# Best Data Labeling Software

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

**Total Products under this Category:** 121

### Category Stats (Aug 2026)

- **Average Rating:** 4.49/5 (↓0.01 vs Jul 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product:** FiftyOne (+1.01%) - Among all products in this category, FiftyOne recorded the largest rating increase compared to last month

_Last updated: August 19, 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
- 121+ 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=128515&focus%5B%5D=125020&focus%5B%5D=168222&focus%5B%5D=78925&focus%5B%5D=87452&focus%5B%5D=126287&focus%5B%5D=125450&focus%5B%5D=142739)

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

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

**Sponsored**

### Amazon SageMaker

Amazon SageMaker is a fully managed service that enables data scientists and developers to build, train, and deploy machine learning (ML) models at scale. It provides a comprehensive suite of tools and infrastructure, streamlining the entire ML workflow from data preparation to model deployment. With SageMaker, users can quickly connect to training data, select and optimize algorithms, and deploy models in a secure and scalable environment. Key Features and Functionality: - Integrated Development Environments (IDEs): SageMaker offers a unified, web-based interface with built-in IDEs, including JupyterLab and RStudio, facilitating seamless development and collaboration. - Pre-built Algorithms and Frameworks: It includes a selection of optimized ML algorithms and supports popular frameworks like TensorFlow, PyTorch, and Apache MXNet, allowing flexibility in model development. - Automated Model Tuning: SageMaker can automatically tune models to achieve optimal accuracy, reducing the time and effort required for manual adjustments. - Scalable Training and Deployment: The service manages the underlying infrastructure, enabling efficient training of models on large datasets and deploying them across auto-scaling clusters for high availability. - MLOps and Governance: SageMaker provides tools for monitoring, debugging, and managing ML models, ensuring robust operations and compliance with enterprise security standards. Primary Value and Problem Solved: Amazon SageMaker addresses the complexity and resource-intensive nature of developing and deploying ML models. By offering a fully managed environment with integrated tools and scalable infrastructure, it accelerates the ML lifecycle, reduces operational overhead, and enables organizations to derive insights and value from their data more efficiently. This empowers businesses to innovate rapidly and implement AI solutions without the need for extensive in-house expertise or infrastructure management.

[Visit website](https://www.g2.com/external_clickthroughs/record?secure%5Bad_program%5D=ppc&secure%5Bad_slot%5D=category_product_list_llm&secure%5Bcategory_id%5D=2361&secure%5Bchosen_at%5D=2026-08-20T20%3A12%3A38Z&secure%5Bdisplayable_resource_id%5D=2361&secure%5Bdisplayable_resource_type%5D=Category&secure%5Bmedium%5D=sponsored&secure%5Bplacement_reason%5D=page_category&secure%5Bplacement_resource_ids%5D%5B%5D=2361&secure%5Bprioritized%5D=false&secure%5Bproduct_id%5D=52115&secure%5Bresource_id%5D=2361&secure%5Bresource_type%5D=Category&secure%5Bsource_type%5D=category_page&secure%5Bsource_url%5D=https%3A%2F%2Fwww.g2.com%2Fcategories%2Fdata-labeling&secure%5Btoken%5D=909d90265fb9211ebb6e1b2b4da61c6849a96c57642618763f582f473bfe4497&secure%5Burl%5D=https%3A%2F%2Faws.amazon.com%2Fsagemaker%2F%3Ftrk%3De054ba95-b51d-4594-98bc-aa0239b1797a%26sc_channel%3Ddisplay%2Bads&secure%5Burl_type%5D=custom_url)

### [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:** 354

#### 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: 8.9/10)
- **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 enjoy the **intuitive interface** of SuperAnnotate, which simplifies large-scale annotation projects and boosts collaboration.
- Users enjoy the **user-friendly interface** of SuperAnnotate, facilitating efficient and accurate annotations with powerful tools.
- Users praise the **annotation efficiency** of SuperAnnotate, appreciating its time-saving features and user-friendly interface.
- Users highlight the **efficiency** of SuperAnnotate, enabling quick, high-quality annotations with user-friendly tools and collaboration features.
- Users value the **high-quality annotations** provided by SuperAnnotate, enhancing efficiency and ensuring consistency across projects.

##### Cons

- Users have noted **performance issues** with SuperAnnotate, including slow loading times for large projects and technical glitches.
- Users often face **slow performance** , experiencing lag and hanging when cropping images and labeling tasks on SuperAnnotate.
- Users find the **difficult learning curve** challenging, particularly with advanced features and large datasets requiring time to master.
- Users find the **complexity for new users** of SuperAnnotate challenging, especially with advanced tools and features.
- Users find the **lack of guidance** challenging, making the learning curve steep for new users of SuperAnnotate.

#### 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)

**["Streamlined Data Annotation with Ease"](https://www.g2.com/survey_responses/superannotate-review-13215367)**

**Rating:** 5.0/5.0 stars

_— EMAN S._

[Read full review](https://www.g2.com/survey_responses/superannotate-review-13215367)

#### 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:** 157

#### 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: 8.9/10)
- **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)
- **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)  
144 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 **ease of use** of Roboflow, enabling efficient model training and collaboration with a user-friendly interface.
- Users highlight Roboflow's **efficiency** in dataset management, streamlining tasks and significantly saving time and reducing errors.
- Users value the **annotation efficiency** of Roboflow, enjoying time savings and reduced errors in dataset management.
- Users love how Roboflow's **data labeling** simplifies collaboration, annotation, and export processes, saving time and reducing errors.
- Users appreciate the **powerful and versatile features** of Roboflow, making it ideal for academic and large-scale projects.

##### Cons

- Users find the **cost prohibitive** for advanced features, especially students needing budget-friendly options.
- Users note the **limited features** of Roboflow, as some advanced options require higher-tier plans and constraints exist.
- Users experience **limited functionality** in Roboflow, particularly with advanced features and flexibility for complex tasks.
- Users find **annotation issues** with Roboflow, especially in auto-labeling and polygon marking for complex images.
- Users find **inefficient labeling** processes cumbersome, especially in team environments with a lack of automation and shortcuts.

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

**["Roboflow Accelerated Our Computer Vision POC with Easy Dataset and Model Iteration"](https://www.g2.com/survey_responses/roboflow-review-13277088)**

**Rating:** 4.0/5.0 stars

_— Verified User in Business Supplies and Equipment_

[Read full review](https://www.g2.com/survey_responses/roboflow-review-13277088)

**["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: 8.9/10)
- **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)  
205 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 praise Encord for its **responsive customer support** , ensuring quick solutions and seamless collaboration during projects.
- Users commend Encord for its **annotation efficiency** , appreciating the smooth workflow and intuitive interface that enhances productivity.
- Users value the **intuitive interface and powerful AI features** of Encord, enhancing their data annotation efficiency significantly.
- Users value the **efficiency** of Encord, experiencing smooth workflows and quick data integration that accelerates their processes.
- Users value the **intuitive interface** and comprehensive features of Encord, enhancing efficiency in data curation and annotation.

##### Cons

- Users find that **custom workflows can be challenging** , but support from the team helps mitigate the difficulties.
- Users find it challenging to keep up with **frequent updates** from Encord, despite support from their customer success team.
- Users find it challenging to keep up with **best practices** due to frequent feature updates from Encord.

#### 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)

### [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: 8.9/10)
- **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)

## 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.

### [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: 8.9/10)
- **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 find Labelbox extremely **easy to use** , simplifying project management and enhancing productivity for data training.
- Users appreciate the **easy and efficient data labeling** process of Labelbox, enhancing workflow and model accuracy.
- Users value the **efficiency** of Labelbox, appreciating its smooth setup and seamless project management capabilities.
- Users appreciate the **AI capabilities** of Labelbox, simplifying data labeling and enhancing model accuracy effortlessly.
- Users find **easy integrations** in Labelbox beneficial, enabling a seamless setup and user-friendly experience for all.

##### Cons

- Users express frustration over the **lack of features** , citing limited task claims and minimal customization options.
- Users report **slow performance** when handling large datasets, impacting efficiency and user experience significantly.
- Users find Labelbox to have a **difficult learning curve** due to its complexity and slower processing with large data sets.
- Users find the **high cost** daunting, especially small scale users who may struggle with affordability despite great features.
- Users experience **slow processing** with Labelbox, leading to long waits for project initiation and completion.

#### 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)

### [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: 8.9/10)
- **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 admire the **ease of use** of V7 Darwin, finding it intuitive and efficient for managing HR tasks.
- Users value the **annotation efficiency** of V7 Darwin, streamlining repetitive tasks and enhancing accuracy in processes.
- Users praise the **easy-to-use annotation tools** of V7 Darwin, enhancing project efficiency and quality control.
- Users commend the **intuitive UI and comprehensive functionalities** of V7 Darwin, enhancing HR management and efficiency.
- Users appreciate the **efficiency** of V7 Darwin, which simplifies tasks and enhances productivity in HR management.

##### Cons

- Users find the **lack of features** in V7 Darwin, such as file manipulation and intuitive navigation, disappointing.
- Users desire **missing features** in V7 Darwin, such as polygon manipulation and improved export options for datasets.
- Users find the **limited features** of V7 Darwin frustrating, particularly in annotations and dataset management capabilities.
- Users experience **annotation issues** , including inability to retract submissions and lacking shortcuts for approval.
- Users find the **difficult navigation** of V7 Darwin challenging, requiring time to adapt to its complex interface.

#### 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)

### [Datasaur](https://www.g2.com/products/datasaur/reviews)

Datasaur offers the most intuitive interface for all your Natural Language Processing related tasks.

**Average Rating:** 4.4/5.0

**Total Reviews:** 68

#### How Do G2 Users Rate Datasaur?

- **Labeler Quality:** 8.9/10 (Category avg: 8.9/10)
- **Object Detection:** 8.3/10 (Category avg: 8.9/10)
- **Data Types:** 8.5/10 (Category avg: 8.9/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.8/10)

#### Who Is the Company Behind Datasaur?

- **Seller:** [Datasaur](https://www.g2.com/sellers/datasaur)
- **Year Founded:** 2019
- **HQ Location:** San Francisco Bay Area, California
- **Twitter:** @datasaurai  
261 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=07e24f5105db75a643ab99013e56cec5bb628429f73712ae498f72c63729a68a&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fdatasaur%2F&secure%5Burl_type%5D=linkedin_company_website)  
67 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 37% Small, 34% Medium

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

**["Datasaur Delivers Strong Data Privacy and Secure, Flexible Deployment"](https://www.g2.com/survey_responses/datasaur-review-13227124)**

**Rating:** 4.5/5.0 stars

_— Vaishnavi D._

[Read full review](https://www.g2.com/survey_responses/datasaur-review-13227124)

**["Purpose-Built for Complex NLP: Fast NER, Span Labeling, and LLM Benchmarking"](https://www.g2.com/survey_responses/datasaur-review-13320529)**

**Rating:** 5.0/5.0 stars

_— Mukesh D._

[Read full review](https://www.g2.com/survey_responses/datasaur-review-13320529)

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

- [What is Datasaur used for?](https://www.g2.com/discussions/what-is-datasaur-used-for) - 1 comment, 1 upvote

### [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: 8.9/10)
- **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)  
4,349 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 appreciate the **amazing data preprocessing and enrichment features** of Sama, enhancing their analytics experience.
- Users value the **24/7 customer support** services of Sama, enhancing their experience with tutorials and demos.
- Users appreciate the **data cataloging features** of Sama, enhancing data management and providing quick insights.
- Users appreciate the **amazing data pre-processing and enrichment features** of Sama, significantly enhancing their analytics capabilities.
- Users appreciate the **advanced data processing and enrichment features** of Sama, enhancing their analytics capabilities significantly.

##### Cons

- Users find the **complexity** of Sama necessitates extensive training and skilled personnel, complicating its usability.
- Users find the **complex setup** of Sama requires extensive training or skilled personnel for effective operation.
- Users find the product's complexity creates a **lack of training** , necessitating skilled personnel for effective operation.
- Users find that the product demands **extensive training** , making it difficult to operate without skilled personnel.

#### 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)

### [SUPA](https://www.g2.com/products/supa/reviews)

Supercharge your AI with human expertise. SUPA is here to help you streamline your data at any stage: collection, curation, annotation, model validation and human feedback. SUPA is trusted by AI teams to solve their human data needs. Our lightning-fast machine-led labeling platform integrates with our diverse workforce to provide high-quality data at scale, making it the most cost-efficient solution for your AI. Visit us at https://www.supa.so/

**Average Rating:** 4.9/5.0

**Total Reviews:** 11

#### How Do G2 Users Rate SUPA?

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

#### Who Is the Company Behind SUPA?

- **Seller:** [TDCX](https://www.g2.com/sellers/tdcx)
- **HQ Location:** Singapore, SG
- **Twitter:** @tdcxgroup  
1,054 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=f51abb55ad18cb507299afe0569be18815a9e2cb3a40380ff7f421113492cbb7&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Ftdcxgroup%2F&secure%5Burl_type%5D=linkedin_company_website)  
15,664 employees on LinkedIn®
- **Ownership:** NYSE: TDCX

#### Who Uses This Product?

- **Company Size:** 45% Medium, 45% Small

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

**["Reliable and a great choice for data labelling"](https://www.g2.com/survey_responses/supa-review-9744081)**

**Rating:** 5.0/5.0 stars

_— Verified User in Design_

[Read full review](https://www.g2.com/survey_responses/supa-review-9744081)

**["Head Of Data"](https://www.g2.com/survey_responses/supa-review-8870147)**

**Rating:** 5.0/5.0 stars

_— Dominic C._

[Read full review](https://www.g2.com/survey_responses/supa-review-8870147)

### [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: 8.9/10)
- **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 appreciate the **helpful customer support** of Outlier AI, enhancing their overall experience and satisfaction.
- Users value the **data accuracy** of Outlier AI, appreciating its commitment to precise and thoughtful responses.
- Users appreciate the **flexible and remote project opportunities** provided by Outlier AI for skill-based freelancing.
- Users appreciate the **transparent and fast payment system** of Outlier AI, enhancing their overall satisfaction with the platform.
- Users praise the **fast response speed** of Outlier AI, enhancing their overall user experience and satisfaction.

##### Cons

- Users experience frustrating **work interruptions** due to inconsistent project availability and poor communication from management.
- Users report **buggy performance** that causes unexpected project removals and fluctuating work availability, impacting their overall experience.
- Users express frustration with **low compensation** and inconsistent project availability, hindering their overall experience with Outlier AI.
- Users find the **performance issues** of Outlier AI hinder the overall user experience and usability of the platform.
- Users frequently face **poor customer support** , leading to confusion and frustration with abrupt project changes and management communication.

#### 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)

### [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: 8.9/10)
- **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 highlight the **ease of use** of Taskmonk, praising its intuitive interface and quick onboarding for beginners.
- Users commend the **exceptional customer support** of Taskmonk, highlighting their responsiveness and commitment to user success.
- Users commend Taskmonk for its **efficiency** , enabling quick onboarding and seamless handling of high-volume data annotation projects.
- Users commend Taskmonk for its **intuitive interface and robust features** , making high-volume data labeling effortless and efficient.
- Users highlight the **quick setup and onboarding** of Taskmonk, making it easy to start using the platform swiftly.

##### Cons

- Users find Taskmonk has a **lack of features** , particularly in reporting and user interface flexibility.
- Users find the **difficult learning curve** challenging for beginners, suggesting a need for a more user-friendly interface.
- Users find the **UI complexity** daunting, especially newcomers, due to the overwhelming number of visible options.
- Users experience **occasional technical difficulties** , though prompt support and solutions enhance overall satisfaction with Taskmonk.
- Users face **upload issues** due to delays during peak times and limited local options for LiDAR projects.

#### 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)

### [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: 8.9/10)
- **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's **excellent customer support** and responsiveness, enhancing their overall experience with the software.
- Users value the **high-quality annotations** from Keymakr, appreciating their accuracy and attention to detail on various images.
- Users value Keymakr's **efficient workflow integration** , simplifying processes and enhancing overall productivity significantly.
- Users commend Keymakr for its **annotation efficiency** , appreciating quick services and streamlined workflows that enhance productivity.
- Users appreciate Keymakr's **exceptional customer service** , highlighting their proactive approach and fast, accurate responses to issues.

##### Cons

- Users report **recurring annotation mistakes** and difficulties in exporting COCO format labels, though issues were eventually resolved.
- Users find the **difficult setup** challenging, with initial adjustments and confusing UI navigation causing frustration.
- Users find the **UI navigation convoluted** , making the initial setup and connections, like to S3, challenging.
- Users find the **limited customization** options in Keymakr frustrating, particularly regarding password changes.

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

**["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)

**["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)

### [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: 8.9/10)
- **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 and engaging diversity** of tasks on Appen, enhancing their work experience and enjoyment.
- Users appreciate the **ease of use** of Appen, allowing task completion conveniently via their cell phones.
- Users appreciate the **flexibility** of Appen, allowing them to engage in diverse and interesting projects.

##### Cons

- Users experience frequent **work interruptions** due to inconsistent project availability and navigation issues, affecting income reliability.
- Users find the **low compensation** and inconsistent work availability makes Appen unreliable for a steady income.
- Users find the **navigation confusing** and experience frequent logouts, impacting their usability of Appen.
- Users often experience **connectivity issues** with Appen, leading to confusion and frequent disconnections.
- Users find the **navigation confusing** and report frequent disconnections, impacting their overall experience with Appen.

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

**["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)

**["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)

### [Clarifai](https://www.g2.com/products/clarifai/reviews)

Clarifai is a leader in AI orchestration and development, helping organizations, teams, and developers build, deploy, orchestrate, and operationalize AI at scale. Clarifai’s cutting-edge AI workflow orchestration platform leverages today's modern AI technologies like Large Language Models (LLMs), Large Vision Models (LVMs), and Retrieval Augmented Generation (RAG), data labeling, inference, and more, and is available in cloud, on-premises, or hybrid environments. Founded in 2013, Clarifai has been used to build more than 1.5 million AI models with more than 400,000 users in 170 countries. Learn more at www.clarifai.com.

**Average Rating:** 4.3/5.0

**Total Reviews:** 74

#### How Do G2 Users Rate Clarifai?

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

#### Who Is the Company Behind Clarifai?

- **Seller:** [Clarifai](https://www.g2.com/sellers/clarifai)
- **Year Founded:** 2013
- **HQ Location:** Wilmington, Delaware
- **Twitter:** @clarifai  
10,922 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=8e96cd967f3d17ebfc636080f07d5b87c9155366576feb1d18188684d774aa32&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2F10064814%2F&secure%5Burl_type%5D=linkedin_company_website)  
49 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 60% Small, 29% Medium

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

_AI-generated summary from verified user reviews_

##### Pros

- Users appreciate Clarifai for its **impressive models and flexibility** , enhancing image and video recognition effectively.
- Users value the **easy AI tools** of Clarifai, enabling fast and accurate image and video recognition effortlessly.
- Users value the **model variety** in Clarifai, enabling tailored solutions with impressive flexibility and ease of integration.
- Users appreciate the **cutting-edge AI integration** of Clarifai, enabling accurate image and video recognition through customizable models.
- Users appreciate the **cutting-edge AI capabilities** of Clarifai, especially for image and video recognition tasks.

##### Cons

- Users find the platform **expensive** for small-scale developers, as costs can accumulate quickly with usage.
- Users find the **complexity of setup and documentation** challenging, especially for newcomers to the platform.
- Users find the **learning curve steep** for Clarifai, particularly for those new to machine learning platforms.
- Users face a **lack of resources** , hindering accessibility for small-scale developers and non-profits seeking affordable options.
- Users often find the **poor documentation** of Clarifai lacking detail, hindering their ability to maximize its features.

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

**["Clarifai’s Flexible Model Workflows and Powerful Multimodal Platform"](https://www.g2.com/survey_responses/clarifai-review-13279085)**

**Rating:** 4.5/5.0 stars

_— Subhashree S._

[Read full review](https://www.g2.com/survey_responses/clarifai-review-13279085)

**["Clarifai Streamlines Multimodal AI Development with a Powerful All-in-One Platform"](https://www.g2.com/survey_responses/clarifai-review-13291150)**

**Rating:** 4.5/5.0 stars

_— Atharva S._

[Read full review](https://www.g2.com/survey_responses/clarifai-review-13291150)

### [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews)

FiftyOne by Voxel51 is the leading data platform for physical AI. Without the right data, even the smartest AI models fail. FiftyOne gives machine learning engineers the power to deeply understand and evaluate their visual datasets—across images, videos, 3D point clouds, geospatial, and medical data. With over 2.8 million open source installs and customers like Walmart, GM, Bosch, Medtronic, and the University of Michigan Health, FiftyOne is an indispensable tool for building computer vision systems that work in the real world, not just in the lab. FiftyOne, combines open-source flexibility with enterprise-grade capabilities to help teams understand and analyze their multimodal data, annotate the right samples, close quality and coverage gaps, and build models that perform reliably in the real world. Proven impact with FiftyOne: ⬆️30% increase in model accuracy ⏱️5+ months of development time saved 📈30% boost in team productivity Learn more about FiftyOne: ✏️Annotation: Adopt smart data selection techniques with auto-labeling and manual workflows to curate first and prioritize the most valuable data to label. 🔍Data Curation and Management: Explore and curate your datasets with precision. Get insights into distribution, diversity, coverage, and more to optimize AI performance. Analyze billions of samples, hosted securely on your infrastructure, whether in the cloud or on-premise. 📊Model Evaluation: Quickly identify what’s driving model failures or successes. From aggregate performance metrics to sample-level diagnostics, diagnose failure modes and edge cases preventing your models from reaching optimal performance in production. At Voxel51, we empower hundreds of thousands of ML engineers around the world to unlock data insights to maximize model performance.

**Average Rating:** 4.5/5.0

**Total Reviews:** 25

#### How Do G2 Users Rate FiftyOne?

- **Ease of Use:** 8.4/10 (Category avg: 8.8/10)

#### Who Is the Company Behind FiftyOne?

- **Seller:** [Voxel51](https://www.g2.com/sellers/voxel51)
- **Year Founded:** 2018
- **HQ Location:** Ann Arbor, US
- **Twitter:** @Voxel51  
1,624 Twitter followers
- **LinkedIn® Page:** [www.linkedin.com](https://www.g2.com/external_clickthroughs/record?secure%5Bsource_type%5D=product_profile&secure%5Btoken%5D=134956ddf898eacf9ea1c4cb96fac4f9f8b538f1d2309a2a1a7da036e8a2aeac&secure%5Burl%5D=https%3A%2F%2Fwww.linkedin.com%2Fcompany%2Fvoxel51&secure%5Burl_type%5D=linkedin_company_website)  
63 employees on LinkedIn®

#### Who Uses This Product?

- **Top Industries:** Computer Software
- **Company Size:** 68% Small, 24% Medium

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

**["Accessible, Intuitive UI That Makes Dataset Exploration Effortless"](https://www.g2.com/survey_responses/fiftyone-review-13222110)**

**Rating:** 5.0/5.0 stars

_— Sems K._

[Read full review](https://www.g2.com/survey_responses/fiftyone-review-13222110)

**["Built a working AI coaching plugin on FiftyOne in a hackathon weekend"](https://www.g2.com/survey_responses/fiftyone-review-13227837)**

**Rating:** 5.0/5.0 stars

_— Paramjeet S._

[Read full review](https://www.g2.com/survey_responses/fiftyone-review-13227837)

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 ![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

### Data Labeling Topics

- [What is Data Labeling Software?](#what-is-data-labeling-software)
- [What types of data labeling software exist?](#what-types-of-data-labeling-software-exist)
- [What are the Common Features of Data Labeling Software?](#what-are-the-common-features-of-data-labeling-software)
- [Benefits of Data Labeling Software](#benefits-of-data-labeling-software)
- [Who Uses Data Labeling Software?](#who-uses-data-labeling-software)
- [Alternatives to data labeling software](#alternatives-to-data-labeling-software)
- [Challenges with Data Labeling Software](#challenges-with-data-labeling-software)
- [What companies should buy data labeling software?](#what-companies-should-buy-data-labeling-software)
- [How to Buy Data Labeling Software](#how-to-buy-data-labeling-software)
- [What does data labeling software cost?](#what-does-data-labeling-software-cost)
- [Implementation of data labeling software](#implementation-of-data-labeling-software)
- [Data Labeling Software Trends](#data-labeling-software-trends)
- [Data Labeling FAQs](#data-labeling-faqs)
- [Most Popular FAQs](#most-popular-faqs)
- [Small Business FAQs](#small-business-faqs)
- [Enterprise FAQs](#enterprise-faqs)

[
### Data Labeling Topics
 Expand/Collapse ](#)
- [What is Data Labeling Software?](#what-is-data-labeling-software)
- [What types of data labeling software exist?](#what-types-of-data-labeling-software-exist)
- [What are the Common Features of Data Labeling Software?](#what-are-the-common-features-of-data-labeling-software)
- [Benefits of Data Labeling Software](#benefits-of-data-labeling-software)
- [Who Uses Data Labeling Software?](#who-uses-data-labeling-software)
- [Alternatives to data labeling software](#alternatives-to-data-labeling-software)
- [Challenges with Data Labeling Software](#challenges-with-data-labeling-software)
- [What companies should buy data labeling software?](#what-companies-should-buy-data-labeling-software)
- [How to Buy Data Labeling Software](#how-to-buy-data-labeling-software)
- [What does data labeling software cost?](#what-does-data-labeling-software-cost)
- [Implementation of data labeling software](#implementation-of-data-labeling-software)
- [Data Labeling Software Trends](#data-labeling-software-trends)
- [Data Labeling FAQs](#data-labeling-faqs)
- [Most Popular FAQs](#most-popular-faqs)
- [Small Business FAQs](#small-business-faqs)
- [Enterprise FAQs](#enterprise-faqs)

## Learn More 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)