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Labelbox

  • What it is:Labelbox is a data infrastructure platform that combines software tools and expert labeling services to build, manage, and scale high-quality training data for AI models.
  • Best for:AI/ML engineering teams, Mid-sized AI companies (50-500 employees), Teams needing iterative labeling
  • Pricing:Starting from 500 LBUs/month
  • Rating:88/100Very Good
  • Expert's conclusion:Labelbox is an Essential Tool for Serious ML Teams Building Production Models That Require High-Quality Scalable Training Data Preparation.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Labelbox and What Does It Do?

Labelbox has been a top provider of data-centric AI solutions since 2018 by offering a suite of products and services that help organizations create, manage, and grow their AI model training data.

Active
📍San Francisco, CA
📅Founded 2018
🏢Private
TARGET SEGMENTS
AI TeamsMachine Learning EngineersEnterprise AI LabsResearch Organizations

What Are Labelbox's Key Business Metrics?

📊
$189M
Total Funding
📊
$110M
Latest Funding Round
📊
5
Funding Rounds
💵
$21M
Revenue
🏢
151
Employees
Rating by Platforms
4.5/ 5
G2 (150 reviews)
Regulated By
SOC 2(USA)

How Credible and Trustworthy Is Labelbox?

88/100
Excellent

In terms of Credibility, Labelbox has demonstrated this through several areas including substantial venture funding, a significant presence within the marketplace since 2018, and an enterprise-grade approach to building AI data infrastructure with demonstrated security best practices.

BREAKDOWN
Product Maturity90/100
Company Stability85/100
Security & Compliance92/100
User Reviews88/100
Transparency85/100
Support Quality87/100
TRUST SIGNALS
$189M total funding from top VCsUsed by leading AI research labs and enterprisesSOC 2 Type II certifiedCloud Security Alliance member151 employees with international presence

What is the history of Labelbox and its key milestones?

2018

Company Founded

Labelbox was formed in San Francisco as a collaborative training data platform for computer vision and machine learning application use cases.

2021

Significant Funding Milestone

Labelbox has secured multiple rounds of funding culminating in its most recent round which raised $110 million and brought the companies total funding to date to $189 million to continue growing its AI data infrastructure.

2023

LLM Solution Launch

Labelbox introduced a large language model (LLM) solution using its partnership with Google Cloud to provide additional resources for generative AI developers.

2023

Cloud Security Alliance

Labelbox joined the Cloud Security Alliance to further develop data-centric security practices for AI applications.

2023

Alignerr Network Expansion

Labelbox launched its Alignerr expert network as well as an engineering hub in Wrocław, Poland to further expand its global capabilities to support data operations.

Who Are the Key Executives Behind Labelbox?

Manu Sharma— CEO
Labelbox leads the company’s mission to build the world’s first and foremost “data factory” for frontier AI and oversees the development of its platform globally.. LinkedIn
Chief Technology Officer— CTO
Labelbox drives the technical architecture for its scalable AI data platform and serves as a key technology leader in the company org chart.

What Are the Key Features of Labelbox?

📊
Advanced Annotation Tools
Labelbox features comprehensive tools for labeling computer vision, NLP and multimodal data at the pixel-perfect level of precision and with automation.
✨
Workflow Automation
Labelbox features customizable workflows with quality control, consensus mechanisms and automated review processes to support the needs of enterprise-scale operations.
✨
Alignerr Expert Network
Labelbox features a global network of subject matter experts for complex data labeling tasks and human preference datasets.
✨
LLM Data Solution
End-to-end solution that allows customers to generate their own training data for large language models (LLMs) and then fine-tune them using Google Cloud services.
✨
Expert Marketplace
Marketplace where companies can connect with a pool of specialized annotators in order to scale as needed.
🔒
Enterprise Security
Supports SOC 2 compliance, role based access controls, and audit logging to allow customers in regulated industries to use the product.
✨
Model-Assisted Labeling
Uses pre-labeling from previous models to speed up annotation workflow and increase efficiency.

What Technology Stack and Infrastructure Does Labelbox Use?

Infrastructure

Multi-cloud with primary Google Cloud partnership and dedicated Wrocław engineering hub

Technologies

PythonReactTypeScriptNode.jsKubernetesPostgreSQL

Integrations

Google CloudAWSAzureMLflowWeights & BiasesJIRASlack

AI/ML Capabilities

Proprietary automation models for model-assisted labeling, consensus workflows, and LLM preference data generation with multimodal support

Inferred from product capabilities, job postings, and engineering blog patterns for AI data platforms

What Are the Best Use Cases for Labelbox?

Computer Vision Teams
Offers toolsets and quality workflows to streamline the annotation of images, video, and 3D sensor data to be used in autonomous systems.
Generative AI Labs
Allows users to create human preference datasets and fine-tune LLMs by utilizing the Alignerr Expert Network and integrated Google Cloud workflows.
Enterprise ML Operations
Provides scalable data pipelines through automation, consensus labeling, and compliance features for production AI deployments.
Medical Imaging Teams
Has high-precision annotation tools that are suitable for regulated healthcare data with enterprise level security controls.
NOT FORSolo ML Researchers
The enterprise pricing model and team focused features will likely make it too expensive for individuals working on hobby projects.
NOT FORReal-time Inference Applications
Is primarily an end-to-end data preparation platform for training data and does not have the capability to support low latency model serving infrastructure.

How Much Does Labelbox Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Free500 LBUs/monthIncludes API rate limit 1,500 req/min, max 30 users per org—
Starter$0.10 per LBUFixed rate, unlimited users, API rate limit 5,000 req/min. Catalog: 1 LBU/60 data rows monthly, Annotate: 1 LBU/labeled row, Model: 1 LBU/5 data rows—
EnterpriseCustom (volume discounts)No LBU limits, API rate limit 20,000 req/min, unlimited users. Minimum spend commitment required—
Labeling ServicesStarts at $10/hourFully managed labeling, model evaluations, data generation. Volume discounts available—
EducationNo Limit LBUsAPI rate limit 5,000 req/min, unlimited users—
Free500 LBUs/month
Includes API rate limit 1,500 req/min, max 30 users per org
Starter$0.10 per LBU
Fixed rate, unlimited users, API rate limit 5,000 req/min. Catalog: 1 LBU/60 data rows monthly, Annotate: 1 LBU/labeled row, Model: 1 LBU/5 data rows
EnterpriseCustom (volume discounts)
No LBU limits, API rate limit 20,000 req/min, unlimited users. Minimum spend commitment required
Labeling ServicesStarts at $10/hour
Fully managed labeling, model evaluations, data generation. Volume discounts available
EducationNo Limit LBUs
API rate limit 5,000 req/min, unlimited users
💡Pricing Example: 10k data rows in Catalog (basic type), 1k labeled in Annotate, 1k with model predictions (Starter tier)
Catalog Monthly$8.33/month
10k rows = ~167 LBUs (1 LBU/60 rows) x $0.10
Annotate One-time$100 one-time
1k labeled rows x 1 LBU x $0.10
Model One-time$20 one-time
1k rows = 200 LBUs (1 LBU/5 rows) x $0.10
💰Savings:Contact sales for volume discounts on higher usage

How Does Labelbox Compare to Competitors?

FeatureLabelboxScale AIVisionRepo
Core FunctionalityData catalog, annotation, model trackingProject-based labelingImage/video annotation & repos
Pricing (starting)Free/ $0.10/LBUProject-based/Enterprise agreementsFree/$49/mo
Free TierYes (500 LBUs/mo)NoYes (500 credits/mo)
Enterprise FeaturesSSO, audit logs, customYes (annual agreements)PowerBI, enterprise support
API AvailabilityYes (rate limited by tier)YesYes (full API)
Integration CountHigh (AI/ML ecosystem)SpecializedStorage, export formats
Support OptionsCommunity to dedicatedEnterpriseStandard to priority
Security CertificationsSOC 2, GDPR (assumed)Enterprise-gradeEnterprise support
Data Types SupportedImages, video, text, complexMultimodalImages, video frames
Managed LabelingYes ($10+/hr)Core offeringAI-assisted only
Core Functionality
LabelboxData catalog, annotation, model tracking
Scale AIProject-based labeling
VisionRepoImage/video annotation & repos
Pricing (starting)
LabelboxFree/ $0.10/LBU
Scale AIProject-based/Enterprise agreements
VisionRepoFree/$49/mo
Free Tier
LabelboxYes (500 LBUs/mo)
Scale AINo
VisionRepoYes (500 credits/mo)
Enterprise Features
LabelboxSSO, audit logs, custom
Scale AIYes (annual agreements)
VisionRepoPowerBI, enterprise support
API Availability
LabelboxYes (rate limited by tier)
Scale AIYes
VisionRepoYes (full API)
Integration Count
LabelboxHigh (AI/ML ecosystem)
Scale AISpecialized
VisionRepoStorage, export formats
Support Options
LabelboxCommunity to dedicated
Scale AIEnterprise
VisionRepoStandard to priority
Security Certifications
LabelboxSOC 2, GDPR (assumed)
Scale AIEnterprise-grade
VisionRepoEnterprise support
Data Types Supported
LabelboxImages, video, text, complex
Scale AIMultimodal
VisionRepoImages, video frames
Managed Labeling
LabelboxYes ($10+/hr)
Scale AICore offering
VisionRepoAI-assisted only

How Does Labelbox Compare to Competitors?

vs Scale AI

Labelbox is a flexible self-service platform with LBU (Label-Based Usage) pricing for AI teams that build their own internal data pipelines, while Scale AI is a managed service for high volume labeling needs with project-based pricing. Labelbox would be best suited for ongoing curation and validation workflows, while Scale is best for outsourced expertise.

Use Labelbox for team-focused iterative projects; use Scale for automated, high-volume data ingestion.

vs VisionRepo

VisionRepo has predictable subscription tier pricing designed for small teams for the annotation of images/videos, which provide fixed credits, while Labelbox uses a usage-based LBU pricing model that can grow with more complex, multi-product workflows that span the entire scope of cataloging, labeling, and model operations.

For small computer vision teams with limited budgets, choose VisionRepo. For managing your full ML data life cycle, choose Labelbox.

vs Encord

Both Labelbox and Encord are enterprise-level data platforms; however, Labelbox emphasizes the integration of curation, annotation, and modeling, while Encord places a heavy emphasis on quality workflows and active learning. Both products also have similar levels of pricing complexity with usage tier pricing models.

For a fully self-sufficient data engine for your ML pipeline, use Labelbox. For quality-oriented refinement of annotations, use Encord.

vs SuperAnnotate

SuperAnnotate provides a similar set of collaboration tools for annotating and is more tightly integrated into models and is more suited for enterprise. While both are based on usage, the unified Labelbox LBU integrates all usage across all Labelbox products.

If you need ontology management and model feedback loops for your production ML pipeline, choose Labelbox.

What are the strengths and limitations of Labelbox?

Pros

  • A single platform for managing your entire data ecosystem including catalog, annotation, and model tracking.
  • The flexible LBU pricing allows you to pay only for what you actually use (across all Labelbox products).
  • The LBU pricing model supports everything from a completely free offering to an enterprise model that can accommodate anything from prototype to production environments.
  • Supports a wide variety of data types such as image, video, text, 3-D with varying LBU rates.
  • Designed with APIs first for programmable workflow capabilities for ML teams.
  • Managed Services available for quality labeling starting at $10/hr.
  • Actively developing new features and functionality for meeting Gen AI data requirements.

Cons

  • LBU pricing complexity — requires a calculator and ongoing monitoring to accurately forecast costs.
  • Does not offer flat-rate pricing — therefore it is difficult to predict how much you will be charged as usage scales.
  • The free tier is extremely limited — includes only 500 LBUs for testing.
  • Tiered API limits — 1500 req/min on the free tier limits automation.
  • Limited number of users allowed on free tier — max 30 users per organization.
  • Requires a long-term commitment — min spend for custom features in enterprise.
  • Consumes varying amounts of LBUs — complex data types consume more than simple table rows.

Who Is Labelbox Best For?

Best For

  • AI/ML engineering teams — Manages your complete data life cycle from curation through model evaluation within a unified platform.
  • Mid-sized AI companies (50-500 employees) — The starter LBU pricing scales predictably with increasing data volumes.
  • Teams needing iterative labeling — Tight integration with models and active learning feedback loops to optimize workflows.
  • Computer vision and multimodal projects — Supports many complex data types (beyond simple images) and does so well.
  • Teams prototyping ML pipelines — Offers a generous free tier of 500 LBUs for early exploration/prototyping.

Not Suitable For

  • Budget-constrained startups — The LBU costs add up quickly once you pass the free tier. Look into fixed plans from VisionRepo
  • Non-technical teams needing simple annotation — The complexity of the platforms are geared toward ML Engineers and less so for Business Users. Try CVAT or LabelStudio
  • One-off labeling projects — Pure managed services such as Scale AI will be better suited to your needs compared to a platform subscription.
  • Teams needing predictable monthly costs — There are no spending limits with usage-based pricing. Fixed tier alternatives such as VisionRepo would provide better value.

Are There Usage Limits or Geographic Restrictions for Labelbox?

Free Tier LBUs
500 LBUs per month
API Rate Limit
1,500 req/min (Free), 5,000 (Starter/Education), 20,000 (Enterprise)
Max Users (Free)
30 users per organization
Starter LBU Rate
$0.10 per LBU, unlimited users
Enterprise LBUs
No limits, custom pricing
Catalog LBU Consumption
1 LBU per 60 basic data rows monthly
Annotate LBU Consumption
1 LBU per labeled data row (one-time)
Model LBU Consumption
1 LBU per 5 data rows (one-time)
Complex Data Types
Higher LBU consumption than basic data rows
Users (Paid)
Unlimited

Is Labelbox Secure and Compliant?

SOC 2 ComplianceEnterprise-grade security with independent audit controls for data platforms.
GDPR ComplianceSupports EU data residency and privacy requirements for AI training data.
Data EncryptionEncryption at rest and in transit for customer data rows and labels.
Access ControlOrganization-level user management, role-based permissions, SSO for Enterprise.
Audit LoggingComplete audit trails for labeling workflows and data access (Enterprise).
Infrastructure SecurityCloud-hosted with redundancy, API security, rate limiting by subscription.
Data Residency OptionsMulti-region deployment available for compliance needs (Enterprise).

What Customer Support Options Does Labelbox Offer?

Channels
Available for all customersIn-app support during business hours24/7 self-service at docs.labelbox.comUser forums and help center
Hours
Business hours (PST) for live support, 24/7 documentation access
Response Time
Typically within 24 hours for email support
Satisfaction
4.5/5 based on G2 and Capterra reviews
Specialized
Dedicated support for Enterprise customers
Business Tier
Priority response queues and customer success managers for Enterprise
Support Limitations
•No phone support mentioned
•Advanced features may require sales contact for assistance
•Support quality varies based on plan tier

What APIs and Integrations Does Labelbox Support?

API Type
REST API with comprehensive documentation
Authentication
API Keys, OAuth integration support
Webhooks
Supported for workflow events and data updates
SDKs
Official Python SDK, JavaScript support
Documentation
Excellent - full API reference at docs.labelbox.com
Sandbox
Available via development workspace with rate limits
SLA
Enterprise SLA 99.9% uptime, standard plans 99.5%
Rate Limits
Tiered: 1,000 calls/hour (Starter), 10,000+/hour (Enterprise)
Use Cases
Programmatic data import/export, workflow automation, model integration, custom labeling pipelines

What Are Common Questions About Labelbox?

Labelbox can handle multimodal data including images, video, text, audio, and 3D sensor data. The platform contains special annotation tools for each data type with AI assistance to help speed up labeling.

Labelbox has flexible pricing plans which allow for "pay-as-you-go" options based on data volume and complexity of annotation. Enterprise plans also have custom pricing with dedicated support. For a quote contact sales.

Labelbox contains enterprise-grade features such as automation, quality control, team collaboration, and integration that do not exist in open-source tools. Labelbox was built specifically for production scale ML teams and not the individual researcher.

Yes, Labelbox is SOC 2 compliant with enterprise grade security. Data is encrypted at rest and in transit with role-based access controls and audit logs to meet compliance requirements.

Labelbox is easily integrated with AWS, GCP, Azure, and popular ML frameworks. Labeled data can be exported in industry standard formats (COCO, YOLO) or custom schema for direct model training.

Labelbox offers Alignerr managed labeling services with expert labelers for RLHF, SFT, and multimodal tasks. Use the collaborative platform to work together with your internal team.

Yes, Labelbox offers a free tier and trial workspace to test the core features. Upgrade to paid plans for unlimited data and advanced automation capabilities.

Model-assisted labeling uses your trained models to assist in pre-labeling data with human-in-the-loop review for quality. Bulk classification and active learning find edge cases automatically.

Is Labelbox Worth It?

Labelbox provides the enterprise standards for Data Labeling and Management. Labelbox provides extensive tools for multimodal AI training data at scale. Although there is a learning curve for the more advanced features in Labelbox, the automation, quality control, and integration make it very valuable for production ML teams. Labelbox’s position in the market is primarily due to human-in-the-loop workflow, and this makes it superior to the many fragmented open-source alternatives.

Recommended For

  • Production ML teams developing multimodal AI Computer Vision & NLP Models
  • Companies need scalable Data Labeling with Quality Assurance
  • Enterprises require Compliance & Audit Capabilities
  • Teams working with complex multimodal annotation projects

!
Use With Caution

  • Small teams/researchers -- Learning Curve & Cost may be High
  • Simple Classification Tasks – Lighter Tools May Satisfy
  • Budget-Constrained Startups – Evaluate ROI vs. Managed Services

Not Recommended For

  • One-Off Hobby Projects – Free/Open-Source Tools Better
  • Real-Time Inference Applications – Data Labeling Focus Only
  • Teams Without ML Engineering Resources – Requires Setup Expertise
Expert's Conclusion

Labelbox is an Essential Tool for Serious ML Teams Building Production Models That Require High-Quality Scalable Training Data Preparation.

Best For
Production ML teams developing multimodal AI Computer Vision & NLP ModelsCompanies need scalable Data Labeling with Quality AssuranceEnterprises require Compliance & Audit Capabilities

What do expert reviews and research say about Labelbox?

Key Findings

Labelbox is firmly established as one of the top enterprise data labeling platforms that provide a comprehensive multimodal solution with AI-Assisted Automation and Production Grade Quality Controls. Labelbox’s strong customer validation (Dialpad) demonstrate significant Return-On-Investment (ROI). Labelbox provides solutions for the entire data-centric AI Lifecycle from Curation to Model Evaluation.

Data Quality

Good - detailed feature information from official docs and solution pages. Limited pricing transparency and recent customer reviews. Enterprise details require sales contact.

Risk Factors

!
Opacity of pricing -- Requires Sales Consultation
!
Learning Curve for Advanced Ontology/Workflow Configuration
!
Competitor Space with Emerging AI-Native Alternatives
!
Dependence on Skilled Labelers for Complex Tasks
Last updated: February 2026

What Additional Information Is Available for Labelbox?

Customer Success

Dialpad accelerated LLM development by 75% using Labelbox for data curation, annotation, and human evaluation across 5 Billion Minutes of Conversation Data. Supports AI Customer Intelligence Platform.

Alignerr Labeling Service

Expert on-demand labeling for advanced tasks such as SFT, multi-modal LLM evaluation, red teaming and code review. In combination with a self-service labeling platform for hybrid workflows.

Industry Applications

For quality control, product classification, and feature extraction in manufacturing. And supports the sound event detection and audio classification needed by Voice AI.

Data-Centric Approach

Relies on batch-by-batch iteration for labeling, rather than large-scale datasets. Also promotes an iterative two-way process for labeling that allows both labelers and ML teams to work together to prepare data.

Recent Innovation

The bulk-classification feature enables users to use one click to label many different sub-sets of data based on their similarity using natural language searches, embeddings, and zero- /few-shot learning workflow capabilities.

What Are the Best Alternatives to Labelbox?

  • •
    Scale AI: An enterprise-level data platform with managed labeling services and extensive Gen-AI capabilities; while more expensive than Labelbox’s self-service model, it has higher volume managed services. It is best suited for teams that are using managed services for complex RLHF/red teaming. (scale.com)
  • •
    Snorkel AI: A programmatic labeling capability through the use of weak-supervision and foundation models, which is less dependent upon human-in-loop interactions than Labelbox, and better suited for teams that utilize data-programming methodologies to build synthetic labels. (snorkel.ai)
  • •
    Encord: A computer vision-focused platform that includes strong active-learning capabilities. This is also a much more affordable option compared to Labelbox with very similar computer vision annotation capabilities. It is best used by teams that are focused on computer vision only, and want a cost-effective method for quality controls. (encord.com)
  • •
    CVAT (Computer Vision Annotation Tool): An open-source annotation tool that provides wide-ranging computer vision capabilities. While this can be a free self-hosted solution, there are no comparable enterprise-level solutions to provide automation and collaboration capabilities. It is best used by research teams that have sufficient engineering resources. (cvat.ai)
  • •
    Prodigy: A lightweight active-learning annotation tool for NLP/CV. Compared to Labelbox, this is much less expensive and much less complicated, however, it does lack the ability to collaborate among teams and at an enterprise level. It is best used by individual researchers or small teams. (explosion.ai/prodigy)

What Are Labelbox's Classification Accuracy?

Achieved via model-assisted labeling
High Accuracy
Improved with human-in-the-loop %
Precision
Real-time metrics
Quality Control
75% faster LLM creation
Benchmark

What Supported Data Types Does Labelbox Offer?

Images

Segmentation, Object Detection, Classification

Video

Advanced Annotation Support

Text

Text Classification and NLP Tasks

Audio

Transcription, Sound Classification, Event Tagging

3D Sensor Data

Point clouds and sensor fusion

Multimodal

Text-to-image, video, audio tasks

What Nlp Capabilities Does Labelbox Offer?

Text Classification

Advanced tooling with AI assistance

Sentiment Analysis

Via fine-tuning for business conversations

Entity Extraction

Insight extraction from calls

Supervised Fine-tuning (SFT)

High-quality training data generation

Reinforcement Learning (RLHF)

Human feedback integration

Multimodal LLM Evaluation

Text-to-image and audio tasks

What Is Labelbox's Training Options?

Minimum Training Samples
Iterative small batches recommended
Training Time
Accelerated via model-assisted labeling
Custom Taxonomies
Yes, custom workflows and ontologies
Active Learning
Yes, AI-assisted suggestions and bulk classification
Model Versioning
Yes, comprehensive dataset versioning
Auto Retraining
Zero-shot and few-shot learning capabilities
Human In The Loop
Built-in quality control and review

What Integration Connectors Does Labelbox Support?

AWSGoogle CloudAzureAlignerr CommunityREST APICloud Storage

What Are Labelbox's Processing Specs?

Large datasets in few clicks
Bulk Classification
75% faster LLM creation
Automation Speed
Real-time parallel workflows
Team Collaboration
Scalable enterprise-grade
Data Volume
Significantly accelerated via AI assistance
Labeling Speed

What Compliance Certifications Does Labelbox Have?

Enterprise SecurityCloud provider compliant
Data PrivacyQuality control best practices
GDPR ComplianceEnterprise platform standards
SOC 2AI enterprise standard
HIPAABAA potentially available

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