialized data types.
How does Labelbox pricing work?
Labelbox charges based on Labelbox Units (LBUs), a usage-based metric that varies by product: data curation is cheap, labeling is the most expensive, and model evaluation is priced in between.
Does Labelbox have a free plan?
Yes, Labelbox offers a free tier including up to 500 LBUs per month, and the platform is also free for non-commercial academic use.
Who founded Labelbox?
Labelbox was founded in 2018 by Manu Sharma, Brian Rieger, and Dan Rasmuson, and is headquartered in San Francisco, California.
Is Labelbox open source?
No, Labelbox is a closed-source commercial SaaS platform, unlike open-source alternatives such as Label Studio.
What are the main alternatives to Labelbox?
Common alternatives include Scale AI, SuperAnnotate, Encord, V7, Dataloop, Kili Technology, and the open-source tool Label Studio.