Labelbox

A data-centric AI platform that unifies data curation, multi-modal labeling, and model evaluation—best for enterprise teams managing production-grade computer vision or LLM workflows.

Excellent for teams needing high-quality ground truth and integrated data management, weaker for solo developers or teams with minimal budgets.

Datasets Machine Learning Ai Development Computer Vision Nlp Llm Fine-Tuning Data Analysis

Analysis based on product data, pricing structure, traffic signals, and public user sentiment.

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Who Should Use Labelbox?

Typical users

ML engineers and data scientists at mid-to-large enterprises who manage complex datasets across images, video, and text.

Maturity fit

scaling to advanced

Choose this if…

Your priority is data quality and lineage over the lowest possible labeling cost.
You want to manage data curation and labeling in a single interface rather than jumping between tools.
You need to perform RLHF or fine-tune LLMs with human-in-the-loop feedback.
Your workflow requires a powerful Python SDK to automate data pipelines.

Skip this if…

You need a simple, free open-source tool for a small, one-off image classification task.
Your budget is strictly limited and you cannot commit to usage-based pricing models.
You require a fully managed labeling service where you don't want to touch the software at all.

About Labelbox

Labelbox is a centralized platform for the entire AI data lifecycle. It exists to solve the 'garbage in, garbage out' problem by providing tools to curate data, orchestrate human labeling, and diagnose model performance. It targets teams moving from experimental notebooks to production-grade AI applications.

Official profiles

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What it actually does

The platform provides a suite for annotating multi-modal data, managing datasets through a searchable catalog, and evaluating model predictions against ground truth. It allows users to set up labeling workflows, manage workforce performance, and automate labeling using foundation models.

What makes it different

Unlike point solutions that only handle labeling, Labelbox integrates data management (Catalog) and model diagnostics (Model) into one environment. This allows users to find high-value data points for labeling based on model uncertainty or data distribution gaps rather than labeling randomly.

Multi-modal annotation (Image, Video, Text, Audio, PDF, Geospatial) Data curation and similarity search Model-assisted labeling (MAL) to pre-populate tags RLHF and LLM evaluation workflows Workforce management and quality control (Consensus, Benchmarks) Python SDK and REST API for pipeline integration Model error analysis and performance visualization

Ratings across the web

Capterra 0 reviews

0.0/5

Ratings aggregated from independent review platforms.

Key Features

Catalog

Centralizes unstructured data with metadata filtering and similarity search to find the right data to label.

Annotate

Configurable labeling interfaces for complex tasks like video object tracking or document NER.

Model

Compares model predictions against ground truth to identify where the model is failing.

Foundry

Provides access to foundation models to automate the generation of labels or synthetic data.

Quality Workflows

Built-in tools for consensus scoring and gold-standard benchmarks to ensure labeler accuracy.

SDK-First Architecture

Allows developers to programmatically manage projects, upload data, and export labels.

Cloud Connectors

Direct integration with AWS S3, GCP, and Azure Blob Storage to keep data in your own bucket.

Pricing

Free

Up to 10,000 Labelbox Units (LBUs)
Standard labeling tools
Community support
Basic Catalog functionality

Popular

Pro

Usage-based monthly

Pay-as-you-go LBUs
Advanced quality workflows
Priority support
Full SDK/API access

Enterprise

Custom annual

Volume discounts
Custom security and SSO
Dedicated account manager
SLA guarantees

Pricing checked 5 months ago

Pricing guidance

Best plan for most users: The Pro plan is the most logical choice for scaling startups as it provides full access to the SDK and quality tools without the upfront commitment of an Enterprise contract.

Free plan enough? Yes, if you are a student or researcher working on a small dataset (under 1,000 images) and don't need advanced workforce management.

Upgrade when:

When you exceed the 10,000 LBU limit on the Free tier.
When you need to implement consensus or benchmark testing for label quality.
When you need to integrate with private cloud storage buckets.

Watch out for:

LBU consumption varies by data type (video consumes units faster than images).
Some advanced foundation models in Foundry may incur additional costs.
Support response times are tiered by plan level.

Premium positioning justified by feature depth and enterprise-ready security.

Pros & Cons

Strengths

Unified data-centric workflow
By keeping curation, labeling, and evaluation in one tool, teams reduce the friction of moving data between disparate systems, which speeds up iteration cycles.
Highly flexible annotation UI
The interface supports complex nested classifications and multi-modal tasks that simpler tools like CVAT or Label Studio often struggle to handle without heavy customization.
Strong automation capabilities
Model-assisted labeling and the use of foundation models to pre-label data significantly reduce the manual effort and cost of human annotation.
Enterprise-grade security and compliance
Offers SOC2 Type II compliance, SSO, and the ability to keep data in private buckets, which is a requirement for regulated industries.

Weaknesses

Complex pricing structure
The use of 'Labelbox Units' (LBUs) can make it difficult to predict monthly costs as your data volume and labeling complexity grow.

Affects: Finance teams and project managers on tight budgets
Steep learning curve for the SDK
While powerful, the Python SDK is complex and requires significant engineering time to master for advanced automation workflows.

Affects: Small teams without dedicated ML Ops engineers
Performance lags on massive files
Users report occasional UI slowdowns when working with very high-resolution video or extremely large text datasets in the browser.

Affects: Annotators working on high-density data tasks

Real User Sentiment

Generally positive, with users praising the professional UI and robust feature set, though frequently citing high costs as a primary pain point.

Users tend to like

Intuitive and clean annotation interface
Powerful Python SDK for automation
Seamless integration between data curation and labeling
Excellent support for video and multi-frame tasks

Users commonly complain about

Expensive pricing for high-volume projects
Occasional bugs in the web UI during large exports
Documentation for the SDK can be lagging behind new features
Complexity of the LBU credit system

Recurring tradeoffs

You trade lower cost (found in open-source tools) for significantly higher developer productivity and better data governance.

Happiest users

ML teams at established companies who value engineering efficiency and data quality over raw tool cost.

Often frustrated

Individual researchers or bootstrapped startups who find the usage-based pricing prohibitive for large-scale experimentation.

Use Cases

Autonomous Vehicles

Labeling thousands of hours of video with temporal consistency for object tracking.

Medical AI

Annotating DICOM imagery for tumor detection with high-precision mask tools.

LLM Fine-tuning

Using human-in-the-loop to rank model responses for RLHF training.

E-commerce

Curating and tagging millions of product images to improve search and recommendation engines.

Geospatial Analysis

Identifying land use patterns in high-resolution satellite imagery using tiling tools.

Frequently Asked Questions

Does Labelbox have a free plan?

Yes, Labelbox offers a Free tier that includes 10,000 Labelbox Units (LBUs). This is sufficient for small-scale projects, academic research, or initial prototyping before moving to a paid usage-based model.

How does Labelbox compare to Scale AI?

Labelbox is primarily a software platform (SaaS) that provides the tools for your team or a third-party workforce to label data. Scale AI is more of a full-service provider that combines software with their own managed workforce to deliver labeled data as a service.

What are Labelbox Units (LBUs)?

LBUs are the currency Labelbox uses for pricing. Different actions, such as labeling an image, processing a minute of video, or using a foundation model to generate a label, consume different amounts of units.

Can I use my own labeling workforce?

Yes, Labelbox is designed to be workforce-agnostic. You can use your internal team, bring your own BPO (Business Process Outsourcing) partner, or access on-demand labeling services through their partner network.

Does Labelbox support on-premise deployment?

Labelbox is a cloud-native SaaS platform. While they offer features like 'Cloud Connectors' to keep your data in your own private cloud storage (AWS, GCP, Azure), the application itself is hosted by Labelbox.

What data types are supported?

Labelbox supports a wide range of data including images (JPEG, PNG, TIFF), video (MP4, MOV), text (HTML, TXT), audio, PDF documents, and specialized formats like DICOM for medical imaging and geospatial data.

Why trust this page?

This evaluation combines product positioning, pricing analysis, traffic and market signals, and public user sentiment into a single decision-support page. Content is generated editorially — not copied from the vendor's website.

Funding & Company

Founded

2018

Stage

—

Total Raised

$188.9M

Latest Round

Series D (Jan 2022)

Notable Investors

SoftBank Vision Fund 2 Andreessen Horowitz B Capital Group Kleiner Perkins Gradient Ventures First Round Capital Databricks Ventures

Labelbox has raised a total of $188.9 million over five rounds, culminating in a $110 million Series D in January 2022 led by SoftBank Vision Fund 2. This significant, late-stage funding from top-tier investors provides the company with substantial runway and resources. The consistent backing from prominent VCs like Andreessen Horowitz and Kleiner Perkins signals strong confidence in its position within the AI data infrastructure market.

Full funding report high confidence

Market Signals & Traffic

Estimated visits, global rank, geography, traffic sources, monthly visit trends, and organic search keywords (Similarweb)—on a dedicated page built for depth and search.

Estimated visits: 918,251
Global rank: #41,891
Snapshot: Apr 2026
Traffic trend: Falling