# Best Data Labeling Software

## How Many Data Labeling Software Products Does G2 Track?

**Total Products under this Category:** 116

### Category Stats (Aug 2026)

- **Average Rating:** 4.49/5 (↓0.02 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** Datature (+0.08%) - Among all products in this category, Datature recorded the largest rating increase compared to last month

_Last updated: August 01, 2026_

## How Does G2 Rank Data Labeling Software Products?

**Why You Can Trust G2's Software Rankings:**

- 30 Analysts and Data Experts
- 1,800+ Authentic Reviews
- 116+ Products
- Unbiased Rankings

G2's software rankings are built on verified user reviews, rigorous moderation, and a consistent research methodology maintained by a team of analysts and data experts. Each product is measured using the same transparent criteria, with no paid placement or vendor influence. While reviews reflect real user experiences, which can be subjective, they offer valuable insight into how software performs in the hands of professionals. Together, these inputs power the G2 Score, a standardized way to compare tools within every category.

## G2 Grid® for Data Labeling Software
 ![G2 Grid® for Data Labeling Software plotting products by satisfaction and market presence](https://www.g2.com/categories/data-labeling/grids.png?focus%5B%5D=125020&focus%5B%5D=128515&focus%5B%5D=168222&focus%5B%5D=78925&focus%5B%5D=87452&focus%5B%5D=142739&focus%5B%5D=126287&focus%5B%5D=1563734)

Highlighted products: Roboflow, SuperAnnotate, Encord, Labelbox, Amazon Sagemaker Ground Truth, Sama, V7 Darwin, and Outlier AI.

Underlying data: [Grid® JSON](https://www.g2.com/categories/data-labeling/grids.json?focus%5B%5D=roboflow&focus%5B%5D=superannotate&focus%5B%5D=encord&focus%5B%5D=labelbox&focus%5B%5D=amazon-sagemaker-ground-truth&focus%5B%5D=sama&focus%5B%5D=v7-darwin&focus%5B%5D=outlier-ai-outlier-ai)

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### [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)

SuperAnnotate bridges the gap between cutting-edge AI innovation and the high-quality human data that powers it - helping advanced AI teams build more intelligent models. With a global network of thousands of rigorously vetted experts, ethical and scalable managed operations, precise talent matching, and purpose‑built technology, SuperAnnotate delivers full project visibility and unmatched data quality. SuperAnnotate powers complex annotation, evaluation, and reinforcement learning workflows to build, evaluate and align frontier AI. Trusted by innovators like Databricks, IBM and ServiceNow - and backed by NVIDIA, Dell Technologies Capital, Databricks Ventures, Cox Enterprises, and Lionel Messi’s Play Time VC - SuperAnnotate enables the world’s top AI teams to build responsible and state‑of‑the‑art models with human data.

**Average Rating:** 4.8/5.0

**Total Reviews:** 353

#### How Do G2 Users Rate SuperAnnotate?

- **Labeler Quality:** 9.6/10 (Category avg: 8.9/10)
- **Object Detection:** 9.4/10 (Category avg: 8.9/10)
- **Data Types:** 9.5/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind SuperAnnotate?

- **Seller:** [SuperAnnotate](https://www.g2.com/sellers/superannotate)
- **Company Website:** superannotate.com
- **Year Founded:** 2018
- **HQ Location:** San Francisco, CA
- **Twitter:** @superannotate  
720 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=ed4d3a394b0ca2eaac6c63041f9bd1bf14ee26538d356cd977a2b0f50c15f4d1&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F18999422%2F&secure%5Burl_type%5D=linkedin_company_website)  
361 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Student, Data Trainer
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 57% Small, 23% Medium

#### What Do G2 Reviewers Say About SuperAnnotate?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **intuitive interface** of SuperAnnotate, making large-scale annotation projects easy to manage and efficient.
- Users appreciate the **user-friendly interface** of SuperAnnotate, which enhances the efficiency and accuracy of their annotation tasks.
- Users value the **annotation efficiency** of SuperAnnotate, enabling quick, consistent, and high-quality annotations across diverse projects.
- Users commend the **efficiency** of SuperAnnotate, appreciating the time saved and the streamlined annotation process.
- Users value SuperAnnotate for its **high-quality annotations** , ensuring consistent and efficient collaboration and management of projects.

##### Cons

- Users notice **performance issues** with SuperAnnotate, particularly related to loading times and occasional technical glitches.
- Users experience **slow performance** with SuperAnnotate, particularly during image cropping and handling large projects.
- Users find the **difficult learning** curve for advanced features challenging, impacting their overall experience with SuperAnnotate.
- Users find the **complexity of the platform** daunting, particularly for new users navigating advanced features.
- Users find a **lack of guidance** in SuperAnnotate, making it challenging for newcomers to navigate advanced features effectively.

#### What Are Recent G2 Reviews of SuperAnnotate?

**["Easy-to-Use Data Organization and Powerful Image-Splitting Tools"](https://www.g2.com/survey_responses/superannotate-review-13146852)**

**Rating:** 5.0/5.0 stars

_— Doniaa K._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-13146852)

**["Streamlines Annotation with an Easy Setup and Strong Support"](https://www.g2.com/survey_responses/superannotate-review-12584940)**

**Rating:** 4.0/5.0 stars

_— Nada A._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-12584940)

#### What Are G2 Users Discussing About SuperAnnotate?

- [What is your experience with SuperAnnotate for data annotation, and what would you like to see improved?](https://www.g2.com/discussions/what-is-your-experience-with-superannotate-for-data-annotation-and-what-would-you-like-to-see-improved) - 1 comment
- [How do I annotate an image in OpenCV?](https://www.g2.com/discussions/how-do-i-annotate-an-image-in-opencv)
- [Is SuperAnnotate free?](https://www.g2.com/discussions/is-superannotate-free)
- [How do you use SuperAnnotate?](https://www.g2.com/discussions/how-do-you-use-superannotate)
- [What is SuperAnnotate?](https://www.g2.com/discussions/what-is-superannotate) - 1 comment, 2 upvotes

### [Roboflow](https://www.g2.com/products/roboflow/reviews)

Roboflow has everything you need to build and deploy computer vision applications. Over 1,000,000 users from businesses of every size — from startups to public companies — use the company's end-to-end platform for image and video collection, organization, annotation, preprocessing, model training, and deployment. Roboflow provides tools for each step in the computer vision deployment lifecycle and integrates with your existing solutions so you can tailor your pipeline to meet your needs.

**Average Rating:** 4.7/5.0

**Total Reviews:** 155

#### How Do G2 Users Rate Roboflow?

- **Labeler Quality:** 9.0/10 (Category avg: 8.9/10)
- **Object Detection:** 9.1/10 (Category avg: 8.9/10)
- **Data Types:** 8.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Roboflow?

- **Seller:** [Roboflow](https://www.g2.com/sellers/roboflow)
- **Company Website:** roboflow.com
- **Year Founded:** 2019
- **HQ Location:** Remote, US
- **Twitter:** @roboflow  
13,577 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=660f87d85fdd82e0f1cecfe2354a16103bb5a6f5508134496575ec655201678c&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F36096640&secure%5Burl_type%5D=linkedin_company_website)  
137 employees on LinkedIn®

#### Who Uses This Product?

- **Who Uses This:** Founder, Researcher
- **Top Industries:** Computer Software, Research
- **Company Size:** 78% Small, 14% Medium

#### What Do G2 Reviewers Say About Roboflow?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **intuitive interface** of Roboflow, enabling efficient annotation and seamless collaboration for computer vision projects.
- Users value the **efficiency** of Roboflow, praising its streamlined dataset management that saves time and reduces errors.
- Users appreciate the **annotation efficiency** of Roboflow, enjoying streamlined dataset management that saves time and reduces errors.
- Users appreciate the **easy data labelling process** in Roboflow, which streamlines annotation and enhances team collaboration.
- Users appreciate the **powerful and versatile features** of Roboflow, enhancing academic projects and computer vision tasks.

##### Cons

- Users find Roboflow **expensive** , especially for students, as key features require paid plans for privacy and customization.
- Users note a **lack of features** for advanced analytics and customization on lower-tier plans in Roboflow.
- Users find **limited functionality** in Roboflow, facing challenges like feature restrictions and lack of flexibility in advanced tasks.
- Users experience **annotation issues** with Roboflow, often needing extensive manual adjustments for accuracy and efficiency.
- Users find **inefficient labeling** management cumbersome, needing manual organization and lacking shortcuts for smoother annotation completion.

#### What Are Recent G2 Reviews of Roboflow?

**["Speeds up our agri‑CV research"](https://www.g2.com/survey_responses/roboflow-review-12692685)**

**Rating:** 5.0/5.0 stars

_— Alexey K._

[Read full review](https://www.g2.com/survey_responses/roboflow-review-12692685)

**["Roboflow Makes Computer Vision Projects Easy to Build, Train, and Deploy"](https://www.g2.com/survey_responses/roboflow-review-12984362)**

**Rating:** 5.0/5.0 stars

_— noah r._

[Read full review](https://www.g2.com/survey_responses/roboflow-review-12984362)

### [Encord](https://www.g2.com/products/encord/reviews)

Encord is the universal data layer for AI. The platform helps AI teams train and run their models with the right data - managing, curating, annotating, and aligning data across the full AI lifecycle. Encord works with over 300 leading AI teams, including Woven by Toyota, Zipline, AXA, and Flock Safety. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant.

**Average Rating:** 4.8/5.0

**Total Reviews:** 65

#### How Do G2 Users Rate Encord?

- **Labeler Quality:** 9.4/10 (Category avg: 8.9/10)
- **Object Detection:** 9.3/10 (Category avg: 8.9/10)
- **Data Types:** 9.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Encord?

- **Seller:** [Encord](https://www.g2.com/sellers/encord)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @encord\_team  
1,014 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b5b79c871099fa074d165dec60c3345148d0ae005f29a546864b838e8a7500a8&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F69557125&secure%5Burl_type%5D=linkedin_company_website)  
195 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Hospital & Health Care
- **Company Size:** 51% Small, 40% Medium

#### What Do G2 Reviewers Say About Encord?

_AI-generated summary from verified user reviews_

##### Pros

- Users commend the **responsive customer support** from Encord, ensuring quick assistance during their project needs.
- Users praise Encord for its **annotation efficiency** , streamlining workflows and enhancing speed in data curation tasks.
- Users value the **intuitive interface and seamless integration** of Encord's annotation tools for efficient data workflow.
- Users appreciate the **efficiency** of Encord, highlighting smooth workflows and quick integration that enhances their productivity.
- Users value Encord's **intuitive interface and seamless integration** , enhancing their workflows with efficient data curation and support.

##### Cons

- Users find **complex automation** challenging, though the supportive team helps navigate the intricacies of custom workflows.
- Users find it challenging to stay current due to **frequent updates** , but support from the team is helpful.
- Users find the **lack of guidance** challenging due to rapid feature updates, despite helpful customer support.

#### What Are Recent G2 Reviews of Encord?

**["Strong video labeling platform with excellent support"](https://www.g2.com/survey_responses/encord-review-12281672)**

**Rating:** 5.0/5.0 stars

_— Angela S._

[Read full review](https://www.g2.com/survey_responses/encord-review-12281672)

**["Built for fast model development cycles"](https://www.g2.com/survey_responses/encord-review-12219596)**

**Rating:** 5.0/5.0 stars

_— Brian E._

[Read full review](https://www.g2.com/survey_responses/encord-review-12219596)

### [Labelbox](https://www.g2.com/products/labelbox/reviews)

Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject these systems with the right degree of human supervision and automation. Whether they are building AI products with custom or foundation models, or using AI to automate data tasks or find business insights, Labelbox enables teams to do so effectively and quickly. The platform is used by Fortune 500 enterprises such as Walmart, P&G, Genentech, and Adobe, and hundreds of leading AI teams. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google's AI-focused fund), and Databricks Ventures.

**Average Rating:** 4.5/5.0

**Total Reviews:** 48

#### How Do G2 Users Rate Labelbox?

- **Labeler Quality:** 9.1/10 (Category avg: 8.9/10)
- **Object Detection:** 8.6/10 (Category avg: 8.9/10)
- **Data Types:** 8.8/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.0/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Labelbox?

- **Seller:** [Labelbox](https://www.g2.com/sellers/labelbox)
- **Year Founded:** 2018
- **HQ Location:** San Francisco, California
- **Twitter:** @labelbox  
3,489 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=46b35e20f0b8a1bbee233e1f6d5a6d1a72e4e49ac4f1901248ae276388e42159&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Flabelbox%2F&secure%5Burl_type%5D=linkedin_company_website)  
469 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 46% Small, 38% Medium

#### What Do G2 Reviewers Say About Labelbox?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **ease of use** of Labelbox, highlighting its simple setup and accessible features for project management.
- Users commend the **easy and fast data labeling** process of Labelbox, enhancing workflow and improving data quality effectively.
- Users enjoy the **efficient project management** capabilities of Labelbox, streamlining tasks and enhancing overall organization.
- Users value the **AI capabilities** of Labelbox for simplifying data labeling and improving model accuracy effectively.
- Users find **easy integrations** with Labelbox, enhancing their experience with a user-friendly interface and quick setup.

##### Cons

- Users express frustration with the **lack of features** , feeling limited by the inability to customize and increase task availability.
- Users experience **slow performance** with Labelbox, especially when working with large datasets and output visualizations.
- Users find Labelbox to have **difficult learning** due to its complexity and slow processing with large datasets.
- Users find Labelbox **expensive** and express concerns about the high cost, especially for small-scale users.
- Users express frustration with the **slow processing** speed of Labelbox, delaying project initiation and overall experience.

#### What Are Recent G2 Reviews of Labelbox?

**["LLM Training at it’s finest!"](https://www.g2.com/survey_responses/labelbox-review-11265400)**

**Rating:** 4.5/5.0 stars

_— Staci T._

[Read full review](https://www.g2.com/survey_responses/labelbox-review-11265400)

**["Professional Interface, Simple Setup, Needs Data Update"](https://www.g2.com/survey_responses/labelbox-review-12625977)**

**Rating:** 4.0/5.0 stars

_— Ashish S._

[Read full review](https://www.g2.com/survey_responses/labelbox-review-12625977)

#### What Are G2 Users Discussing About Labelbox?

- [How do I create a labeled dataset?](https://www.g2.com/discussions/how-do-i-create-a-labeled-dataset)
- [What is label tool?](https://www.g2.com/discussions/what-is-label-tool)
- [How do I download Labelbox?](https://www.g2.com/discussions/how-do-i-download-labelbox) - 2 comments
- [Is Labelbox open source?](https://www.g2.com/discussions/is-labelbox-open-source)

## FAQs About Data Labeling Software

Generated using AI

Last updated: June 3, 2026

### Which Data Labeling platforms support computer vision models using bounding boxes and point cloud annotation

Based on G2 reviews, these products are the clearest fits for image and point cloud annotation needs.

- [Taskmonk](https://www.g2.com/products/taskmonk) — LiDAR projects with quality checks.
- [SuperAnnotate](https://www.g2.com/products/superannotate) — bounding boxes, segmentation, review workflows.
- [Segments.ai](https://www.g2.com/products/segments-ai) — multi-sensor and point cloud labeling.
- [Roboflow](https://www.g2.com/products/roboflow) — computer vision datasets and annotations.

### Data Labeling platforms that maintain annotation quality while handling high-volume image processing without slowdowns

According to verified users, teams evaluating data labeling software often look for a balance between speed, consistency, and review controls. Recent G2 reviews highlight strengths such as built-in quality checks, version control, structured review steps, and collaboration features that help maintain labeling quality at scale. Reviewers also repeatedly call out limits to watch for, including lag with very large datasets, slower uploads or exports, and occasional responsiveness issues on heavy image workloads. In this category, buyers tend to compare how well platforms support organized dataset management, annotation review, and reliable throughput when image volume rises, rather than looking for raw processing speed alone.

### Which Data Labeling tools avoid performance issues and instability when processing large image batches

Based on G2 reviews, these products are commonly mentioned for managing larger image workloads with structured workflows.

- [SuperAnnotate](https://www.g2.com/products/superannotate) — large datasets with quality controls.
- [Roboflow](https://www.g2.com/products/roboflow) — image annotation and dataset versioning.
- [Taskmonk](https://www.g2.com/products/taskmonk) — scalable labeling with built-in QC.
- [Encord](https://www.g2.com/products/encord) — video and data pipeline workflows.

### What are the most important features in data labeling software

According to verified users, the most important features in data labeling software are annotation tools that match the data type, clear review and quality control workflows, dataset organization, and collaboration support. Recent G2 reviews also point to versioning, preprocessing or augmentation support, export flexibility, and AI-assisted labeling as recurring decision factors. For computer vision use cases, buyers often mention support for bounding boxes, polygons, segmentation, and video or point cloud workflows. Reviewers also care about how easily teams can move from labeling into model training or downstream pipelines. In practice, the best-fit platforms reduce manual effort while still helping teams maintain consistency, traceability, and clean handoffs.

### How do teams use Data Labeling for quality control

According to verified users, teams use data labeling for quality control by building review steps directly into annotation workflows. Recent G2 reviews describe practices such as peer review, verify-before-submit checkpoints, role-based task assignment, change tracking, and version control to catch mistakes early and improve consistency across annotators. Buyers also look for tools that centralize labeling, feedback, and project management so fewer issues slip through handoffs. In image-heavy workflows, users value features that help compare annotations, review edge cases, and maintain standards across large batches. The common theme is that quality control works best when it is embedded in the labeling process rather than handled as a separate cleanup step later.

### [Amazon Sagemaker Ground Truth](https://www.g2.com/products/amazon-sagemaker-ground-truth/reviews)

Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks.

**Average Rating:** 4.1/5.0

**Total Reviews:** 19

#### How Do G2 Users Rate Amazon Sagemaker Ground Truth?

- **Labeler Quality:** 10.0/10 (Category avg: 8.9/10)
- **Object Detection:** 10.0/10 (Category avg: 8.9/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Amazon Sagemaker Ground Truth?

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud  
2,232,483 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=072881eee28a2afe24f8d1bda9f20e3e146b9fb4b214f216411ce2ed6898b31e&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Famazon-web-services%2F&secure%5Burl_type%5D=linkedin_company_website)  
147,094 employees on LinkedIn®
- **Ownership:** NASDAQ: AMZN

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 37% Large, 37% Small

#### What Are Recent G2 Reviews of Amazon Sagemaker Ground Truth?

**["The best fully managed data labeling service ever"](https://www.g2.com/survey_responses/amazon-sagemaker-ground-truth-review-4971883)**

**Rating:** 5.0/5.0 stars

_— Vithushan S._

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-ground-truth-review-4971883)

**["Great for Agile lovers."](https://www.g2.com/survey_responses/amazon-sagemaker-ground-truth-review-5146806)**

**Rating:** 4.0/5.0 stars

_— Verified User in Computer Software_

[Read full review](https://www.g2.com/survey_responses/amazon-sagemaker-ground-truth-review-5146806)

#### What Are G2 Users Discussing About Amazon Sagemaker Ground Truth?

- [Which of these types of workforce are available in Amazon SageMaker ground truth?](https://www.g2.com/discussions/which-of-these-types-of-workforce-are-available-in-amazon-sagemaker-ground-truth)
- [How much does ground truth cost?](https://www.g2.com/discussions/how-much-does-ground-truth-cost)
- [Which type of data are included in Amazon SageMaker ground truth manifest file?](https://www.g2.com/discussions/which-type-of-data-are-included-in-amazon-sagemaker-ground-truth-manifest-file)

### [V7 Darwin](https://www.g2.com/products/v7-darwin/reviews)

V7 Darwin is a specialized AI platform for creating high-quality training data and managing annotation workflows. It is engineered for teams building sophisticated computer vision models and solving complex, domain-specific challenges with AI. V7 Darwin provides a comprehensive suite of tools for data labeling, video annotation, and medical imaging annotation. - Create pixel-perfect image and video annotations with Auto-Annotate and SAM for semantic masks, instance segmentation, keypoints, and polygons. - Develop medical AI with tools for DICOM, NIfTI, and WSI annotation, featuring an interface with MPR, 3D rendering, precise crosshairs, windowing, and oblique views. - Accelerate video annotation by up to 10x with AI-assisted auto-tracking for objects across frames. - Manage long videos, multi-camera views, and nested annotation classes. - Design multi-stage review workflows with conditional logic, consensus, and task assignment for your data labeling pipeline. - Organize, filter, and manage large datasets with custom views and tags, enabling real-time team collaboration for annotators, reviewers, and ML engineers. - Scale your annotation projects with professional data labeling services, including certified annotators and experts in various domains (medical, video, LLMs, scientific). You can seamlessly integrate V7 Darwin with your existing tech stack and import/export annotations with ease. Get complete control over your models, tasks, and datasets through the open API, Darwin-py SDK, and CLI.

**Average Rating:** 4.7/5.0

**Total Reviews:** 55

#### How Do G2 Users Rate V7 Darwin?

- **Labeler Quality:** 9.4/10 (Category avg: 8.9/10)
- **Object Detection:** 9.4/10 (Category avg: 8.9/10)
- **Data Types:** 9.2/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind V7 Darwin?

- **Seller:** [V7](https://www.g2.com/sellers/v7)
- **Year Founded:** 2018
- **HQ Location:** London, England
- **Twitter:** @v7labs  
3,471 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=de877e1b154c6b0c137e7416f2b5d21ab1d53e5f5fc06f8706ec44d415a22436&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fv7labs%2F&secure%5Burl_type%5D=linkedin_company_website)  
106 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 55% Small, 35% Medium

#### What Do G2 Reviewers Say About V7 Darwin?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of V7 Darwin, simplifying annotation and project management with an intuitive interface.
- Users value the **annotation efficiency** of V7 Darwin, enabling easy labeling of complex datasets and improving productivity.
- Users find the **automatic annotation tools** of V7 Darwin extremely helpful for easy and efficient dataset labeling.
- Users value the **innovative LLM integration** in V7 Darwin, enhancing spreadsheet functionality with rich content support.
- Users commend the **efficiency** of V7 Darwin, streamlining HR processes and enhancing productivity through automation.

##### Cons

- Users find the **lack of features** frustrating, especially regarding annotation management and export options for datasets.
- Users find **missing features** like lacking retraction options and limited export formats frustrating in V7 Darwin.
- Users find the **limited features** of V7 Darwin frustrating, desiring more flexibility in annotations and dataset manipulation.
- Users are frustrated by the **annotation issues** that hinder their ability to retract or approve submissions smoothly.
- Users find the **difficult navigation** of V7 Darwin frustrating, as it hampers their ability to utilize features effectively.

#### What Are Recent G2 Reviews of V7 Darwin?

**["Comprehensive HRMS for End-to-End Employee Lifecycle Management"](https://www.g2.com/survey_responses/v7-darwin-review-11727041)**

**Rating:** 4.0/5.0 stars

_— Shiv S._

[Read full review](https://www.g2.com/survey_responses/v7-darwin-review-11727041)

**["Easy Video Annotation and Predictive Labeling for Massive Datasets"](https://www.g2.com/survey_responses/v7-darwin-review-12700843)**

**Rating:** 4.5/5.0 stars

_— Jed D._

[Read full review](https://www.g2.com/survey_responses/v7-darwin-review-12700843)

#### What Are G2 Users Discussing About V7 Darwin?

- [What is V7 used for?](https://www.g2.com/discussions/what-is-v7-used-for)

### [Sama](https://www.g2.com/products/sama/reviews)

Sama is a globally recognized leader in data annotation solutions for enterprise computer vision and generative AI models that require the highest accuracy. As an industry pioneer with 15 years of experience, Sama’s expertise and solutions are trusted by leading companies such as GM, Ford, Continental, Google, and many more. Sama specializes in data annotation services for generative AI, and 2D and 3D image and video (including LiDAR and sensor fusion). We also validate complex machine learning algorithms. As a leader in ethical AI and a Certified B-Corp, we’ve pioneered an impact model that harnesses the power of markets for social good. We have meaningfully improved employment and income outcomes for those with the greatest barriers to formal work (validated by an independent MIT study). So far, we've helped more than 60,000 people lift themselves out of poverty. For more information, visit www.sama.com

**Average Rating:** 4.6/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate Sama?

- **Labeler Quality:** 9.0/10 (Category avg: 8.9/10)
- **Object Detection:** 9.6/10 (Category avg: 8.9/10)
- **Data Types:** 9.6/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Sama?

- **Seller:** [Sama](https://www.g2.com/sellers/sama)
- **Year Founded:** 2008
- **HQ Location:** San Francisco, US
- **Twitter:** @SamaAI  
228,871 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=05e2827df365f003c18aadac7b3a03b2ddcf89a9cc0237cf0288b460cca1fcfb&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F410136&secure%5Burl_type%5D=linkedin_company_website)  
4,342 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 55% Small, 36% Large

#### What Do G2 Reviewers Say About Sama?

_AI-generated summary from verified user reviews_

##### Pros

- Users value the **powerful analytics and data preparation features** of Sama, enhancing their insights and efficiency.
- Users appreciate the **24/7 customer support** of Sama, finding it helpful alongside tutorials and demos for better usability.
- Users commend Sama for its **amazing data cataloging features** , enhancing data preparation and providing fast insights.
- Users value the **amazing data pre-processing and enrichment features** of Sama, enhancing their analytics experience.
- Users appreciate the **amazing data pre processing and enrichment features** of Sama, enhancing their analytics capabilities.

##### Cons

- Users find the product **complex and hard to run** , necessitating extensive training or skilled personnel to operate effectively.
- Users find the **complex setup** of Sama demanding extensive training or skilled personnel to operate effectively.
- Users struggle with the **lack of training** required for Sama, finding it complex and difficult to operate effectively.
- Users find the **extensive training requirements** of Sama challenging, making it difficult to operate effectively.

#### What Are Recent G2 Reviews of Sama?

**["Translates to better model performance"](https://www.g2.com/survey_responses/sama-review-9669978)**

**Rating:** 4.5/5.0 stars

_— Mohammad A._

[Read full review](https://www.g2.com/survey_responses/sama-review-9669978)

**["Impressive accuracy on their data annotations"](https://www.g2.com/survey_responses/sama-review-9935840)**

**Rating:** 4.5/5.0 stars

_— Nikita D._

[Read full review](https://www.g2.com/survey_responses/sama-review-9935840)

### [Outlier AI](https://www.g2.com/products/outlier-ai-outlier-ai/reviews)

Outlier AI is a platform that connects human expertise with artificial intelligence to enhance the accuracy, speed, and reliability of AI models. By engaging a global network of over 100,000 experts across more than 50 countries, Outlier AI has facilitated the development of more knowledgeable and impactful AI systems, distributing over $500 million to its contributors. Key Features and Functionality: - Expert-Driven AI Training: Outlier AI leverages the specialized skills of its global expert community to train and refine AI models, ensuring high-quality data annotation and model development. - Flexible Remote Work Opportunities: The platform offers individuals meaningful and accessible job opportunities, allowing experts to contribute remotely and on their own schedules. - Integration with Scale AI: Powered by Scale AI, Outlier AI combines top-tier data infrastructure with advanced anomaly detection capabilities, enhancing the scalability and accuracy of AI solutions. Primary Value and Problem Solved: Outlier AI addresses the challenge of developing reliable and effective AI models by integrating human expertise into the AI training process. This approach not only improves the quality of AI outputs but also provides flexible employment opportunities to a diverse global workforce. By bridging the gap between human intelligence and artificial intelligence, Outlier AI ensures that AI systems are more accurate, efficient, and aligned with real-world applications.

**Average Rating:** 4.0/5.0

**Total Reviews:** 14

#### How Do G2 Users Rate Outlier AI?

- **Labeler Quality:** 9.6/10 (Category avg: 8.9/10)
- **Object Detection:** 9.2/10 (Category avg: 8.9/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.1/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Outlier AI?

- **Seller:** [Outlier AI](https://www.g2.com/sellers/outlier-ai)
- **Year Founded:** 2023
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=cd7fc401ed04daac65acf0d9fb4d1889c1db791ed38161618afccb1b10a73dfa&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftry-outlier&secure%5Burl_type%5D=linkedin_company_website)  
28,516 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 100% Small

#### What Do G2 Reviewers Say About Outlier AI?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **helpful customer support** team of Outlier AI, enhancing their overall experience and satisfaction.
- Users value the **high data accuracy** of Outlier AI, appreciating its commitment to well-thought-out responses.
- Users appreciate the **flexibility** of Outlier AI, allowing for remote project work and skill-based freelancing.
- Users appreciate the **transparent and fast payment system** of Outlier AI, enhancing their overall experience and satisfaction.
- Users highlight the **fast response speed** of Outlier AI, enhancing their overall experience with the platform.

##### Cons

- Users note **work interruptions** due to inconsistent project availability and challenging assessments, impacting their overall experience.
- Users report **buggy performance** with Outlier AI, leading to inconsistent project assessments and unexpected removals from tasks.
- Users are frustrated by the **low compensation** and inconsistent project availability, affecting their overall experience with Outlier AI.
- Users note that **performance issues** hinder a faster, smoother experience with Outlier AI, affecting usability.
- Users face **poor customer support** with unannounced work changes and inadequate communication from management, causing frustration.

#### What Are Recent G2 Reviews of Outlier AI?

**["Flexible, Seamless Workflow for Meaningful AI Projects"](https://www.g2.com/survey_responses/outlier-ai-review-12904617)**

**Rating:** 4.0/5.0 stars

_— Verified User in Information Technology and Services_

[Read full review](https://www.g2.com/survey_responses/outlier-ai-review-12904617)

**["A Developer’s Dream at the Intersection of AI"](https://www.g2.com/survey_responses/outlier-ai-review-12834046)**

**Rating:** 4.5/5.0 stars

_— Verified User in Computer Software_

[Read full review](https://www.g2.com/survey_responses/outlier-ai-review-12834046)

### [Keymakr](https://www.g2.com/products/keymakr/reviews)

We are a data labeling company that focuses on providing high quality annotation services and excellent customer support. We are the best choice for: Image Annotation Video Annotation Data validation Document Annotation Data Creation Data Collection Our company creates best-in-class computer vision training data. We offer an in-house team paired with advanced proprietary annotation tools. Scalable and secure one-stop shop for your AI

**Average Rating:** 4.8/5.0

**Total Reviews:** 45

#### How Do G2 Users Rate Keymakr?

- **Labeler Quality:** 9.4/10 (Category avg: 8.9/10)
- **Object Detection:** 9.7/10 (Category avg: 8.9/10)
- **Data Types:** 9.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Keymakr?

- **Seller:** [Keymakr](https://www.g2.com/sellers/keymakr)
- **Year Founded:** 2015
- **HQ Location:** New York, NY
- **Twitter:** @keymakr\_com  
354 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=5fb808098ef63b965158417815d66f2ac2810beadbf8235c0f26f8875a0b72ae&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fkeymakr%2F&secure%5Burl_type%5D=linkedin_company_website)  
69 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 52% Small, 22% Medium

#### What Do G2 Reviewers Say About Keymakr?

_AI-generated summary from verified user reviews_

##### Pros

- Users commend Keymakr for their **exceptional customer support** , highlighting fast responses and a proactive service approach.
- Users appreciate Keymakr's **efficiency** in integrating with workflows, streamlining processes, and enhancing productivity effortlessly.
- Users find the **initial setup incredibly easy** , enjoying a smooth start with Keymakr's efficient implementation process.
- Users value the **efficient annotation process** of Keymakr, enhancing workflow and ensuring quality results quickly.
- Users value the **excellent data management** of Keymakr, appreciating the team's commitment to quality and client satisfaction.

##### Cons

- Users find the **difficult setup** challenging, with convoluted navigation and connection issues during initial use.
- Users face **recurring annotation mistakes** , though improvements in data quality and processes have enhanced results over time.
- Users face challenges with **complex UI navigation** and initial setup, particularly with S3 connectivity.
- Users find the **limited customization** of Keymakr frustrating, particularly the inability to change passwords independently.

#### What Are Recent G2 Reviews of Keymakr?

**["A true Partner in Image Recognition"](https://www.g2.com/survey_responses/keymakr-review-11968704)**

**Rating:** 5.0/5.0 stars

_— Yacine B._

[Read full review](https://www.g2.com/survey_responses/keymakr-review-11968704)

**["Accurate Annotation, Valuable Results"](https://www.g2.com/survey_responses/keymakr-review-11103169)**

**Rating:** 4.5/5.0 stars

_— Rinat L._

[Read full review](https://www.g2.com/survey_responses/keymakr-review-11103169)

### [Taskmonk](https://www.g2.com/products/taskmonk/reviews)

Taskmonk is the enterprise AI training data platform for teams that need annotation to work at production scale, not pilot scale. It's one of the few platforms that handles six data modalities: text, image, audio, video, LiDAR, and DICOM in a single environment, so teams aren't stitching together separate tools once a project moves past standard computer vision. Built for annotation teams, project managers, and AI leads alike, Taskmonk pairs a no-code workflow builder with AI-assisted labeling and QA that holds up at scale: • No-code/low-code workflows that adapt per project without engineering time • AI-assisted labeling and model-assisted pre-labeling that cut annotation hours and lift throughput • Multi-stage QA like gold sets, consensus, adjudication, plus affinity-based task routing by annotator performance, not just role • SOC 2 Type II and ISO 27001 compliance, with HIPAA and data residency available for regulated industries Taskmonk has processed 480M+ annotation tasks and 6M+ labeling hours for 10+ Fortune 500 clients, including Flipkart, Myntra, and LG, saving clients $10M+ to date. Teams can also plug in a vetted, SLA-backed annotation workforce on the same platform, so there's one contract and one escalation path instead of sourcing labeling talent separately

**Average Rating:** 4.6/5.0

**Total Reviews:** 17

#### How Do G2 Users Rate Taskmonk?

- **Labeler Quality:** 9.3/10 (Category avg: 8.9/10)
- **Object Detection:** 9.2/10 (Category avg: 8.9/10)
- **Data Types:** 9.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Taskmonk?

- **Seller:** [Taskmonk](https://www.g2.com/sellers/taskmonk)
- **Year Founded:** 2018
- **HQ Location:** Bengaluru, Karnataka, India
- **Twitter:** @TaskmonkAI  
16 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=1d6fc148b17fe971252bbfff900d04da0cb064fa240b87046de8c9b9bcd442f6&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftaskmonk%2F&secure%5Burl_type%5D=linkedin_company_website)  
28 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 72% Small, 22% Large

#### What Do G2 Reviewers Say About Taskmonk?

_AI-generated summary from verified user reviews_

##### Pros

- Users find Taskmonk to be **incredibly easy to use** , with a quick setup and intuitive interface for all levels.
- Users commend Taskmonk for its **exceptional customer support** , noting their responsiveness and dedication to helping clients succeed.
- Users highlight the **efficiency** of Taskmonk, enabling quick setups and seamless project management for high-volume tasks.
- Users value the **intuitive interface and robust features** of Taskmonk, making data labeling efficient and user-friendly.
- Users commend the **quick and easy setup** of Taskmonk, enabling rapid onboarding and seamless integration into workflows.

##### Cons

- Users find the **lack of features** limits advanced reporting and flexibility, affecting usability and experience.
- Users find the **difficult learning** curve challenging, especially for beginners navigating the complex UI.
- Users find the **complexity of the UI** overwhelming, particularly with the number of options presented at once.
- Users experience occasional **technical difficulties** , but the Taskmonk team quickly resolves issues and provides solutions.
- Users find **upload issues** frustrating, particularly with cloud restrictions and delays during peak times impacting efficiency.

#### What Are Recent G2 Reviews of Taskmonk?

**["Impressive Product!"](https://www.g2.com/survey_responses/taskmonk-review-11562637)**

**Rating:** 4.0/5.0 stars

_— Aditya N._

[Read full review](https://www.g2.com/survey_responses/taskmonk-review-11562637)

**["A beginner-friendly, fast, and highly customisable tool for any type of process."](https://www.g2.com/survey_responses/taskmonk-review-11752427)**

**Rating:** 5.0/5.0 stars

_— JAYAPRAKASH K._

[Read full review](https://www.g2.com/survey_responses/taskmonk-review-11752427)

### [Appen](https://www.g2.com/products/appen/reviews)

Appen collects and labels images, text, speech, audio, video, and other data to create training data used to build and continuously improve the world’s most innovative artificial intelligence systems. We offer a state of the art, licensable data annotation platform to annotate training data use cases in computer vision and natural language processing. Our platform enhances accuracy and efficiency through our Smart Labeling and Pre-Labeling features which use Machine Learning to ease human annotations. You choose the level of service and security you want for data collection and annotation, from white-glove managed service to flexible self-service. Our expertise includes having a global crowd of over 1 million skilled contractors who speak over 235 languages and dialects, in over 70,000 locations and 170 countries, and the industry’s most advanced AI-assisted data annotation platform. Our reliable training data gives leaders in technology, automotive, financial services, retail, healthcare, and governments the confidence to deploy world-class AI products. Founded in 1996, Appen has customers and offices globally.

**Average Rating:** 4.2/5.0

**Total Reviews:** 33

#### How Do G2 Users Rate Appen?

- **Labeler Quality:** 8.5/10 (Category avg: 8.9/10)
- **Object Detection:** 8.7/10 (Category avg: 8.9/10)
- **Data Types:** 8.8/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Appen?

- **Seller:** [Appen](https://www.g2.com/sellers/appen)
- **Year Founded:** 1996
- **HQ Location:** Kirkland, Washington, United States
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=b0931c0a7f2c53197a2b59e80de4a7fdfa27c61180c1a3b6c615dca728909c73&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fappen&secure%5Burl_type%5D=linkedin_company_website)  
20,647 employees on LinkedIn®
- **Ownership:** ASX:APX
- **Total Revenue (USD mm):** $244,900

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services
- **Company Size:** 54% Small, 26% Large

#### What Do G2 Reviewers Say About Appen?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **flexibility** of Appen, enabling them to engage in diverse and interesting projects from their cell phones.
- Users appreciate the **ease of use** of Appen, enabling task completion effortlessly through their cell phones.
- Users appreciate the **flexibility** of Appen, enabling engaging and diverse projects that enhance their work experience.

##### Cons

- Users experience frequent **work interruptions** , facing challenges like inconsistent availability and lengthy qualification processes.
- Users experience **low compensation** due to inconsistent work availability and monthly payments, affecting income stability.
- Users find the **navigation confusing** and experience frequent disconnects, leading to frustration while using Appen.
- Users often face **connectivity issues** , experiencing confusion while navigating and frequent disconnections from the app.
- Users find the **navigation confusing** , often resulting in unexpected logouts that disrupt their experience with Appen.

#### What Are Recent G2 Reviews of Appen?

**["Ideal for Freelancers, Simplicity with Room for Support Improvement"](https://www.g2.com/survey_responses/appen-review-12550258)**

**Rating:** 5.0/5.0 stars

_— Ashish S._

[Read full review](https://www.g2.com/survey_responses/appen-review-12550258)

**["Robust Crowdsourcing Platform for AI and Language Tasks"](https://www.g2.com/survey_responses/appen-review-12769449)**

**Rating:** 4.0/5.0 stars

_— Sina A._

[Read full review](https://www.g2.com/survey_responses/appen-review-12769449)

### [Kili](https://www.g2.com/products/kili/reviews)

Kili Technology is a collaborative AI data platform designed to meet the rigorous needs of building large-scale production-ready AI data securely. Founded in Paris in 2018, Kili Technology caters to a diverse range of industries, including healthcare, financial services, manufacturing, defense, and technology. The platform is engineered to support teams of varying sizes, accommodating anywhere from 1 to over 500 concurrent users, and processes millions of assets annually. The core functionality of Kili Technology lies in its ability to facilitate collaboration among cross-functional teams. Unlike traditional labeling tools that primarily serve machine learning engineers, Kili connects data science teams with business stakeholders and subject matter experts. This integration enhances the AI development lifecycle by streamlining processes from annotation and labeling to validation and model feedback. As a result, users can ensure that the data used for training AI models is not only accurate but also relevant to the specific business context. Kili Technology is particularly beneficial for organizations looking to harness the power of AI while maintaining a high level of data quality. The platform supports various data modalities, allowing teams to work with text, images, audio, and video data seamlessly. This versatility makes it suitable for a wide range of applications, from developing natural language processing models to image recognition systems. By fostering collaboration among different roles within an organization, Kili enhances the overall efficiency of the AI development process. Key features of Kili Technology include an intuitive user interface that simplifies the labeling process, robust tools for data validation, and comprehensive feedback mechanisms that enable continuous improvement of AI models. Additionally, the platform offers advanced analytics capabilities, allowing teams to track progress and identify areas for enhancement. These features collectively empower organizations to build high-quality training datasets that meet the demands of complex AI applications. Kili Technology stands out in the competitive landscape of AI data platforms by prioritizing collaboration and usability. By bridging the gap between technical and non-technical stakeholders, it ensures that the development of AI solutions is a cohesive effort. This approach not only accelerates the time to market for AI initiatives but also enhances the overall quality of the training data, ultimately leading to more effective AI models.

**Average Rating:** 4.7/5.0

**Total Reviews:** 52

#### How Do G2 Users Rate Kili?

- **Labeler Quality:** 9.2/10 (Category avg: 8.9/10)
- **Object Detection:** 9.2/10 (Category avg: 8.9/10)
- **Data Types:** 9.2/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Kili?

- **Seller:** [Kili Technology](https://www.g2.com/sellers/kili-technology)
- **Company Website:** kili-technology.com
- **Year Founded:** 2018
- **HQ Location:** Paris, FR
- **Twitter:** @Kili\_Technology  
438 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=528a4a4dc014455d66d25ef4421bc2e8c6ca341820ef9e1a434c4f0e6a6e0e80&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F33266852&secure%5Burl_type%5D=linkedin_company_website)  
48 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Medium, 34% Small

#### What Do G2 Reviewers Say About Kili?

_AI-generated summary from verified user reviews_

##### Pros

- Users love the **ease of use** of Kili's annotation platform and appreciate its comprehensive metrics for projects.
- Users value the **ease of use** and precise metrics for comprehensive project visualization on Kili's annotation platform.
- Users love the **ease of use** offered by Kili, enhancing their annotation experience significantly.
- Users love the **variety of models** available on Kili, enhancing their annotation projects with tailored solutions.

##### Cons

- Users feel that Kili lacks **adequate content updates** , limiting the platform's overall utility and engagement.
- Users feel Kili lacks **adequate content updates** , limiting their overall experience and engagement with the platform.

#### What Are Recent G2 Reviews of Kili?

**["Intuitive UX and Quick Installation"](https://www.g2.com/survey_responses/kili-review-12342244)**

**Rating:** 5.0/5.0 stars

_— Hery R._

[Read full review](https://www.g2.com/survey_responses/kili-review-12342244)

**["Ease of Use and Exceptional Efficiency"](https://www.g2.com/survey_responses/kili-review-12354373)**

**Rating:** 5.0/5.0 stars

_— Lantosoa V._

[Read full review](https://www.g2.com/survey_responses/kili-review-12354373)

### [Datature](https://www.g2.com/products/datature/reviews)

Datature is an AI Vision platform that simplifies computer vision development by unifying data labeling, model training, and deployment into a single workflow. By eliminating the need for fragmented tools and complex infrastructure, teams can focus on solving real-world problems.

**Average Rating:** 4.9/5.0

**Total Reviews:** 39

#### How Do G2 Users Rate Datature?

- **Labeler Quality:** 9.5/10 (Category avg: 8.9/10)
- **Object Detection:** 9.9/10 (Category avg: 8.9/10)
- **Data Types:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.5/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Datature?

- **Seller:** [Datature](https://www.g2.com/sellers/datature)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @DatatureAI  
168 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=63ff7ae5a5cc37c5d990c326e31c4bcfe336374f4a4d8bc4267e834b2e8dc247&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdatature%2F&secure%5Burl_type%5D=linkedin_company_website)  
23 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Research
- **Company Size:** 64% Small, 28% Large

#### What Do G2 Reviewers Say About Datature?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **efficiency** of Datature, enabling quick data labeling and streamlined model training for faster project progress.
- Users value the **annotation efficiency** of Datature, streamlining their machine learning projects with user-friendly tools.
- Users find Datature to be **extremely user-friendly** , simplifying data labeling and model training for all skill levels.
- Users value the **extensive model types** and options in Datature, praising its support and user-friendly design.
- Users appreciate the **efficient AI capabilities** of Datature, enhancing data labeling and model training processes significantly.

##### Cons

- Users find **limited customization options** in Datature may hinder advanced users seeking more control over their setups.
- Users experience **annotation tool clarity issues** , yet prompt support in Slack helps resolve problems quickly.
- Users experience a **difficult learning curve** when setting up labeling jobs, despite finding model building easy.
- Users face a **difficult setup** for labeling jobs, which may hinder their initial experience with Datature.
- Users note the **high cost** of Datature, which may be a barrier for personal users despite its quality.

#### What Are Recent G2 Reviews of Datature?

**["Impressive range of CV models makes custom Vision AI projects easy"](https://www.g2.com/survey_responses/datature-review-12102117)**

**Rating:** 4.5/5.0 stars

_— Phil B._

[Read full review](https://www.g2.com/survey_responses/datature-review-12102117)

**["Extremely User-Friendly Platform Backed by a Kind and Committed Team"](https://www.g2.com/survey_responses/datature-review-13081746)**

**Rating:** 5.0/5.0 stars

_— Sam R._

[Read full review](https://www.g2.com/survey_responses/datature-review-13081746)

### [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews)

Company Overview: CVAT.ai is a global provider of data annotation tools and services, known for developing one of the most popular open-source annotation tools, CVAT. In addition to the open-source platform, we offer professional data labeling services, an Enterprise version of CVAT, as well as consulting and customization services to meet specific client needs. Our team supports businesses and AI researchers worldwide in efficiently managing data annotation for computer vision projects. Key Features: - Popular Open-Source Tool: CVAT is trusted by thousands of developers and organizations globally. - Data Labeling Services: We provide expert data labeling services to handle projects from start to finish. - Enterprise Version of CVAT: The Enterprise version offers advanced features, support, and scalability for larger organizations. - Consulting and Customization: We offer consulting services and can customize CVAT to match your project needs. Learn more about our approach to consulting and feature requests here. - AI-Assisted Automation: Our platform uses AI to enhance labeling efficiency and accuracy. - Team Collaboration: Teams can collaborate seamlessly on large-scale projects. - Customizable and Scalable: CVAT can be adapted to your project size and needs. - Secure: We meet global data privacy and security standards. What We Solve: CVAT.ai helps users reduce manual efforts by making data annotation faster, more accurate, and easy to manage. Through our open-source platform, professional labeling services, consulting, and the Enterprise version, we offer a flexible, comprehensive solution for any computer vision project.

**Average Rating:** 4.5/5.0

**Total Reviews:** 31

#### How Do G2 Users Rate CVAT.ai?

- **Labeler Quality:** 9.5/10 (Category avg: 8.9/10)
- **Object Detection:** 9.2/10 (Category avg: 8.9/10)
- **Data Types:** 8.6/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)

#### Who Is the Company Behind CVAT.ai?

- **Seller:** [CVAT.ai](https://www.g2.com/sellers/cvat-ai)
- **Company Website:** www.cvat.ai
- **Year Founded:** 2022
- **HQ Location:** Palo Alto, US
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=3fcff5bc744a0faed9f68d2f3d4c21c790b4a51f28c113fb6952ea0700f32084&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fcvat-ai%2F&secure%5Burl_type%5D=linkedin_company_website)  
114 employees on LinkedIn®

#### Who Uses This Product?

- **Company Size:** 71% Small, 19% Medium

#### What Do G2 Reviewers Say About CVAT.ai?

_AI-generated summary from verified user reviews_

##### Pros

- Users praise the **exceptional customer support** from CVAT.ai, enhancing their experience with valuable assistance and guidance.
- Users commend the **amazing annotation speed** of CVAT.ai, ensuring efficient and high-quality results for complex tasks.
- Users value the **customization options** of CVAT.ai, enabling tailored annotation projects and versatile setups.
- Users praise the **efficient data management** of CVAT.ai, which enhances workflow and collaboration in medical imaging projects.
- Users appreciate the **versatile annotation options** of CVAT.ai, enabling support for various computer vision algorithms.

##### Cons

- Users find the platform **difficult to learn** , especially beginners, due to its complexity and time-consuming features.
- Users find the **complexity of the interface** can be overwhelming, especially for beginners navigating the features.
- Users notice **labeling inconsistencies** in complex cases, though the team quickly addresses feedback to improve accuracy.
- Users experience a **lack of features** due to missing support for large 16-bit images in CVAT.ai.
- Users experience **slow performance** with CVAT.ai when processing large video files and numerous images.

#### What Are Recent G2 Reviews of CVAT.ai?

**["Model-Assisted CVAT Annotation Turned Weeks of Work Into Days"](https://www.g2.com/survey_responses/cvat-ai-review-12981074)**

**Rating:** 4.0/5.0 stars

_— Fabian v._

[Read full review](https://www.g2.com/survey_responses/cvat-ai-review-12981074)

**["Powerful image annotation with prelabeling and review tools"](https://www.g2.com/survey_responses/cvat-ai-review-12936375)**

**Rating:** 4.5/5.0 stars

_— Katarzyna K._

[Read full review](https://www.g2.com/survey_responses/cvat-ai-review-12936375)

### [Dataloop](https://www.g2.com/products/dataloop-dataloop/reviews)

Dataloop is a cutting-edge AI Development Platform that's transforming the way organizations build AI applications. Our platform is meticulously crafted to cater to developers at the heart of the AI development process, making it simpler and more intuitive to work with data and AI models. Our comprehensive solution spans the full AI development lifecycle, offering tools and functionalities that streamline data management, annotation, model selection, and deployment. Dataloop's platform is built with a focus on collaboration, allowing developers, data scientists, and engineers to work together seamlessly, breaking down traditional silos and fostering innovation. Key features include an intuitive drag-and-drop interface for constructing data pipelines, a vast library of pre-built AI elements and models, and robust data curation and annotation capabilities. These features are designed to empower developers to rapidly prototype, iterate, and deploy AI solutions, keeping pace with the fast-evolving demands of the market. Dataloop is committed to advancing AI development by providing a developer-centric platform that addresses the complexities and challenges of AI and data management. Our vision is to democratize AI development, enabling every organization to harness the power of AI and drive forward their innovative solutions.

**Average Rating:** 4.4/5.0

**Total Reviews:** 87

#### How Do G2 Users Rate Dataloop?

- **Labeler Quality:** 8.8/10 (Category avg: 8.9/10)
- **Object Detection:** 9.2/10 (Category avg: 8.9/10)
- **Data Types:** 9.2/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.8/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Dataloop?

- **Seller:** [Dataloop](https://www.g2.com/sellers/dataloop)
- **Year Founded:** 2017
- **HQ Location:** Herzliya, IL
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=da9efd7c177fd7b05cc561f5bccd297b04f67ce9561db7a8c8492155b1c98891&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdataloop&secure%5Burl_type%5D=linkedin_company_website)  
52 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 39% Medium, 33% Small

#### What Do G2 Reviewers Say About Dataloop?

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate the **ease of use** of Dataloop, finding the intuitive UI and annotation simple and effective.
- Users appreciate the **annotation efficiency** of Dataloop, enjoying a simple interface that enhances the overall experience.
- Users value the **easy annotation** capabilities of Dataloop, benefitting from its simple and intuitive interface.
- Users love the **simple and easy-to-navigate interface** of Dataloop, enhancing their overall experience with the tool.
- Users appreciate the **easy integrations** of Dataloop, enhancing their existing workflows effortlessly.

##### Cons

- Users find the **UI changes complicated** , making the overall experience with Dataloop less intuitive.
- Users find the **confusing syntax** of Dataloop frustrating, particularly after recent UI changes that impacted usability.
- Users find the **difficult navigation** due to UI changes to be confusing, impacting their overall experience.
- Users feel that the **lack of communication** from the community is hindering their overall experience with Dataloop.
- Users feel the lack of a **demo section for first-time users** hinders their onboarding experience with Dataloop.

#### What Are Recent G2 Reviews of Dataloop?

**["A journey into Data workflow with Dataloop."](https://www.g2.com/survey_responses/dataloop-review-9633025)**

**Rating:** 4.0/5.0 stars

_— Dennis R._

[Read full review](https://www.g2.com/survey_responses/dataloop-review-9633025)

**["I have had a smooth and convenient time every day I am working on Dataloop"](https://www.g2.com/survey_responses/dataloop-review-9624539)**

**Rating:** 5.0/5.0 stars

_— Mzamil J._

[Read full review](https://www.g2.com/survey_responses/dataloop-review-9624539)

#### What Are G2 Users Discussing About Dataloop?

- [What are data annotations?](https://www.g2.com/discussions/dataloop-what-are-data-annotations) - 1 comment
- [What are data annotations?](https://www.g2.com/discussions/what-are-data-annotations) - 1 comment
- [Is Dataloop free?](https://www.g2.com/discussions/dataloop-is-dataloop-free) - 1 comment
- [Is Dataloop free?](https://www.g2.com/discussions/is-dataloop-free) - 1 comment
- [What are the industries that Dataloop supports?](https://www.g2.com/discussions/dataloop-what-are-the-industries-that-dataloop-supports) - 1 comment

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[Browse Data Labeling Themes](/categories/data-labeling/themes)

 ![Bijou Barry](/assets/transparent-ad5be28fbcd25b7b08d2cebe1d957125437fb5407d75ee717965ad22c8808791.gif "Bijou Barry")
BB

Researched and written by [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)

Updated April 9, 2026

Data labeling software helps data science and machine learning teams source, manage, annotate, and classify unstructured data, including text, images, videos, audio, and PDFs, into labeled datasets that create efficient training data pipelines for building and improving AI and ML models.

### Core Capabilities of Data Labeling Software

To qualify for inclusion in the Data Labeling category, a product must:

- Integrate a managed workforce and/or data labeling service
- Ensure labels are accurate and consistent
- Give the user the ability to view analytics that monitor the accuracy and speed of labeling
- Allow annotated data to be integrated into data science and machine learning platforms to build machine learning models

### Common Use Cases for Data Labeling Software

ML engineers, data scientists, and AI teams use data labeling tools to build high-quality training datasets across a wide range of application types. Common use cases include:

- Annotating images, video, and text for computer vision, NLP, and speech recognition model training
- Fine-tuning and evaluating large language models (LLMs) with human-labeled feedback data
- Building training pipelines for object detection, named entity recognition, and sentiment analysis applications

### How Data Labeling Software Differs from Other Tools

Data labeling is a foundational building block of the AI development lifecycle, distinct from the downstream tools it feeds. It integrates with [generative AI software](https://www.g2.com/categories/generative-ai), [MLOps platforms](https://www.g2.com/categories/mlops-platforms), [data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), [LLM software](https://www.g2.com/categories/large-language-models-llms), and [active learning tools](https://www.g2.com/categories/active-learning-tools) to support the full model development pipeline.

### Insights from G2 on Data Labeling Software

Based on category trends on G2, labeling accuracy controls and workforce management features stand out as standout capabilities. Faster training data pipeline construction and improved model accuracy stand out as primary outcomes of adoption.

Show More

* * *

## How Do You Choose the Right Data Labeling Software?

### What You Should Know About Data Labeling Software

### What is Data Labeling Software?

Data labeling software labels or annotates data for training machine learning models. Machine learning algorithms rely on large amounts of labeled data to learn patterns and make predictions. Data labeling solutions help humans identify and label the relevant features and characteristics of the data that will be used to train the machine learning model.

Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data, and many data labeling platforms now extend that same tooling to computer vision tasks like bounding boxes and point cloud annotation for 3D and LiDAR data.

Data labeling software is used in various applications, including[](https://www.g2.com/articles/natural-language-processing)[natural language processing,](https://www.g2.com/articles/natural-language-processing) image and video classification, and[](https://www.g2.com/articles/object-detection)[object detection](https://www.g2.com/articles/object-detection). It is an important tool in the development and training of machine learning models and plays a critical role in their accuracy and effectiveness.

### What types of data labeling software exist?

Selecting a data labeling software requires a prior evaluation and understanding of data-driven workflows in your business. Below are the types of software you can consider.

- **Manual labeling software:** These data labeling platforms segment, label, and classify data with the help of a "[human in the loop"](https://www.g2.com/glossary/human-in-the-loop-definition) service. Human annotators label the training data based on businesses' geographic locations. The data annotation service is extended to the[ML model](https://www.g2.com/articles/machine-learning-models) development workflow, and labeling data becomes more effective.
- **Automated labeling software:** The automated data labeling software preprocesses raw datasets consisting of text, images, liDAR data, DICOM, PDF, or audio using an unsupervised learning approach. The algorithm assigns labels and categories to data without referring to external annotators.
- **Active learning labeling software:** Also known as active learning tools, these are semi-supervised tools that follow a "query-based" approach to labeling data. Based on the uncertainty score, they query data using manual or annotator labeling. For more challenging labels, they prompt the human annotator with queries.
- **Crowdsource labeling software:** These data labeling platforms crowd data labeling services to a crowd of developers to[train high-quality data pipelines](https://learn.g2.com/training-data). Custom data labeling can be ideal for large or enterprise-sized teams.
- **Integrated labeling and model training software:** These tools provide combined services for data labeling and predictive modeling. Using advanced data analysis, users can label, train, and build machine learning models to optimize their production cycles.

### What are the Common Features of Data Labeling Software?

Based on G2 reviews, data scientists and machine learning engineers evaluate data labeling software by comparing annotation tool variety, model-assisted labeling speed, and collaboration features. There are several features that are often included in data labeling software, including:

- **Label assignment:** Data labeling software allows users to assign labels or tags to specific data points, such as text, images, or videos.
- **Annotation tools:** Some data labeling software includes tools for annotating data, such as bounding boxes, polygon drawing tools, cloud points, keymakers, and point annotation tools. These tools can be used to highlight specific features or characteristics of the data.
- **Machine learning algorithms:** Some data labeling software uses machine learning algorithms to automate the labeling process or generate initial labels for data, which humans can then review and correct as needed.
- **Data management and organization** : Data labeling software often includes features for organizing and managing large datasets, such as the ability to filter and search for specific data points, track progress and completion, and generate reports.
- **Collaboration tools:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users, track changes and revisions, and review and discuss data labeling decisions.
- **Integration with data science and machine learning platforms** : Some data labeling software is designed to integrate with popular[](https://www.g2.com/categories/data-science-and-machine-learning-platforms)[data science and machine learning platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms), such as TensorFlow or PyTorch, making it easier to use the labeled data to train machine learning models.
- **Image, text, audio, or video annotation:** These tools comply with multiple unstructured data formats to train and validate models designed to generate output in images, text, video, audio, PDF, and so on.

### Benefits of Data Labeling Software

Choosing a data labeling platform empowers businesses to either pre-train existing machine learning models to save time or build new models to upgrade their workflows and train teams. 

While data labeling platforms can help do both, it also has some significant benefits listed as under:

- **Improved accuracy and quality of labeled data** : Data labeling software can help ensure that data is accurately and consistently labeled, which is critical for the accuracy and effectiveness of machine learning models.
- **Increased efficiency and productivity** : Data labeling software can help streamline the data labeling process, allowing users to label more data in less time. This can be particularly useful for large datasets or repetitive or routine tasks.
- **Enhanced collaboration and team communication:** Some data labeling software includes collaboration tools, such as the ability to assign tasks to multiple users and track changes and revisions. These tools can help improve communication and coordination within teams working on data labeling projects.
- **Reduced cost** : Using data labeling software can help reduce the cost of data labeling projects by automating routine tasks and reducing the need for manual labor.
- **Increased flexibility and scalability** : Data labeling software can be used to label a wide variety of data types and can be easily scaled up or down as needed to meet project demands.
- **Respite for data operations, ML, and data science teams:** These solutions offer agile service marketplaces with high-quality labelers and annotators that solve the problems of data cleaning, preprocessing, and classification for these teams.
- **Superpixel segmentation and brushes:** These tools are also widely used for image recognition, natural language processing (NLP), and computer vision algorithms. It creates region pools using brushing and superpixel segmentation to classify images.

### Who Uses Data Labeling Software?

The data labeling tools are a must-have for businesses that want to foray into AI automation and build robust and efficient product applications and SDK with pre-installed machine learning capabilities.

Below are the individuals and organizations that use data labeling platforms:

- **Data scientists and machine learning engineers** : Data scientists and machine learning engineers use data labeling software to label and annotate data that will be used to train machine learning models. This helps the models learn to recognize patterns and make predictions based on the labeled data.
- **Business analysts and data analysts** : Business analysts and data analysts may use data labeling software to label and annotate data to create reports and visualizations or for use in machine learning models.
- **Quality assurance professionals** : Quality assurance professionals may use data labeling software to label and annotate data to test and debug machine learning models or other software applications.
- **Researchers** : Researchers in various fields, such as computer science, linguistics, and biology, may use data labeling software to label and annotate data to conduct research or develop machine learning models.

### Alternatives to data labeling software

Some alternatives to data labeling software provide annotation and labeling services along with other machine learning features.

- [Natural language processing (NLP) software](https://www.g2.com/categories/natural-language-processing-nlp) **:** The NLP software derives semantic relationships between words of an input sentence and generates relevant and personalized content. These tools replicate the functioning of a human brain to register prompt intent and derive coherent content blocks.
- [Machine learning operationalization (MLOps software):](https://www.g2.com/categories/mlops-platforms) The MLOPs software facilitates the entire machine learning model journey, from data preprocessing to ML integration and delivery. It applies various DevOps automation concepts and runs ML-based workflows without human supervision.
- [Image recognition software:](https://www.g2.com/categories/image-recognition) Image recognition software detects, categorizes, and localizes digital images or photographs. It is based on specialized deep-learning models that group data into grids and identify relevant categories of all objects.

### Challenges with Data Labeling Software

Even though data labeling software reduces costs, provides security and privacy to data, and moderates data quality control, some evident challenges can occur at any stage of working with this platform.

Below are some of the challenges of data labeling software

- **Data quality and consistency:** It is not certain that data labeling tools would predict accurate labels for ML models. Sometimes, the platform can incorrectly categorize text as video or process incorrect calculations, which can lower the data quality.
- **Scalability:** As a business receives large influxes of data, repurposing raw data to train models, make model versions, calculate risks, and be consistent with quality control becomes a challenge and results in scalability problems for different teams across the company.
- **Cost: ** Though data labeling platforms tend to be cheaper than other expensive human annotation services, submitting a large cluster of datasets for categorization can become costly. It would exhaust your credits and leave you with no alternative but to upgrade to a more expensive plan.
- **Complexity of tasks:** Not all data labeling tasks are simple. Some require deep domain exercises and more specialized algorithm training, such as reinforcement learning, query sampling, or entropy, to build ML models accurately without investing in external annotation services.
- **Data privacy and security:** These platforms are open source or paid. However, they retrieve and store data on[](https://www.g2.com/categories/hybrid-cloud-storage-solutions)[hybrid](https://www.g2.com/categories/hybrid-cloud-storage-solutions) or[](https://www.g2.com/articles/public-cloud)[public cloud storage platforms](https://www.g2.com/articles/public-cloud), which can infect your dataset and give hackers and fishers leeway to infect the data. 

### What companies should buy data labeling software?

Companies that want to optimize the quality of their datasets and build powerful algorithms should consider data labeling software. Not just because it helps label data but because it can build accurate predictions and forecasts. Here are some companies that can benefit from these tools:

- **Machine learning startups or research labs:** These companies conduct the majority of machine learning experiments and constantly work with data tools. Investing in a data labeling tool can benefit their AI research and ML model development processes.
- **Data companies:** Companies that provide data management services like search engines, e-commerce platforms, or social media management tools also need data labeling software to generate effective algorithms that generate accurate responses and deal with large data volumes.
- **Market research companies:** Companies that conduct market research or gather customer insights and trends can also benefit from data labeling platforms. These platforms allow them to gather real-time market trends and track consumer behaviors.
- **Healthcare organizations:** These companies utilize data labeling platforms for early detection of diseases, medical imaging, patient recordkeeping, consultation, and treatments. With this software, they accurately study patient data and forecast treatment cycles.

### How to Buy Data Labeling Software

Investing in data labeling software is a step-by-step process that requires the input of all related teams and stakeholders. Below are the steps buyers need to follow chronologically to purchase the best data labeling platform for their business. 

#### Requirements Gathering (RFI/RFP) for Data Labeling Software

Before purchasing, buyers should consider their needs and determine what they hope to achieve with this software. Evaluate the type of database system, products, AI maturity, and budget data from revenue teams. Also, make a list of the data-related and language services you expect from the product. Enlist all these points in the form of a structured request for proposal (RFP) and get the approval of your teams and stakeholders who are involved in the decision-making process.

#### Compare Data Labeling Software Products

Evaluate the shortlisted products' features, security and privacy guidelines, pros and cons, pricing, and AI functionalities. Compare the features and benefits with the requirements your team has listed in the request for proposal. Analyze the budget, contract metrics, and return on investment for each software feature and compare them with those of other contenders in the market. 

At this stage, buyers can also request demos or free trials to see how the software works and ensure it meets their needs. While shortlisting vendors, it is also crucial to consider their credibility. Look for vendors with a strong track record and a good reputation.

#### Selection of Data Labeling Software

Discuss all shortlisted software's technical and configuration workflows with your IT and software development teams. Sit with them to analyze current software consumption, active subscription plans, system of records, and IT audit reports, and then check where this software fits in your tech stack. Discuss the compatibility of the software with related account executives and sales teams to ensure that the software doesn't cause more overheads and storage expenses for your teams.

#### Negotiation

After finalizing the software, get your legal teams to draft a legitimate contract outlining RFP terms, renewal policies, data retention and privacy policies, and the vendor's non-compete and discuss it with the vendor. At this stage, it is also feasible to negotiate for a better subscription rate, more features, or add-ons that buyers are interested in at the vendor's discretion. 

#### Final decision

The final decision to purchase data labeling software lies with the buyer's decision-making teams. These could be the chief information officer (CIO), head of the data science team, or procurement team. While making this decision, it is also important to consider budget constraints, team queries, or business objectives. It will be helpful to consult with stakeholders and experts, like data scientists and ML engineers, to get their input on the best data labeling solution for the institution.

### What does data labeling software cost?

The cost of data labeling software can vary widely depending on its specific features and capabilities, as well as the size and scope of the deployment. Some software is free or open-source, while others are commercial products sold on a subscription or per-use basis.

Data labeling software designed for enterprise-level use with a wide range of advanced features will be more expensive than straightforward solutions. Prices can range from a few hundred dollars per year for an introductory subscription to several thousand dollars for a more comprehensive solution.

It is essential to evaluate subscription, license, pay-per-seat, and pay-per-token usage costs to check whether the product is suitable for your business and has scope for a decent return on investment (ROI). While you are engaged in the monetary calculations, factor in software upgrade cost, business size, version, software maintenance, and upsell costs to indicate the budget clearly. These tools can help improve productivity and efficiency, contributing to ROI calculation.

To calculate the ROI of data labeling software, the following formula can be used:

ROI = (Benefits - Costs) / Costs

"Benefits" is the value of the time saved and increased productivity resulting from using the software, and "Costs" is the total cost of the software license and any additional costs associated with implementation and use.

### Implementation of data labeling software

When considering purchasing data labeling software, companies should have a rough vision of how to implement it for data science and machine learning teams.

Other factors, such as alignment with notebook editors, statistical tools, data analysis limitations, training, and testing ML cycles, will be altered and modified per the implementation timeline of data labeling software. Below are some tips to ensure a smooth implementation.

- **Integration with existing data and ML workflows:** Consult your software development teams on setting up user permissions and integrating this platform with your existing code development platform, such as R or Python editors. The first step is to ensure it is compatible with various data formats, data types, data analysis tools, and other collaborative ML tools.
- **Customization and flexibility in labeling tasks:** These platforms must be agile and compatible with datasets of multiple formats and languages. It should provide customization for various tasks such as image recognition, computer vision, audio generation, video generation, and[speech recognition](https://www.g2.com/glossary/speech-recognition-definition). Labeling unstructured data should be open to anyone who authenticates their identity through multi-factor authentication and is an authorized user.
- **Collaboration and workforce management features:** The data labeling platform needs to be activated for model prototype and version control. It should have features like role-based access control, data privacy and security guidelines, user authentication, model collaboration, and ML code supervision. The platform should be accessible to respective team members so they can double-check the labeled tasks and stop the model from hallucinating at any stage of the training data pipeline.
- **Quality assurance and review mechanisms:** When a model's output accuracy depends on the quality of training data, it is evident that data labeling platforms need to be set of modulation accuracy, quality control, and labeling review mechanisms. Given the models might inaccurately label datasets or predict wrong values, the labels need to be further supervised by a human in the loop service or external human oracle.
- **Scalability, automation, and cost efficiency:** As labeling needs grow, ML engineers and developers need to invest in a scalable and cost-efficient data labeling solution that doesn't obstruct their network infrastructure and database architecture. The final implementation step is to ensure that the controls are set, the license is active, and the platform is retrieving and labeling data typically.

### Data Labeling Software Trends

Overall, these trends reflect the growing importance of data labeling in the machine learning and AI ecosystem and the need for tools and technologies to help organizations create and manage large datasets of labeled data efficiently and effectively. There are several trends surrounding data labeling software that are worth noting:

- **Increased adoption of artificial intelligence (AI) and machine learning (ML)**: One key trend in data labeling software is the increasing adoption of AI and ML technologies. Many software solutions now incorporate AI and machine learning algorithms to automate and streamline the data labeling process, improving efficiency and accuracy. As with general AI software,[](https://www.g2.com/articles/ai-trends-2023)[G2 expects this software to get cheaper](https://www.g2.com/articles/ai-trends-2023).
- **Growing demand for high-quality labeled data** : Another trend is the growing demand for high-quality labeled data to train and test machine learning models. Data labeling software can help organizations create and manage large datasets of labeled data, improving the quality and reliability of machine learning models.
- **Focus on user experience and collaboration** : Another trend in data labeling software is a focus on user experience and collaboration. Many data labeling software solutions now offer intuitive and user-friendly interfaces, tools, and features that facilitate collaboration and teamwork.

### Data Labeling FAQs

### Most Popular FAQs

#### Which data labeling software has the best reviews?

Across the Data Labeling category, where ML engineers make up a notable share of the most trusted reviews, the strongest ratings tend to go to platforms that make the repetitive work of annotation feel fast and organized rather than tedious.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Carries a large review base at a high rating, with users pointing to frequent updates and a genuinely friendly interface for building computer vision datasets.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Supports essentially every data type, with strong tools for splitting images and organizing projects across a team.
- [Encord](https://www.g2.com/products/encord/reviews): A newer name in this data set, though its S3 integration and customer success team come up repeatedly as reasons teams building serious infrastructure choose it.

#### What are the best automated data labeling tools?

Automation here usually means pre-labeling data with a model first, so a human is verifying and correcting rather than tagging everything from a blank slate.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Its data augmentation and AI labeling features come up often as a reason it saves real time over manual tagging.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Handles auto-labeling across a wide range of data types, which keeps a project's setup and dataset preparation in one place.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): A smaller sample on this specific theme, but its model-assisted annotation loop runs YOLO and SAM2 pre-annotation directly, turning most of the work into verifying rather than labeling from scratch.

#### Which data labeling software enables consistent, high-quality annotations across multiple image formats and datasets?

Consistency at this level usually comes down to how well a platform organizes projects and enforces the same workflow across every dataset a team touches.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Lets teams export and import datasets with annotations intact, keeping formats consistent as data moves between training runs.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its ability to organize projects across data types is what keeps annotation quality consistent at scale.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller sample so far, but its no-code Task Builder and pre-built quality checks are designed specifically to keep labeling consistent across any data type.

#### Which data labeling software offers user-friendly interfaces and shortcut tools for efficient image annotation and tagging?

The platforms reviewers single out here are the ones where annotating feels fast because of the interface itself, not just the underlying model.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): This is the single most-mentioned strength in its reviews — a clean, intuitive interface that keeps annotation moving quickly.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Described as intuitive and easy to use, with frequent updates that keep the tagging workflow current.
- [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews): A smaller sample on this specific theme, but its visualization module lets a team spot labeling discrepancies and edge cases at a glance rather than digging through data manually.

#### What is the best data labeling software?

The right answer depends on the type of data and team size, but a few platforms come up across a wide range of annotation projects.

- [Kili](https://www.g2.com/products/kili/reviews): A smaller footprint in this data set, but simple cloud storage integration and scalability make it work well for growing teams.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Intuitive enough to find what's needed quickly, especially compared to a self-built annotation tool.
- [Labelbox](https://www.g2.com/products/labelbox/reviews): A newer name here, though its professional, uncluttered interface comes up as a reason it's simple and effective to work in day to day.

#### Which data labeling tools maintain stable performance and annotation quality for high-volume image batches?

Volume tends to be where a platform's design choices show up the most, the same interface that feels snappy on a small project can slow down once a team is running thousands of high-resolution images through it, so reviewers' experience at scale is the most useful signal here.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Reviewers describe performance as "consistently fast, very intuitive, and reliable, even when handling large datasets or heavier annotation workloads," though a few note that pagination can interrupt the review flow once a dataset gets very large.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Reviewers specifically call out its keyboard shortcuts as "an absolute lifesaver when working on large batches," while also noting the platform can slow down when loading very large or high-resolution datasets — a tradeoff worth weighing against its annotation depth.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Its model-assisted pre-annotation loop reduces how much of a large batch needs manual tagging in the first place, which is one way to keep quality consistent as volume grows rather than relying on raw interface speed alone.

#### Which data labeling platforms do ML engineers trust most for scalable annotation with intuitive design and responsive support?

Engineers evaluating this category tend to weigh three things together rather than separately: does the interface stay out of the way, does support actually respond, and does the workflow speed up how fast a model can go through another training iteration.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): One reviewer, writing from an engineering perspective, described it as reducing "coordination overhead instead of adding more complexity" and called it a "scalable, well-structured annotation solution" for machine learning projects.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): A research team reported cutting the time from raw imagery to a trained neural network by roughly fivefold, with reviewers separately noting that support has "always been quick and helpful" when needed.
- [Labelbox](https://www.g2.com/products/labelbox/reviews): Reviewers describe a professional, uncluttered interface with "all the necessary tools" to work smoothly, without extra details getting in the way of day-to-day annotation work.

### Small Business FAQs

#### What is the most affordable data labeling software for SMBs?

Within the[](https://www.g2.com/categories/data-labeling/small-business)[small business segment of Data Labeling](https://www.g2.com/categories/data-labeling/small-business), the platforms that come up most often are the ones with a genuine free tier rather than just a limited trial.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Offers a free entry tier, which fits its large base of solo founders and researchers using it for computer vision projects.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): A team running a small operation can move straight to verifying and correcting pre-annotated data rather than paying for labeling from scratch.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Reasonable task rates and convenient payment methods come up in reviews as reasons it works for teams watching costs closely.

#### What is the best data labeling software for startups?

Startups evaluating[](https://www.g2.com/categories/data-labeling/small-business)[small business data labeling](https://www.g2.com/categories/data-labeling/small-business) tools tend to prioritize a platform a lean team can run without a dedicated annotation specialist.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): A large share of its reviewers are founders and researchers, and its friendly, frequently updated interface fits a small team without a dedicated ML infrastructure person.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Handles every data type in one place, which cuts down on stitching together separate tools as a startup's dataset needs grow.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller sample so far, but its no-code Task Builder lets a startup manage labeling projects at scale without writing custom tooling.

#### Which data labeling platform is the most user-friendly for startups?

Ease of use matters most at this stage, since the person labeling data is often the same person building and training the model.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its clean, intuitive interface is the most consistently praised feature across reviews from smaller teams.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Easy to understand with a friendly UI, which shortens the ramp-up for a small team's first project.
- [Datature](https://www.g2.com/products/datature/reviews): A newer name in this data set, though it's described as extremely user-friendly for both annotation and model training.

#### Which data labeling tool is easiest to set up for small teams?

Setup speed is one of the more differentiated ratings in this category, and small teams generally do best with a platform that's usable the same day.

- [Labelbox](https://www.g2.com/products/labelbox/reviews): Setup is described as super simple with no issues, with everything accessible right after signing in.
- [Segments.ai](https://www.g2.com/products/segments-ai/reviews): A smaller sample so far, but its all-in-one workflow lets a team label and review data in the same place without stitching tools together.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Frequent updates and a friendly interface keep the path from signup to a first labeled dataset short.

#### Which data labeling tool is best for solo researchers and independent ML practitioners?

A meaningful share of this category's reviewers work alone or on very small teams, and the platforms that fit best tend to double as a full workspace rather than just a labeling tool.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Its user base skews heavily toward founders and researchers working solo, with a free entry tier that matches that reality.
- [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews): Works as a central orchestration layer for an entire computer vision pipeline, useful when one person is running evaluation, visualization, and labeling together.
- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Built for exactly this scenario — a small operation running model-assisted pre-annotation so one person can focus on verifying rather than labeling from scratch.

### Enterprise FAQs

#### What is best-rated data labeling software for large enterprises?

Within the[](https://www.g2.com/categories/data-labeling/enterprise)[Enterprise segment of Data Labeling](https://www.g2.com/categories/data-labeling/enterprise), a smaller set of platforms have the review volume from large organizations to back up a strong rating.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Holds up well at the enterprise level, with the same broad data-type support and project organization that smaller teams rely on scaling cleanly.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): The same frequent updates and friendly interface that made it popular with smaller teams carry over at enterprise scale.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): A smaller footprint at this scale so far, but its custom workflows and pre-built quality checks are built to optimize labeling budgets as volume grows.

#### What is the most reliable data labeling tool for enterprises?

Reliability at this scale tends to come down to support responsiveness, since large teams need fast answers when an annotation pipeline stalls.

- [Roboflow](https://www.g2.com/products/roboflow/reviews): Consistent support ratings back up its reputation for frequent updates and a stable, well-maintained platform.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Support quality holds up even as reviewers' organizations get larger and more projects get added.
- [Encord](https://www.g2.com/products/encord/reviews): A smaller sample so far, but its customer success team is described as coming to every meeting prepared and actively building features based on customer feedback.

#### What is best-reviewed data labeling software for enterprise-scale annotation consistency?

Enterprise deployments need annotation quality to hold steady across many projects and contributors, not just within a single dataset.

- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its project organization tools are what let large teams keep annotation standards consistent across many datasets at once.
- [Roboflow](https://www.g2.com/products/roboflow/reviews): Consistent dataset export and import keeps annotation formats stable as a large organization scales up its computer vision work.
- [Kili](https://www.g2.com/products/kili/reviews): A smaller footprint at enterprise scale so far, but its cloud storage integrations and scalability are what growing organizations lean on most.

#### Which data labeling platform offers the strongest quality assurance and model-assisted annotation for enterprise teams?

At enterprise volume, quality assurance needs to be built into the workflow itself rather than handled through manual spot-checks after the fact.

- [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews): Its model-assisted annotation loop runs pre-annotation directly through the platform, turning most enterprise labeling work into verification rather than labeling from scratch.
- [Taskmonk](https://www.g2.com/products/taskmonk/reviews): Pre-built quality checks and custom workflows are designed specifically to enhance data quality at the volume large teams operate at.
- [SuperAnnotate](https://www.g2.com/products/superannotate/reviews): Its ability to organize projects and split data across large teams supports the kind of structured QA process enterprise labeling needs.

#### Which data labeling platform is best for large-scale crowdsourced annotation workforces?

Some enterprise labeling needs go beyond software alone and require an actual workforce of annotators, which changes what "best" means for this question.

- [Appen](https://www.g2.com/products/appen/reviews): A robust data collection and crowdsourcing platform built specifically for AI, language, and similar large-scale annotation work.
- [Outlier AI](https://www.g2.com/products/outlier-ai-outlier-ai/reviews): Offers a flexible range of real AI project work, from writing prompts to reviewing responses, which enterprise teams tap into for large annotation volumes.
- [Keymakr](https://www.g2.com/products/keymakr/reviews): A smaller sample so far, but its team is described as fast, responsive, and proactive about finding solutions when annotation issues come up.

_Researched and written by_ [_Matthew Miller_](https://learn.g2.com/author/matthew-miller)