Labelbox
Labelbox helps AI teams produce high-quality training data at scale.
Labelbox is a data-centric AI platform that helps AI teams and Fortune 500 enterprises create, manage, and iterate on high-quality labeled training data at scale. The platform combines a collaborative annotation software with managed labeling services and generative AI automation (Foundry) to reduce the time and cost of producing training data. It processes petabytes of multimodal data and serves over 80% of Fortune 500 AI teams, positioning itself as a command center for the entire ML training data iteration loop.
Problem solved
AI teams waste months and millions of dollars manually labeling and iterating on training data without visibility into model performance or efficient workflows.
Target customer
Fortune 500 enterprises, leading AI labs, and organizations building large-scale ML models that require multimodal data annotation and automated data pipeline management.
Founders
M
Manu Sharma
Co-founder & CEO
Aerospace engineer and product designer with 5+ years building products at Planet Labs; studied at Embry Riddle Aeronautical University.
B
Brian Rieger
Co-founder & COO
Data science professional from Boeing; studied at Embry Riddle Aeronautical University alongside Manu Sharma.
D
Daniel Rasmuson
Co-founder
Technical founder with aerospace and software engineering background.
Funding history
Seed
$3.9M
July 2018
Led by Kleiner Perkins
· First Round Capital
Series A
$10M
2019
Led by Gradient Ventures
· Kleiner Perkins, First Round Capital
Series B
$25M
February 2020
Led by Andreessen Horowitz
· Unknown
Series C
$40M
February 2021
Led by General Catalyst
· SoftBank Vision Fund 2, B Capital Group, Gradient Ventures
Series D
$110M
January 2022
Led by SoftBank Vision Fund 2
· Snowpoint Ventures, Databricks Ventures, B Capital Group, Andreessen Horowitz, ARK Invest
Total raised:
$189M
Pricing
Multi-tiered SaaS model with usage-based components. Free tier for evaluation, Starter and Enterprise tiers available. Pricing based on data volume (Labelbox Units/LBUs), number of users, and features. Enterprise contracts represent ~70% of revenue. Estimated ARR exceeds $100M by 2025. Labelbox Boost managed annotation service sold separately.
Notable customers
80% of Fortune 500 AI teams (specific named customers not disclosed in research)
Website
Competitors
Scale AI
Also offers managed data annotation and labeling platform, but focuses more heavily on outsourced annotation services rather than platform-first approach.
Prodigy
Lighter-weight annotation tool focused on NLP use cases; lacks the enterprise scale, managed services, and automation features of Labelbox.
Segment Anything (SAM) / Open-source alternatives
Free open-source tools lack managed services, enterprise support, and integrated workflow automation for production ML teams.
Why this matters: Labelbox has become the de facto standard for enterprise ML data preparation, with 80% of Fortune 500 AI teams relying on it. The launch of Foundry in late 2024 using generative AI to automate labeling represents a significant competitive moat, transforming the category from manual annotation tools into an AI-powered data factory that directly reduces training costs and time-to-model.
Best for: Enterprise AI teams and Fortune 500 companies building production ML models that require scalable, high-quality labeled data with integrated error analysis and automated data pipeline workflows.
Use cases
Computer Vision Model Training at Scale
AI teams use Labelbox to annotate millions of images with bounding boxes, polygons, and semantic segmentation. The platform's collaborative features let distributed annotation teams work simultaneously while data scientists monitor quality and refine annotations in real-time, reducing iteration cycles from months to weeks.
LLM Training Data Preparation
Organizations preparing data for large language model fine-tuning use Labelbox's NLP tools (NER, sentiment analysis, classification) to label text at scale. The Foundry automation suite uses generative AI to pre-label text, dramatically reducing manual effort while maintaining quality standards.
Continuous Model Improvement Loop
Production AI teams use Labelbox as a command center to identify model failure modes through error analysis, augment underperforming data, refine annotations, and retrain models. This closed-loop workflow reduces time-to-model-improvement from months to weeks.
Alternatives
Scale AI
More annotation service-centric; better for teams wanting to outsource labeling entirely rather than manage internal annotation workflows.
Prodigy
Lightweight, developer-friendly annotation tool best for smaller teams and NLP-focused projects; lacks enterprise features and managed services.
Amazon SageMaker Ground Truth
AWS-native solution better for organizations already deeply embedded in AWS; less sophisticated automation and smaller community than Labelbox.
FAQ
What does Labelbox do? +
Labelbox is a data-centric AI platform that helps teams create, annotate, and iterate on training data at scale. It combines collaborative annotation software with managed labeling services and generative AI automation (Foundry) to reduce the time and cost of producing high-quality training data for machine learning models.
How much does Labelbox cost? +
Labelbox uses a multi-tiered SaaS model with usage-based pricing. Costs depend on data volume (measured in Labelbox Units), number of users, and features. Free tier available for evaluation; Starter and Enterprise tiers for production use. Estimated ARR exceeds $100M, with enterprise contracts representing ~70% of revenue. Contact sales for custom pricing.
What are alternatives to Labelbox? +
Scale AI (annotation service-focused), Prodigy (lightweight NLP annotation), and Amazon SageMaker Ground Truth (AWS-native solution). Labelbox differentiates through its enterprise-scale automation, managed annotation services, and closed-loop ML workflow integration.
Who uses Labelbox? +
Over 80% of Fortune 500 AI teams use Labelbox, along with leading AI labs and enterprises building large-scale machine learning systems. Target customers are organizations requiring high-quality multimodal data annotation and automated ML pipeline management.
How does Labelbox compare to Scale AI? +
Both offer annotation services and platforms, but Labelbox is more platform and automation-first with features like Foundry (generative AI pre-labeling), error analysis, and integrated retraining workflows. Scale AI is stronger in pure managed annotation services for outsourcing. Labelbox is better for teams wanting internal control and automation; Scale AI for outsourcing-focused teams.
Tags
machine learning
data labeling
annotation
training data
computer vision
NLP
data-centric AI
generative AI automation
enterprise AI
multimodal data