# Best Data Labeling Software

*By [Bijou Barry](https://research.g2.com/insights/author/bijou-barry)*


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.






## How Many Data Labeling Software Products Does G2 Track?
**Total Products under this Category:** 106

### Category Stats (Jun 2026)
- **Average Rating**: 4.52/5 The average rating of products in this category, based on all submitted ratings
- **Top Trending Product**: Roboflow (+0.04%) - Among all products in this category, Roboflow recorded the largest rating increase compared to last month
*Last updated: June 18, 2026*


## How Does G2 Rank Data Labeling Software Products?

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

- 30 Analysts and Data Experts
- 1,700+ Authentic Reviews
- 106+ 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.


## Which Data Labeling Software Is Best for Your Use Case?

- **Leader:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)
- **Highest Performer:** [Datature](https://www.g2.com/products/datature/reviews)
- **Easiest to Use:** [Roboflow](https://www.g2.com/products/roboflow/reviews)
- **Top Trending:** [Encord](https://www.g2.com/products/encord/reviews)
- **Best Free Software:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)


---

**Sponsored**

### SuperAnnotate

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.



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---

## What Are the Top-Rated Data Labeling Software Products in 2026?
### 1. [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:** 352
**How Do G2 Users Rate SuperAnnotate?**

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

**Who Is the Company Behind SuperAnnotate?**

- **Seller:** [SuperAnnotate](https://www.g2.com/sellers/superannotate)
- **Company Website:** https://superannotate.com/
- **Year Founded:** 2018
- **HQ Location:** San Francisco, CA
- **Twitter:** @superannotate (720 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/18999422/ (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-Business, 23% Mid-Market


#### What Are SuperAnnotate's Pros and Cons?

**Pros:**

- Ease of Use (95 reviews)
- User Interface (60 reviews)
- Annotation Efficiency (48 reviews)
- Efficiency (45 reviews)
- Quality (36 reviews)

**Cons:**

- Performance Issues (21 reviews)
- Slow Performance (19 reviews)
- Difficult Learning (18 reviews)
- Complexity (15 reviews)
- Lack of Guidance (13 reviews)


### What Do G2 Reviewers Say About SuperAnnotate?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **intuitive interface** of SuperAnnotate, which streamlines workflow and enhances productivity in annotation projects.
- Users appreciate the **user-friendly interface** of SuperAnnotate, enhancing efficiency and collaboration in their annotation tasks.
- Users appreciate the **annotation efficiency** of SuperAnnotate, benefiting from its time-saving tools and user-friendly interface.
- Users admire the **efficiency** of SuperAnnotate in streamlining image and linguistic data annotation processes.
- Users value the **high-quality annotations** SuperAnnotate produces, enhancing efficiency and consistency in their machine learning projects.

**Cons:**

- Users encounter **performance issues** with SuperAnnotate, particularly related to slow loading times for large projects.
- Users experience **slow performance** with SuperAnnotate, particularly during image cropping and labeling tasks, affecting efficiency.
- Users face a **difficult learning curve** with advanced features, requiring time to adapt and familiarize themselves.
- Users struggle with the **complexity** of SuperAnnotate, particularly new users facing a steep learning curve.
- Users find the **lack of guidance** challenging, particularly in learning advanced features and workflows in SuperAnnotate.

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

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

**Rating:** 4.0/5.0 stars
*— Nada A.*

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

---

**"[Clean data, friction-free workflows.](https://www.g2.com/survey_responses/superannotate-review-12285413)"**

**Rating:** 5.0/5.0 stars
*— Kevin O.*

[Read full review](https://www.g2.com/survey_responses/superannotate-review-12285413)

---


#### 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

### 2. [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:** 149
**How Do G2 Users Rate Roboflow?**

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

**Who Is the Company Behind Roboflow?**

- **Seller:** [Roboflow](https://www.g2.com/sellers/roboflow)
- **Year Founded:** 2019
- **HQ Location:** Remote, US
- **Twitter:** @roboflow (13,577 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/36096640 (133 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Founder, Researcher
- **Top Industries:** Computer Software, Research
- **Company Size:** 79% Small-Business, 13% Mid-Market


#### What Are Roboflow's Pros and Cons?

**Pros:**

- Ease of Use (69 reviews)
- Efficiency (56 reviews)
- Annotation Efficiency (51 reviews)
- Data Labelling (41 reviews)
- Features (37 reviews)

**Cons:**

- Expensive (24 reviews)
- Lack of Features (23 reviews)
- Limited Functionality (20 reviews)
- Annotation Issues (16 reviews)
- Inefficient Labeling (13 reviews)


### What Do G2 Reviewers Say About Roboflow?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of use** of Roboflow, streamlining dataset preparation and enhancing collaboration in projects.
- Users value the **efficiency** of Roboflow, allowing seamless data management and faster model development.
- Users appreciate the **efficient annotation process** of Roboflow, enabling faster dataset preparation and smoother workflow integration.
- Users appreciate the **streamlined data labelling process** of Roboflow, enhancing efficiency in computer vision development.
- Users appreciate the **end-to-end pipeline** of Roboflow, enhancing efficiency from annotation to model deployment.

**Cons:**

- Users find Roboflow **expensive** , as many essential features require paid plans, limiting access for students and beginners.
- Users find a **lack of features** in Roboflow, including limited model training and constrained customization options.
- Users find the **limited functionality** of Roboflow challenging, particularly with custom scripts and advanced features locked behind paywalls.
- Users note **annotation issues** with Roboflow, particularly for complex labeling and precision in smart polygon segmentation.
- Users criticize the **inefficient labeling** process of Roboflow, finding it overly manual and lacking advanced automation features.

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

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

**Rating:** 5.0/5.0 stars
*— Alexey K.*

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

---

**"[Roboflow: User-Friendly Image Annotation for Training AI Models](https://www.g2.com/survey_responses/roboflow-review-12552590)"**

**Rating:** 5.0/5.0 stars
*— Kim B.*

[Read full review](https://www.g2.com/survey_responses/roboflow-review-12552590)

---



### 3. [Encord](https://www.g2.com/products/encord/reviews)
Encord is the universal data layer for AI. The platform helps AI teams train and run their models with the right data - managing, curating, annotating, and aligning data across the full AI lifecycle. Encord works with over 300 leading AI teams, including Woven by Toyota, Zipline, AXA, and Flock Safety. Confidentially build production AI with rich multimodal data. Encord is SOC 2, AICPA SOC, HIPAA, and GDPR compliant.


**Average Rating:** 4.8/5.0
**Total Reviews:** 65
**How Do G2 Users Rate Encord?**

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

**Who Is the Company Behind Encord?**

- **Seller:** [Encord](https://www.g2.com/sellers/encord)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @encord_team (1,014 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/69557125 (183 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Hospital & Health Care
- **Company Size:** 51% Small-Business, 40% Mid-Market


#### What Are Encord's Pros and Cons?

**Pros:**

- Customer Support (5 reviews)
- Annotation Efficiency (3 reviews)
- Annotation Tools (3 reviews)
- Efficiency (3 reviews)
- Features (3 reviews)

**Cons:**

- Complex Automation (1 reviews)
- Complexity (1 reviews)
- Lack of Guidance (1 reviews)


### What Do G2 Reviewers Say About Encord?
*AI-generated summary from verified user reviews*

**Pros:**

- Users praise Encord's **responsive customer support** , highlighting its effectiveness and adaptability in meeting project needs.
- Users highlight the **annotation efficiency** of Encord, praising its intuitive interface and seamless integration for smooth workflows.
- Users value the **intuitive annotation tools** in Encord, enabling seamless integration and efficient training data curation.
- Users highlight the **efficiency** of Encord, noting seamless integration and a smooth workflow enhancing productivity in their projects.
- Users value the **intuitive interface and seamless integration** of Encord, enhancing their data curation and annotation processes.

**Cons:**

- Users find **custom workflows complex** , but appreciate the helpful support team available to assist them.
- Users find the **complexity of staying current** with Encord's frequent updates challenging, despite helpful support from the team.
- Users find a **lack of guidance** challenging due to frequent feature updates, despite support from the customer success team.

#### 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)

---



### 4. [Labelbox](https://www.g2.com/products/labelbox/reviews)
Labelbox is the leading data-centric AI platform for building intelligent applications. Teams looking to capitalize on the latest advances in generative AI and LLMs use the Labelbox platform to inject these systems with the right degree of human supervision and automation. Whether they are building AI products with custom or foundation models, or using AI to automate data tasks or find business insights, Labelbox enables teams to do so effectively and quickly. The platform is used by Fortune 500 enterprises such as Walmart, P&G, Genentech, and Adobe, and hundreds of leading AI teams. Labelbox is backed by leading investors including SoftBank, Andreessen Horowitz, B Capital, Gradient Ventures (Google's AI-focused fund), and Databricks Ventures.


**Average Rating:** 4.5/5.0
**Total Reviews:** 48
**How Do G2 Users Rate Labelbox?**

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

**Who Is the Company Behind Labelbox?**

- **Seller:** [Labelbox](https://www.g2.com/sellers/labelbox)
- **Year Founded:** 2018
- **HQ Location:** San Francisco, California
- **Twitter:** @labelbox (3,489 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/labelbox/ (469 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 46% Small-Business, 38% Mid-Market


#### What Are Labelbox's Pros and Cons?

**Pros:**

- Ease of Use (9 reviews)
- Data Labeling (6 reviews)
- Efficiency (6 reviews)
- AI Capabilities (5 reviews)
- Easy Integrations (5 reviews)

**Cons:**

- Lack of Features (3 reviews)
- Slow Performance (3 reviews)
- Difficult Learning (2 reviews)
- Expensive (2 reviews)
- Slow Processing (2 reviews)


### What Do G2 Reviewers Say About Labelbox?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use** in Labelbox, benefiting from its intuitive interface and collaborative tools.
- Users value the **clean and intuitive interface** of Labelbox, enhancing their data labeling experience significantly.
- Users value the **efficiency** of Labelbox in managing projects, simplifying training processes, and enhancing team collaboration.
- Users value the **AI capabilities** of Labelbox for simplifying complex tasks and enhancing data labeling efficiency.
- Users find the **easy integrations** of Labelbox beneficial for seamless setup and a friendly user interface.

**Cons:**

- Users are frustrated by the **lack of features** in Labelbox, particularly with limited task allocation and customization options.
- Users report **slow performance** with large datasets, affecting processing speed and output visualization compared to other platforms.
- Users find **difficult learning** curve due to the complexity of tools and slower processing with large datasets.
- Users find the **high cost** of Labelbox concerning, particularly for small-scale users needing budget-friendly options.
- Users express frustration with **slow processing** speeds and limited offline capabilities, delaying project initiation and contributions.

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

**"[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)

---

**"[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)

---


#### 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)

### 5. [Amazon Sagemaker Ground Truth](https://www.g2.com/products/amazon-sagemaker-ground-truth/reviews)
Amazon SageMaker Ground Truth helps you build highly accurate training datasets for machine learning quickly. SageMaker Ground Truth offers easy access to public and private human labelers and provides them with built-in workflows and interfaces for common labeling tasks.


**Average Rating:** 4.1/5.0
**Total Reviews:** 19
**How Do G2 Users Rate Amazon Sagemaker Ground Truth?**

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

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

- **Seller:** [Amazon Web Services (AWS)](https://www.g2.com/sellers/amazon-web-services-aws-3e93cc28-2e9b-4961-b258-c6ce0feec7dd)
- **Year Founded:** 2006
- **HQ Location:** Seattle, WA
- **Twitter:** @awscloud (2,232,483 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 37% Enterprise, 37% Small-Business



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

**"[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)

---

**"[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)

---


#### 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)

### 6. [V7 Darwin](https://www.g2.com/products/v7-darwin/reviews)
V7 Darwin is a specialized AI platform for creating high-quality training data and managing annotation workflows. It is engineered for teams building sophisticated computer vision models and solving complex, domain-specific challenges with AI. V7 Darwin provides a comprehensive suite of tools for data labeling, video annotation, and medical imaging annotation. - Create pixel-perfect image and video annotations with Auto-Annotate and SAM for semantic masks, instance segmentation, keypoints, and polygons. - Develop medical AI with tools for DICOM, NIfTI, and WSI annotation, featuring an interface with MPR, 3D rendering, precise crosshairs, windowing, and oblique views. - Accelerate video annotation by up to 10x with AI-assisted auto-tracking for objects across frames. - Manage long videos, multi-camera views, and nested annotation classes. - Design multi-stage review workflows with conditional logic, consensus, and task assignment for your data labeling pipeline. - Organize, filter, and manage large datasets with custom views and tags, enabling real-time team collaboration for annotators, reviewers, and ML engineers. - Scale your annotation projects with professional data labeling services, including certified annotators and experts in various domains (medical, video, LLMs, scientific). You can seamlessly integrate V7 Darwin with your existing tech stack and import/export annotations with ease. Get complete control over your models, tasks, and datasets through the open API, Darwin-py SDK, and CLI.


**Average Rating:** 4.7/5.0
**Total Reviews:** 55
**How Do G2 Users Rate V7 Darwin?**

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

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

- **Seller:** [V7](https://www.g2.com/sellers/v7)
- **Year Founded:** 2018
- **HQ Location:** London, England
- **Twitter:** @v7labs (3,471 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/v7labs/ (106 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 55% Small-Business, 35% Mid-Market


#### What Are V7 Darwin's Pros and Cons?

**Pros:**

- Ease of Use (10 reviews)
- Annotation Efficiency (8 reviews)
- Annotation Tools (7 reviews)
- Features (6 reviews)
- Efficiency (5 reviews)

**Cons:**

- Lacking Features (5 reviews)
- Missing Features (5 reviews)
- Limited Features (3 reviews)
- Annotation Issues (2 reviews)
- Difficult Navigation (2 reviews)


### What Do G2 Reviewers Say About V7 Darwin?
*AI-generated summary from verified user reviews*

**Pros:**

- Users commend the **ease of use** of V7 Darwin, enjoying its intuitive interface and seamless task automation.
- Users appreciate the **annotation efficiency** of V7 Darwin, significantly enhancing productivity and accuracy in HR workflows.
- Users commend the **user-friendly annotation tools** of V7 Darwin, enhancing project efficiency and quality control.
- Users value the **comprehensive HRMS features** of Darwinbox, enhancing efficiency and accuracy across the employee lifecycle.
- Users praise the **efficiency** of V7 Darwin in automating tasks and streamlining the entire HR process.

**Cons:**

- Users express frustration over the **lack of essential features** in V7 Darwin, hindering navigation and usability.
- Users are disappointed by the **missing features** in V7 Darwin, such as limited polygon manipulation and export options.
- Users express frustration over the **limited features** of V7 Darwin, wishing for more functionality and flexibility.
- Users experience **annotation issues** , including inability to retract submissions and lack of approval shortcuts.
- Users find the **difficult navigation** of V7 Darwin challenging, particularly when trying to access advanced features effectively.

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

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

**Rating:** 4.0/5.0 stars
*— Shiv S.*

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

---

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

**Rating:** 4.5/5.0 stars
*— Jed D.*

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

---


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

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

### 7. [Sama](https://www.g2.com/products/sama/reviews)
Sama is a globally recognized leader in data annotation solutions for enterprise computer vision and generative AI models that require the highest accuracy. As an industry pioneer with 15 years of experience, Sama’s expertise and solutions are trusted by leading companies such as GM, Ford, Continental, Google, and many more. Sama specializes in data annotation services for generative AI, and 2D and 3D image and video (including LiDAR and sensor fusion). We also validate complex machine learning algorithms. As a leader in ethical AI and a Certified B-Corp, we’ve pioneered an impact model that harnesses the power of markets for social good. We have meaningfully improved employment and income outcomes for those with the greatest barriers to formal work (validated by an independent MIT study). So far, we've helped more than 60,000 people lift themselves out of poverty. For more information, visit www.sama.com


**Average Rating:** 4.6/5.0
**Total Reviews:** 11
**How Do G2 Users Rate Sama?**

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

**Who Is the Company Behind Sama?**

- **Seller:** [Sama](https://www.g2.com/sellers/sama)
- **Year Founded:** 2008
- **HQ Location:** San Francisco, US
- **Twitter:** @SamaAI (228,871 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/410136 (4,342 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 55% Small-Business, 36% Enterprise


#### What Are Sama's Pros and Cons?

**Pros:**

- Analytics (1 reviews)
- Customer Support (1 reviews)
- Data Cataloging (1 reviews)
- Data Lineage (1 reviews)
- Data Management (1 reviews)

**Cons:**

- Complexity (1 reviews)
- Complex Setup (1 reviews)
- Lack of Training (1 reviews)
- Training Required (1 reviews)


### What Do G2 Reviewers Say About Sama?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **powerful data preparation and analytics features** of Sama, enhancing efficiency and insight delivery.
- Users appreciate the **24/7 customer support** of Sama, which enhances their overall experience with tutorials and demos.
- Users value the **amazing data preprocessing and enrichment features** of Sama, enhancing their analytics capabilities significantly.
- Users value the **fast insights and data enrichment features** of Sama, enhancing their data preparation experience.
- Users appreciate the **amazing data pre-processing and enrichment features** of Sama, enhancing insights and management capabilities.

**Cons:**

- Users find the product **too complex** , requiring significant training and skilled personnel for effective operation.
- Users find the **complex setup** of Sama challenging, often needing extensive training or skilled personnel for operation.
- Users find the product's **lack of training** challenging, as it demands skilled personnel for effective operation.
- Users find that Sama's **complexity demands extensive training** , making it challenging to operate effectively.

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

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

**Rating:** 4.5/5.0 stars
*— Mohammad A.*

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

---

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

**Rating:** 4.5/5.0 stars
*— Nikita D.*

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

---



### 8. [Outlier AI](https://www.g2.com/products/outlier-ai-outlier-ai/reviews)
Outlier AI is a platform that connects human expertise with artificial intelligence to enhance the accuracy, speed, and reliability of AI models. By engaging a global network of over 100,000 experts across more than 50 countries, Outlier AI has facilitated the development of more knowledgeable and impactful AI systems, distributing over $500 million to its contributors. Key Features and Functionality: - Expert-Driven AI Training: Outlier AI leverages the specialized skills of its global expert community to train and refine AI models, ensuring high-quality data annotation and model development. - Flexible Remote Work Opportunities: The platform offers individuals meaningful and accessible job opportunities, allowing experts to contribute remotely and on their own schedules. - Integration with Scale AI: Powered by Scale AI, Outlier AI combines top-tier data infrastructure with advanced anomaly detection capabilities, enhancing the scalability and accuracy of AI solutions. Primary Value and Problem Solved: Outlier AI addresses the challenge of developing reliable and effective AI models by integrating human expertise into the AI training process. This approach not only improves the quality of AI outputs but also provides flexible employment opportunities to a diverse global workforce. By bridging the gap between human intelligence and artificial intelligence, Outlier AI ensures that AI systems are more accurate, efficient, and aligned with real-world applications.


**Average Rating:** 4.0/5.0
**Total Reviews:** 14
**How Do G2 Users Rate Outlier AI?**

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

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

- **Seller:** [Outlier AI](https://www.g2.com/sellers/outlier-ai)
- **Year Founded:** 2023
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/try-outlier (28,516 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 100% Small-Business


#### What Are Outlier AI's Pros and Cons?

**Pros:**

- Customer Support (1 reviews)
- Data Accuracy (1 reviews)
- Helpful (1 reviews)
- Payment Fairness (1 reviews)
- Response Speed (1 reviews)

**Cons:**

- Work Interruptions (3 reviews)
- Buggy Performance (1 reviews)
- Low Compensation (1 reviews)
- Performance Issues (1 reviews)
- Poor Customer Support (1 reviews)


### 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 with the platform.
- Users appreciate the **data accuracy** of Outlier AI, noting its consistent delivery of reliable responses.
- Users find **flexibility in project work** appealing, allowing them to work remotely on various tasks easily.
- Users value the **transparent and fast payment system** of Outlier AI, enhancing their overall work experience.
- Users appreciate the **fast response speed** of Outlier AI, enhancing their overall experience with efficient support.

**Cons:**

- Users report **work interruptions** due to inconsistent project availability and unclear assessments, leading to frustration and uncertainty.
- Users report **buggy performance** with frequent issues affecting project assessments and availability, causing frustration and confusion.
- Users find the **low compensation** from Outlier AI frustrating, as project availability fluctuates and pay rates can be unreliable.
- Users feel that **performance issues** hinder the speed and smoothness of the Outlier AI experience.
- Users report **poor customer support** characterized by poor communication and unexpected project removals, causing frustration.

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

**"[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)

---

**"[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)

---



### 9. [Appen](https://www.g2.com/products/appen/reviews)
Appen collects and labels images, text, speech, audio, video, and other data to create training data used to build and continuously improve the world’s most innovative artificial intelligence systems. We offer a state of the art, licensable data annotation platform to annotate training data use cases in computer vision and natural language processing. Our platform enhances accuracy and efficiency through our Smart Labeling and Pre-Labeling features which use Machine Learning to ease human annotations. You choose the level of service and security you want for data collection and annotation, from white-glove managed service to flexible self-service. Our expertise includes having a global crowd of over 1 million skilled contractors who speak over 235 languages and dialects, in over 70,000 locations and 170 countries, and the industry’s most advanced AI-assisted data annotation platform. Our reliable training data gives leaders in technology, automotive, financial services, retail, healthcare, and governments the confidence to deploy world-class AI products. Founded in 1996, Appen has customers and offices globally.


**Average Rating:** 4.2/5.0
**Total Reviews:** 33
**How Do G2 Users Rate Appen?**

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

**Who Is the Company Behind Appen?**

- **Seller:** [Appen](https://www.g2.com/sellers/appen)
- **Year Founded:** 1996
- **HQ Location:** Kirkland, Washington, United States
- **LinkedIn® Page:** https://www.linkedin.com/company/appen (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-Business, 26% Enterprise


#### What Are Appen's Pros and Cons?

**Pros:**

- Useful (2 reviews)
- Ease of Use (1 reviews)
- Flexibility (1 reviews)

**Cons:**

- Work Interruptions (3 reviews)
- Low Compensation (2 reviews)
- Complexity (1 reviews)
- Connectivity Issues (1 reviews)
- User Interface Issues (1 reviews)


### What Do G2 Reviewers Say About Appen?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **flexibility and variety** of tasks offered by Appen, enhancing their work experience and enjoyment.
- Users appreciate the **ease of use** of Appen, allowing task completion effortlessly through their cell phones.
- Users appreciate the **flexibility** of Appen, enjoying diverse and interesting projects that enhance their work experience.

**Cons:**

- Users experience **work interruptions** due to inconsistent availability and complicated navigation, affecting their overall reliability on Appen.
- Users highlight **low compensation** due to infrequent payments and inconsistent work availability, affecting overall financial stability.
- Users find the **navigation confusing** and experience frequent logouts, leading to frustration during use.
- Users experience **connectivity issues** , finding navigation confusing and frequently being logged out of the app.
- Users find the **navigation confusing** and report frequent disconnections that hinder their 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)

---



### 10. [Keymakr](https://www.g2.com/products/keymakr/reviews)
We are a data labeling company that focuses on providing high quality annotation services and excellent customer support. We are the best choice for: Image Annotation Video Annotation Data validation Document Annotation Data Creation Data Collection Our company creates best-in-class computer vision training data. We offer an in-house team paired with advanced proprietary annotation tools. Scalable and secure one-stop shop for your AI


**Average Rating:** 4.8/5.0
**Total Reviews:** 45
**How Do G2 Users Rate Keymakr?**

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

**Who Is the Company Behind Keymakr?**

- **Seller:** [Keymakr](https://www.g2.com/sellers/keymakr)
- **Year Founded:** 2015
- **HQ Location:** New York, NY
- **Twitter:** @keymakr_com (354 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/keymakr/ (69 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 52% Small-Business, 22% Mid-Market


#### What Are Keymakr's Pros and Cons?

**Pros:**

- Customer Support (7 reviews)
- Quality (5 reviews)
- Efficiency (4 reviews)
- Annotation Efficiency (3 reviews)
- Helpful (3 reviews)

**Cons:**

- Annotation Issues (3 reviews)
- Difficult Setup (2 reviews)
- Complexity (1 reviews)
- Limited Customization (1 reviews)


### What Do G2 Reviewers Say About Keymakr?
*AI-generated summary from verified user reviews*

**Pros:**

- Users commend Keymakr for their **exceptional customer support** , ensuring smooth and pleasant communication throughout the process.
- Users commend Keymakr for its **high-quality annotations** , praising accuracy that enhances the value of their data.
- Users value Keymakr's **efficiency** in streamlining workflows and enhancing productivity through seamless integration and responsive support.
- Users find Keymakr's **annotation efficiency** remarkable, facilitating quick reviews and enhancing workflow across projects.
- Users commend **Keymakr's exceptional customer service** , highlighting fast responses and proactive problem-solving. Highly recommended!

**Cons:**

- Users face **recurring annotation issues** that can complicate the workflow, but improvements have been noted over time.
- Users report a **difficult setup** process, citing issues with initial adjustments and convoluted UI navigation.
- Users find the **UI navigation convoluted** and face challenges with S3 connectivity, complicating the initial setup.
- Users express frustration over the **limited customization** in Keymakr, notably the inability to change passwords independently.

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

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

**Rating:** 5.0/5.0 stars
*— Yacine B.*

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

---

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

**Rating:** 4.5/5.0 stars
*— Rinat L.*

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

---



### 11. [Taskmonk](https://www.g2.com/products/taskmonk/reviews)
Taskmonk is an all-in-one data labeling platform that empowers businesses to train powerful Enterprise-AI models with ease. You can manage data annotation pipelines, leverage human intelligence, conquer large datasets, and achieve top-tier AI results - all without breaking a sweat on Taskmonk. Taskmonk is built for every stakeholder - from data annotation teams to project managers to AI leads, ensuring top-notch training data with intuitive features that: • Combat massive datasets with low-code/no-code workflows that adapt to your needs in no time. • Amplify human effort with pre-trained models and automation that slash the AHT and improve ROI. • Prioritize data privacy & security, and prevent unauthorized access. 7+ global F500s trust our battle-tested platform with 200M+ tasks labeled and 500K+ labeling hours to: • Scale operations, optimize outputs, and conquer datasets • Get accurate and versatile training data with smooth ML Ops integration • Eliminate silos, leverage skill-based task assignments, and ensure multi-level QA. Taskmonk’s balance of speed, ease of use, and focus on data quality results in enterprise AI success.


**Average Rating:** 4.6/5.0
**Total Reviews:** 17
**How Do G2 Users Rate Taskmonk?**

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

**Who Is the Company Behind Taskmonk?**

- **Seller:** [Taskmonk](https://www.g2.com/sellers/taskmonk)
- **Year Founded:** 2018
- **HQ Location:** Bengaluru, Karnataka, India
- **Twitter:** @TaskmonkAI (16 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/taskmonk/ (28 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 72% Small-Business, 22% Enterprise


#### What Are Taskmonk's Pros and Cons?

**Pros:**

- Ease of Use (12 reviews)
- Customer Support (9 reviews)
- Efficiency (6 reviews)
- Features (6 reviews)
- Setup Ease (6 reviews)

**Cons:**

- Lack of Features (4 reviews)
- Difficult Learning (3 reviews)
- Complexity (2 reviews)
- Technical Difficulties (2 reviews)
- Upload Issues (2 reviews)


### What Do G2 Reviewers Say About Taskmonk?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find Taskmonk to be **incredibly easy to use** , with a quick setup and intuitive interface for beginners.
- Users value the **exceptional customer support** of Taskmonk, noting responsiveness and a proactive approach to meeting needs.
- Users highlight the **efficiency** of Taskmonk, benefiting from its speed and seamless integration for high-volume projects.
- Users appreciate the **intuitive interface** and **quick onboarding** of Taskmonk, making data labeling seamless and efficient.
- Users commend the **quick setup** of Taskmonk, enabling teams to get operational within days with minimal hassle.

**Cons:**

- Users note a **lack of advanced features** in Taskmonk, limiting functionality and usability, especially for beginners.
- Users find the **difficult learning curve** challenging, suggesting enhancements for a more user-friendly experience.
- Users find the **UI complexity** overwhelming at first, making navigation difficult for new users.
- Users experience some **technical difficulties** occasionally, but the prompt support team resolves issues quickly.
- Users face **upload issues** with long processing times and limitations on local uploads, which can hinder efficiency.

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

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

**Rating:** 4.0/5.0 stars
*— Aditya N.*

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

---

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

**Rating:** 5.0/5.0 stars
*— JAYAPRAKASH K.*

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

---



### 12. [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:** 66
**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: 8.8/10)
- **Ease of Use:** 8.2/10 (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:** https://www.linkedin.com/company/10064814/ (63 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 61% Small-Business, 27% Mid-Market


#### What Are Clarifai's Pros and Cons?

**Pros:**

- Features (12 reviews)
- AI Technology (10 reviews)
- Model Variety (9 reviews)
- AI Integration (8 reviews)
- AI Modeling (8 reviews)

**Cons:**

- Expensive (9 reviews)
- Complexity (4 reviews)
- Difficult Learning (3 reviews)
- Lack of Resources (3 reviews)
- Poor Documentation (3 reviews)


### What Do G2 Reviewers Say About Clarifai?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **flexibility and accuracy** of Clarifai's models for imaging tasks across industries.
- Users value the **fast and accurate AI tools** of Clarifai, enhancing image and video recognition effortlessly.
- Users appreciate the **model variety** offered by Clarifai, enabling tailored solutions through both pre-trained and customizable models.
- Users value the **advanced AI integration** of Clarifai, enhancing image and video recognition capabilities significantly.
- Users appreciate the **cutting-edge AI capabilities** of Clarifai, particularly for its image and video recognition features.

**Cons:**

- Users find the product **expensive** , especially for small-scale developers and non-profits with limited resources.
- Users find the **complexity of setup and documentation** makes it challenging to fully utilize Clarifai's capabilities.
- Users find the **learning curve to be steep** for beginners, making the initial experience challenging with Clarifai.
- Users express concern over the **lack of resources** for small developers, impacting usability and affordability.
- Users find the **documentation lacking** , often seeking additional resources to fully understand Clarifai's features.

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

**"[Easy-to-Use AI Tools with Fast, Accurate Image & Video Recognition](https://www.g2.com/survey_responses/clarifai-review-12237261)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Computer Software*

[Read full review](https://www.g2.com/survey_responses/clarifai-review-12237261)

---

**"[Helped with my projects! Would recommend!](https://www.g2.com/survey_responses/clarifai-review-11387093)"**

**Rating:** 4.0/5.0 stars
*— Verified User in Information Technology and Services*

[Read full review](https://www.g2.com/survey_responses/clarifai-review-11387093)

---



### 13. [Dataloop](https://www.g2.com/products/dataloop-dataloop/reviews)
Dataloop is a cutting-edge AI Development Platform that's transforming the way organizations build AI applications. Our platform is meticulously crafted to cater to developers at the heart of the AI development process, making it simpler and more intuitive to work with data and AI models. Our comprehensive solution spans the full AI development lifecycle, offering tools and functionalities that streamline data management, annotation, model selection, and deployment. Dataloop's platform is built with a focus on collaboration, allowing developers, data scientists, and engineers to work together seamlessly, breaking down traditional silos and fostering innovation. Key features include an intuitive drag-and-drop interface for constructing data pipelines, a vast library of pre-built AI elements and models, and robust data curation and annotation capabilities. These features are designed to empower developers to rapidly prototype, iterate, and deploy AI solutions, keeping pace with the fast-evolving demands of the market. Dataloop is committed to advancing AI development by providing a developer-centric platform that addresses the complexities and challenges of AI and data management. Our vision is to democratize AI development, enabling every organization to harness the power of AI and drive forward their innovative solutions.


**Average Rating:** 4.4/5.0
**Total Reviews:** 87
**How Do G2 Users Rate Dataloop?**

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

**Who Is the Company Behind Dataloop?**

- **Seller:** [Dataloop](https://www.g2.com/sellers/dataloop)
- **Year Founded:** 2017
- **HQ Location:** Herzliya, IL
- **LinkedIn® Page:** https://www.linkedin.com/company/dataloop (52 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Information Technology and Services
- **Company Size:** 39% Mid-Market, 33% Small-Business


#### What Are Dataloop's Pros and Cons?

**Pros:**

- Ease of Use (4 reviews)
- Annotation Efficiency (2 reviews)
- Annotation Tools (2 reviews)
- User Interface (2 reviews)
- Easy Integrations (1 reviews)

**Cons:**

- Complexity (1 reviews)
- Confusing Syntax (1 reviews)
- Difficult Navigation (1 reviews)
- Lack of Communication (1 reviews)
- Lack of Guidance (1 reviews)


### What Do G2 Reviewers Say About Dataloop?
*AI-generated summary from verified user reviews*

**Pros:**

- Users love the **ease of use** of Dataloop, highlighting its simple interface and seamless integration into workflows.
- Users value the **annotation efficiency** of Dataloop, enjoying its simple interface and ease of use.
- Users value the **simple interface** of Dataloop, enabling easy and effective annotation across various types.
- Users appreciate the **simple and easy-to-navigate interface** of Dataloop, enhancing their overall experience with the tool.
- Users value the **easy integrations** of Dataloop, enhancing compatibility with existing workflows and boosting productivity.

**Cons:**

- Users find the **UI changes confusing** , leading to a more complex experience with Dataloop.
- Users find the **confusing syntax** of Dataloop challenging, impacting their overall experience with the platform.
- Users find the **difficult navigation** due to the new UI changes frustrating and confusing for their workflow.
- Users feel there is a **lack of communication** within the community, which hinders support and collaboration.
- Users feel there is a lack of **guidance for first-time users** , suggesting a demo section would be beneficial.

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

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

**Rating:** 4.0/5.0 stars
*— Dennis R.*

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

---

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

**Rating:** 5.0/5.0 stars
*— Mzamil J.*

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

---


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

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

### 14. [Kili](https://www.g2.com/products/kili/reviews)
Kili Technology is a collaborative AI data platform designed to meet the rigorous needs of building large-scale production-ready AI data securely. Founded in Paris in 2018, Kili Technology caters to a diverse range of industries, including healthcare, financial services, manufacturing, defense, and technology. The platform is engineered to support teams of varying sizes, accommodating anywhere from 1 to over 500 concurrent users, and processes millions of assets annually. The core functionality of Kili Technology lies in its ability to facilitate collaboration among cross-functional teams. Unlike traditional labeling tools that primarily serve machine learning engineers, Kili connects data science teams with business stakeholders and subject matter experts. This integration enhances the AI development lifecycle by streamlining processes from annotation and labeling to validation and model feedback. As a result, users can ensure that the data used for training AI models is not only accurate but also relevant to the specific business context. Kili Technology is particularly beneficial for organizations looking to harness the power of AI while maintaining a high level of data quality. The platform supports various data modalities, allowing teams to work with text, images, audio, and video data seamlessly. This versatility makes it suitable for a wide range of applications, from developing natural language processing models to image recognition systems. By fostering collaboration among different roles within an organization, Kili enhances the overall efficiency of the AI development process. Key features of Kili Technology include an intuitive user interface that simplifies the labeling process, robust tools for data validation, and comprehensive feedback mechanisms that enable continuous improvement of AI models. Additionally, the platform offers advanced analytics capabilities, allowing teams to track progress and identify areas for enhancement. These features collectively empower organizations to build high-quality training datasets that meet the demands of complex AI applications. Kili Technology stands out in the competitive landscape of AI data platforms by prioritizing collaboration and usability. By bridging the gap between technical and non-technical stakeholders, it ensures that the development of AI solutions is a cohesive effort. This approach not only accelerates the time to market for AI initiatives but also enhances the overall quality of the training data, ultimately leading to more effective AI models.


**Average Rating:** 4.7/5.0
**Total Reviews:** 52
**How Do G2 Users Rate Kili?**

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

**Who Is the Company Behind Kili?**

- **Seller:** [Kili Technology](https://www.g2.com/sellers/kili-technology)
- **Company Website:** https://kili-technology.com
- **Year Founded:** 2018
- **HQ Location:** Paris, FR
- **Twitter:** @Kili_Technology (438 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/33266852 (47 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 38% Mid-Market, 34% Small-Business


#### What Are Kili's Pros and Cons?

**Pros:**

- Data Labeling (1 reviews)
- Data Labelling (1 reviews)
- Ease of Use (1 reviews)
- Model Variety (1 reviews)

**Cons:**

- Limited Features (1 reviews)
- Missing Features (1 reviews)


### What Do G2 Reviewers Say About Kili?
*AI-generated summary from verified user reviews*

**Pros:**

- Users appreciate the **ease of use and precise metrics** provided by Kili for effective annotation project management.
- Users value the **ease of use** of Kili's data labeling platform and its accurate metrics for project visualization.
- Users love the **ease of use** offered by Kili, making annotation projects straightforward and efficient.
- Users value the **ease of use and metric precision** of Kili, enhancing project visualization and annotation efficiency.

**Cons:**

- Users feel that Kili has **limited features** and would benefit from more content in platform updates.
- Users feel that Kili lacks **adequate content updates** , impacting their overall experience and feature engagement.

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

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

**Rating:** 5.0/5.0 stars
*— Lantosoa V.*

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

---

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

**Rating:** 5.0/5.0 stars
*— Hery R.*

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

---



### 15. [CVAT.ai](https://www.g2.com/products/cvat-ai/reviews)
Company Overview: CVAT.ai is a global provider of data annotation tools and services, known for developing one of the most popular open-source annotation tools, CVAT. In addition to the open-source platform, we offer professional data labeling services, an Enterprise version of CVAT, as well as consulting and customization services to meet specific client needs. Our team supports businesses and AI researchers worldwide in efficiently managing data annotation for computer vision projects. Key Features: - Popular Open-Source Tool: CVAT is trusted by thousands of developers and organizations globally. - Data Labeling Services: We provide expert data labeling services to handle projects from start to finish. - Enterprise Version of CVAT: The Enterprise version offers advanced features, support, and scalability for larger organizations. - Consulting and Customization: We offer consulting services and can customize CVAT to match your project needs. Learn more about our approach to consulting and feature requests here. - AI-Assisted Automation: Our platform uses AI to enhance labeling efficiency and accuracy. - Team Collaboration: Teams can collaborate seamlessly on large-scale projects. - Customizable and Scalable: CVAT can be adapted to your project size and needs. - Secure: We meet global data privacy and security standards. What We Solve: CVAT.ai helps users reduce manual efforts by making data annotation faster, more accurate, and easy to manage. Through our open-source platform, professional labeling services, consulting, and the Enterprise version, we offer a flexible, comprehensive solution for any computer vision project.


**Average Rating:** 4.6/5.0
**Total Reviews:** 30
**How Do G2 Users Rate CVAT.ai?**

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

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

- **Seller:** [CVAT.ai](https://www.g2.com/sellers/cvat-ai)
- **Year Founded:** 2022
- **HQ Location:** Palo Alto, US
- **LinkedIn® Page:** https://www.linkedin.com/company/cvat-ai/ (114 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 70% Small-Business, 20% Mid-Market


#### What Are CVAT.ai's Pros and Cons?

**Pros:**

- Annotation Efficiency (7 reviews)
- Ease of Use (4 reviews)
- Efficiency (4 reviews)
- Quality (4 reviews)
- Customer Support (3 reviews)

**Cons:**

- Difficult Learning (2 reviews)
- Complexity (1 reviews)
- Labeling Issues (1 reviews)
- Lack of Features (1 reviews)
- Slow Performance (1 reviews)


### What Do G2 Reviewers Say About CVAT.ai?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **annotation efficiency** of CVAT.ai, appreciating its intuitive interface and diverse tools for streamlined labeling.
- Users rave about the **intuitive interface** of CVAT.ai, making annotation and dataset management seamless and efficient.
- Users commend the **high efficiency** of CVAT.ai, benefiting from seamless integration and intuitive tools for their projects.
- Users admire the **exceptional quality** of CVAT.ai, highlighting its effectiveness and positive collaborative experience.
- Users praise the **exceptional customer support** from CVAT.ai, making technical challenges easier to navigate.

**Cons:**

- Users find CVAT.ai **difficult to learn** , especially beginners, due to its complex functions and time-consuming setup.
- Users find **CVAT.ai complex** for beginners, often feeling lost amid its numerous functions despite detailed information available.
- Users experience **labeling inconsistencies** in complex cases, though the team is responsive to feedback for corrections.
- Users are frustrated by the **lack of support for large 16-bit images** , which limits their workflow effectiveness.
- Users experience **slow performance** when dealing with very large video files or thousands of images on CVAT.ai.

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

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

**Rating:** 4.0/5.0 stars
*— Fabian v.*

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

---

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

**Rating:** 4.5/5.0 stars
*— Katarzyna K.*

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

---



### 16. [Prolific](https://www.g2.com/products/prolific/reviews)
Prolific is helping research teams build a better world with better data. Our platform makes it easy to access high-quality data from 200k+ diverse, vetted participants.


**Average Rating:** 4.6/5.0
**Total Reviews:** 202
**How Do G2 Users Rate Prolific?**

- **Labeler Quality:** 8.1/10 (Category avg: 8.9/10)
- **Object Detection:** 5.3/10 (Category avg: 8.9/10)
- **Data Types:** 5.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)

**Who Is the Company Behind Prolific?**

- **Seller:** [Prolific](https://www.g2.com/sellers/prolific)
- **Company Website:** https://www.prolific.com/
- **Year Founded:** 2014
- **HQ Location:** London, England
- **Twitter:** @Prolific (13,812 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/5168486 (1,246 employees on LinkedIn®)

**Who Uses This Product?**
- **Who Uses This:** Assistant Professor, Associate Professor
- **Top Industries:** Higher Education, Research
- **Company Size:** 40% Enterprise, 37% Small-Business


#### What Are Prolific's Pros and Cons?

**Pros:**

- Ease of Use (39 reviews)
- Participant Recruitment (29 reviews)
- Quality (19 reviews)
- Participant Engagement (16 reviews)
- Customer Support (12 reviews)

**Cons:**

- Expensive (13 reviews)
- Participant Management (11 reviews)
- Limited Features (8 reviews)
- Poor Customer Support (7 reviews)
- Limited Surveys (6 reviews)


### What Do G2 Reviewers Say About Prolific?
*AI-generated summary from verified user reviews*

**Pros:**

- Users find Prolific's interface **easy to use** , ensuring access to quality data and efficient support when needed.
- Users value the **extensive participant recruitment** features of Prolific, enabling diverse and high-quality data collection.
- Users value the **high-quality responses** from Prolific, making data collection fast and efficient.
- Users value the **ease and speed of participant recruitment** with Prolific, ensuring a quality experience.
- Users value the **excellent customer support** provided by Prolific, noting its helpfulness and responsiveness.

**Cons:**

- Users find Prolific to be **too expensive** for smaller projects, impacting accessibility and filtering options.
- Users find that **participant availability is limited** in some countries, affecting the research process.
- Users find the **limited features** of Prolific, particularly in participant screeners, frustrating and restrictive for diverse demographics.
- Users often experience **slow customer support** , which can hinder the resolution of participant-related issues promptly.
- Users find the **limited surveys** on Prolific to be a drawback, affecting their overall survey experience.

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

**"[Easy Targeted Research with a Large Pool of Trusted, Insightful Participants](https://www.g2.com/survey_responses/prolific-review-12294313)"**

**Rating:** 5.0/5.0 stars
*— Michael  M.*

[Read full review](https://www.g2.com/survey_responses/prolific-review-12294313)

---

**"[User-Friendly with Room for Improvement](https://www.g2.com/survey_responses/prolific-review-12177113)"**

**Rating:** 5.0/5.0 stars
*— Annie B.*

[Read full review](https://www.g2.com/survey_responses/prolific-review-12177113)

---



### 17. [Playment](https://www.g2.com/products/playment/reviews)
Playment’s GT Studio is a no-code, self-serve data labeling platform that is heuristically designed to help ML teams create diverse, high-quality ground truth datasets at an efficient cost, scale, and speed. Most ML teams work with sub-optimal data or rely on tools or processes that take up a significant amount of their time which could be spent innovating. GT Studio is a web-based labeling platform that eliminates inefficiencies for the annotator and the project manager via ML-assisted annotation tools and easy-to-use workflow management software. Our flexible engagement models help ML teams of any size and any industry meets their goals faster by leveraging the highest quality data really quickly. In a nutshell: With Playment’s GT Studio you can access: ✔ ML-assisted 2D and 3D labeling tools ✔ 5X faster throughputs than manual labeling ✔ Powerful APIs for easy pipeline integration ✔ Workflow Builder for easier project setup ✔ Built-in QC workflows and tools ✔ Real-time annotator productivity analytics ✔ Assured security and compliance We work with the 200+ ML teams in companies like Samsung, Intel, Nuro, Postmates, AI Motive, Ouster, Sony, Continental, Hella, Renault, Seimens, Daimler, LG, Innoviz, and many more. We are backed by renowned players like Y Combinator, SAIF Partners, Google Launchpad, and Samsung. To learn more about our solutions visit https://playment.io/ or write to us at hello@playment.in.


**Average Rating:** 4.7/5.0
**Total Reviews:** 11
**How Do G2 Users Rate Playment?**

- **Labeler Quality:** 8.9/10 (Category avg: 8.9/10)
- **Object Detection:** 8.9/10 (Category avg: 8.9/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.7/10 (Category avg: 8.8/10)

**Who Is the Company Behind Playment?**

- **Seller:** [Playment](https://www.g2.com/sellers/playment)
- **Year Founded:** 2005
- **HQ Location:** Las Vegas, US
- **LinkedIn® Page:** https://www.linkedin.com/company/6611939 (6,122 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 36% Enterprise, 36% Small-Business



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

**"[Great database of Machine Learning data in various sectors. Industry Leader !!](https://www.g2.com/survey_responses/playment-review-6777084)"**

**Rating:** 5.0/5.0 stars
*— Shivanshu S.*

[Read full review](https://www.g2.com/survey_responses/playment-review-6777084)

---

**"[Genuine and useful data provider for AI in nearly any field !!](https://www.g2.com/survey_responses/playment-review-6760035)"**

**Rating:** 4.5/5.0 stars
*— Umesh Kumar J.*

[Read full review](https://www.g2.com/survey_responses/playment-review-6760035)

---


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

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

### 18. [Datature](https://www.g2.com/products/datature/reviews)
Datature is an AI Vision platform that simplifies computer vision development by unifying data labeling, model training, and deployment into a single workflow. By eliminating the need for fragmented tools and complex infrastructure, teams can focus on solving real-world problems.


**Average Rating:** 4.9/5.0
**Total Reviews:** 38
**How Do G2 Users Rate Datature?**

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

**Who Is the Company Behind Datature?**

- **Seller:** [Datature](https://www.g2.com/sellers/datature)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @DatatureAI (168 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datature/ (23 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software, Research
- **Company Size:** 63% Small-Business, 29% Enterprise


#### What Are Datature's Pros and Cons?

**Pros:**

- Efficiency (5 reviews)
- Annotation Efficiency (4 reviews)
- Ease of Use (4 reviews)
- Model Management (4 reviews)
- AI Capabilities (3 reviews)

**Cons:**

- Limited Customization (2 reviews)
- Annotation Issues (1 reviews)
- Difficult Learning (1 reviews)
- Difficult Setup (1 reviews)
- Expensive (1 reviews)


### What Do G2 Reviewers Say About Datature?
*AI-generated summary from verified user reviews*

**Pros:**

- Users praise Datature for its **efficiency and user-friendliness** , significantly enhancing productivity in computer vision projects.
- Users value the **annotation efficiency** of Datature, finding it convenient for both beginners and professionals.
- Users value the **ease of use** of Datature, appreciating its intuitive interface for efficient project building.
- Users praise Datature's **extensive model options and exceptional support** , making it ideal for efficient AI vision solutions.
- Users appreciate the **efficient AI capabilities** of Datature, enhancing data labeling and model training with intuitive tools.

**Cons:**

- Users find **limited customization** in Datature frustrating, particularly advanced users wanting more control over their setups.
- Users face **annotation issues** due to unclear instructions, but support is available for prompt assistance.
- Users mention a **difficult learning curve** for setup and labeling, despite enjoying the ease of model building.
- Users note a **difficult setup** for labeling jobs, though model building is straightforward once learned.
- Users find the product **expensive** , though they recognize it as a worthwhile investment for serious AI vision projects.

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

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

**Rating:** 4.5/5.0 stars
*— Phil B.*

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

---

**"[Excellent Tool with Outstanding Customer Support!](https://www.g2.com/survey_responses/datature-review-11280690)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Higher Education*

[Read full review](https://www.g2.com/survey_responses/datature-review-11280690)

---



### 19. [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:** 22
**How Do G2 Users Rate FiftyOne?**

- **Ease of Use:** 8.2/10 (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:** https://www.linkedin.com/company/voxel51 (63 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 64% Small-Business, 27% Mid-Market



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

**"[A Powerhouse for Data Visualization and Model Development](https://www.g2.com/survey_responses/fiftyone-review-12573495)"**

**Rating:** 4.5/5.0 stars
*— Liliana C.*

[Read full review](https://www.g2.com/survey_responses/fiftyone-review-12573495)

---

**"[Centralized Solution for AI Pipeline Management](https://www.g2.com/survey_responses/fiftyone-review-12623717)"**

**Rating:** 5.0/5.0 stars
*— Ken P.*

[Read full review](https://www.g2.com/survey_responses/fiftyone-review-12623717)

---



### 20. [Alegion](https://www.g2.com/products/alegion/reviews)
Alegion's managed service accelerates enterprise AI initiatives by validating, labeling, and annotating training data.


**Average Rating:** 4.6/5.0
**Total Reviews:** 12
**How Do G2 Users Rate Alegion?**

- **Labeler Quality:** 8.9/10 (Category avg: 8.9/10)
- **Object Detection:** 9.1/10 (Category avg: 8.9/10)
- **Data Types:** 9.6/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.8/10)

**Who Is the Company Behind Alegion?**

- **Seller:** [Alegion](https://www.g2.com/sellers/alegion)
- **Year Founded:** 2012
- **HQ Location:** Austin, US
- **Twitter:** @Alegion (4 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2756641 (44 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 42% Small-Business, 33% Enterprise


#### What Are Alegion's Pros and Cons?

**Pros:**

- Data Labelling (3 reviews)
- Data Management (3 reviews)
- Features (3 reviews)
- Annotation Efficiency (2 reviews)
- Customization (2 reviews)

**Cons:**

- Expensive (3 reviews)
- Complexity (1 reviews)
- Lack of Features (1 reviews)
- Limited Customization (1 reviews)


### What Do G2 Reviewers Say About Alegion?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of implementation** and scalability of Alegion for efficient data labeling tasks.
- Users value the **efficient handling of high volume and complex data** with automated workflows for better structuring.
- Users appreciate the **comprehensive security features** of Alegion, ensuring ease of use and effective integration for high-volume data.
- Users praise Alegion for its **annotation efficiency** , noting its ease of use and strong support for collaboration.
- Users value the **high levels of customization** in Alegion, simplifying their machine learning data annotation experiences.

**Cons:**

- Users find the pricing of Alegion to be **expensive** , particularly for smaller projects, despite acknowledging its quality.
- Users find the **complexity in setup** challenging, particularly those new to data annotation, affecting their experience.
- Users desire more **interactive options** in Alegion to enhance their overall experience and engagement with the platform.
- Users find the **limited customization** options for various work types in Alegion to be a significant drawback.

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

**"[Alegion](https://www.g2.com/survey_responses/alegion-review-10947473)"**

**Rating:** 5.0/5.0 stars
*— Nanda  M.*

[Read full review](https://www.g2.com/survey_responses/alegion-review-10947473)

---

**"[Alegion Best Provider](https://www.g2.com/survey_responses/alegion-review-10975674)"**

**Rating:** 4.5/5.0 stars
*— Gautam M.*

[Read full review](https://www.g2.com/survey_responses/alegion-review-10975674)

---


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

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

### 21. [Shaip Cloud](https://www.g2.com/products/shaip-cloud/reviews)
Shaip Data is a modern platform designed to gather high-quality, ethical data for training AI models. It has three main parts: Shaip Manage, Shaip Work, and Shaip Intelligence. The platform makes workflows easier, reduces issues with a global team, and offers better visibility and real-time quality checks. Shaip Data helps quickly collect, process, and label large amounts of data (text, audio, images, and video) to train and improve AI and ML models.


**Average Rating:** 4.3/5.0
**Total Reviews:** 21
**How Do G2 Users Rate Shaip Cloud?**

- **Labeler Quality:** 8.3/10 (Category avg: 8.9/10)
- **Object Detection:** 8.5/10 (Category avg: 8.9/10)
- **Data Types:** 8.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.8/10)

**Who Is the Company Behind Shaip Cloud?**

- **Seller:** [Shaip](https://www.g2.com/sellers/shaip)
- **Year Founded:** 2018
- **HQ Location:** Louisville, Kentucky
- **Twitter:** @weareShaip (224 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/66611098 (363 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 41% Enterprise, 36% Small-Business



#### What Are Recent G2 Reviews of Shaip Cloud?

**"[Gold Standard AI Driven Healthcare Solutions](https://www.g2.com/survey_responses/shaip-cloud-review-8255450)"**

**Rating:** 4.5/5.0 stars
*— Nikhil G.*

[Read full review](https://www.g2.com/survey_responses/shaip-cloud-review-8255450)

---

**"[Boosting my App with Shaip Cloud](https://www.g2.com/survey_responses/shaip-cloud-review-8561561)"**

**Rating:** 5.0/5.0 stars
*— Dhawlandra S.*

[Read full review](https://www.g2.com/survey_responses/shaip-cloud-review-8561561)

---



### 22. [Hive Data](https://www.g2.com/products/hive-data/reviews)
Founded in 2013, Hive is a pioneering AI company specialized in computer vision and deep learning. Hive is focused on powering innovators across industries with practical AI solutions and data labeling, grounded in the world's highest quality visual and audio metadata. The company solves challenges for enterprises through three main pillars of the business: Hive Data, Hive Predict, and Hive Enterprise. Hive Data is the world's largest distributed data labeling platform with over 2 million registered contributors globally. Hive Predict is our set of proprietary deep learning models, powering AI for corporate clients. Hive Enterprise packages applied industry solutions, integrating proprietary models with client datasets and systems.


**Average Rating:** 4.4/5.0
**Total Reviews:** 10
**How Do G2 Users Rate Hive Data?**

- **Labeler Quality:** 7.5/10 (Category avg: 8.9/10)
- **Object Detection:** 10.0/10 (Category avg: 8.9/10)
- **Data Types:** 6.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)

**Who Is the Company Behind Hive Data?**

- **Seller:** [Hive.ai](https://www.g2.com/sellers/hive-ai)
- **Year Founded:** 2013
- **HQ Location:** San Francisco, California
- **Twitter:** @hive_ai (4,857 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/hiveai (516 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Enterprise, 40% Small-Business



#### What Are Recent G2 Reviews of Hive Data?

**"[Hive data review](https://www.g2.com/survey_responses/hive-data-review-5235228)"**

**Rating:** 4.0/5.0 stars
*— Gaurav Y.*

[Read full review](https://www.g2.com/survey_responses/hive-data-review-5235228)

---

**"[Best Application for Logo & Brand Exposure](https://www.g2.com/survey_responses/hive-data-review-5012433)"**

**Rating:** 5.0/5.0 stars
*— Ajeet P.*

[Read full review](https://www.g2.com/survey_responses/hive-data-review-5012433)

---


#### What Are G2 Users Discussing About Hive Data?

- [What does the data warehouse software hive allow?](https://www.g2.com/discussions/hive-data-what-does-the-data-warehouse-software-hive-allow)
- [What does the data warehouse software hive allow?](https://www.g2.com/discussions/what-does-the-data-warehouse-software-hive-allow)
- [What are the features and limitations of hive?](https://www.g2.com/discussions/hive-data-what-are-the-features-and-limitations-of-hive)
- [What are the features and limitations of hive?](https://www.g2.com/discussions/what-are-the-features-and-limitations-of-hive)
- [What are the features of hive in big data?](https://www.g2.com/discussions/hive-data-what-are-the-features-of-hive-in-big-data)

### 23. [BasicAI Data Annotation Platform](https://www.g2.com/products/basicai-data-annotation-platform/reviews)
BasicAI Data Annotation Platform (https://www.basic.ai/basicai-cloud-data-annotation-platform) is an All-in-One Smart Data Annotation Platform with strong multimodal feature and AI-powered annotation tools that supports: - Auto-annotation and objects tracking of 3D point cloud (single frame & frame series), 2D & 3D sensor fusion, images and video (consecutive images) data - Auto-segmentation of 3D point cloud data - Smooth annotation teamwork, including management of workflow, performance roles & permission, etc. - No-lag annotation of up to 150 million points in 300 frame in one point cloud data, as well as 1,000 images in one 2D data.


**Average Rating:** 4.4/5.0
**Total Reviews:** 36
**How Do G2 Users Rate BasicAI Data Annotation Platform?**

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

**Who Is the Company Behind BasicAI Data Annotation Platform?**

- **Seller:** [BasicAI](https://www.g2.com/sellers/basicai)
- **Year Founded:** 2019
- **HQ Location:** Irvine, CA
- **Twitter:** @BasicAIteam (94 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/basicaius/about/?viewAsMember=true (16 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services, Computer Software
- **Company Size:** 44% Small-Business, 31% Mid-Market



#### What Are Recent G2 Reviews of BasicAI Data Annotation Platform?

**"[Very Likely](https://www.g2.com/survey_responses/basicai-data-annotation-platform-review-9440455)"**

**Rating:** 5.0/5.0 stars
*— Vineeth V.*

[Read full review](https://www.g2.com/survey_responses/basicai-data-annotation-platform-review-9440455)

---

**"[User Friendly](https://www.g2.com/survey_responses/basicai-data-annotation-platform-review-9440485)"**

**Rating:** 4.5/5.0 stars
*— Rahul  D.*

[Read full review](https://www.g2.com/survey_responses/basicai-data-annotation-platform-review-9440485)

---


#### What Are G2 Users Discussing About BasicAI Data Annotation Platform?

- [What are the 5 components of AI?](https://www.g2.com/discussions/what-are-the-5-components-of-ai)
- [What are the basic functions of AI?](https://www.g2.com/discussions/what-are-the-basic-functions-of-ai)
- [What are the basic elements of AI programs?](https://www.g2.com/discussions/what-are-the-basic-elements-of-ai-programs)
- [What are the main features of AI?](https://www.g2.com/discussions/what-are-the-main-features-of-ai)

### 24. [Labellerr](https://www.g2.com/products/labellerr/reviews)
Labellerr is a computer vision workflow automation platform. It helps ML teams to manage their AI development lifecycle much more efficiently. It helps teams to collaboratively work on data labeling tasks and have modules to manage multiple projects, users, and millions of unstructured data. Teams can perform- 1. Automated data curation 2. EDA (Exploratory Data Analysis) 3. Automated data labeling 4. Quality control with assurance 5. Automated QC 6. Model debugging Data types that it supports are images, videos, text, audio, and PDFs. Use cases it supports are object detection, segmentation, classification, image captioning, transcription, and translation. The active learning feature has helped users save 1000s USD per task. Labellerr recently launched LabelGPT which labels images using a prompt. It leverages the combination of generative AI models to label data in minutes rather than months.


**Average Rating:** 4.8/5.0
**Total Reviews:** 21
**How Do G2 Users Rate Labellerr?**

- **Labeler Quality:** 9.9/10 (Category avg: 8.9/10)
- **Object Detection:** 9.7/10 (Category avg: 8.9/10)
- **Data Types:** 9.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.6/10 (Category avg: 8.8/10)

**Who Is the Company Behind Labellerr?**

- **Seller:** [Tensor Matics Inc.](https://www.g2.com/sellers/tensor-matics-inc)
- **Year Founded:** 2017
- **HQ Location:** Wilmington, Delaware
- **LinkedIn® Page:** https://www.linkedin.com/company/tensormatics/ (2 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 57% Small-Business, 38% Mid-Market


#### What Are Labellerr's Pros and Cons?

**Pros:**

- Annotation Efficiency (1 reviews)
- Collaboration (1 reviews)
- Customer Support (1 reviews)
- Data Accuracy (1 reviews)
- Efficiency (1 reviews)

**Cons:**

- Difficult Setup (1 reviews)


### What Do G2 Reviewers Say About Labellerr?
*AI-generated summary from verified user reviews*

**Pros:**

- Users praise the **annotation efficiency** of Labellerr, benefiting from high-quality results and seamless collaboration.
- Users praise the **excellent collaboration** offered by Labellerr, enhancing their overall experience and productivity.
- Users value the **responsive customer support** of Labellerr, appreciating the team's constant availability for assistance.
- Users commend the **high annotation quality** of Labellerr, contributing to effective collaboration and team success.
- Users praise the **efficiency** of Labellerr, highlighting its quality and excellent collaboration among team members.

**Cons:**

- Users find the **difficult setup** of Labellerr time-consuming, impacting the initial collaboration experience.

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

**"[Incredible solution and support team](https://www.g2.com/survey_responses/labellerr-review-9089700)"**

**Rating:** 5.0/5.0 stars
*— Aswin M.*

[Read full review](https://www.g2.com/survey_responses/labellerr-review-9089700)

---

**"[LabellErr annotation partnership for waste image computer vision](https://www.g2.com/survey_responses/labellerr-review-10840868)"**

**Rating:** 5.0/5.0 stars
*— Laurent M.*

[Read full review](https://www.g2.com/survey_responses/labellerr-review-10840868)

---



### 25. [Segments.ai](https://www.g2.com/products/segments-ai/reviews)
Multi-sensor labeling platform for robotics and autonomous driving. Segments.ai is a fast and accurate data labeling platform for multi-sensor data annotation. You can obtain segmentation labels, vector labels, and more via the intuitive labeling interfaces for images, videos, and 3D point clouds (lidar and RGBD). Image Segmentation - Semantic segmentation - Instance segmentation - Panoptic segmentation - ML-powered labeling tools: DeepPixels and Autosegment Image Vector Labeling - Bounding boxes - Polygons - Polylines - Keypoints Point Cloud Segmentation - Semantic segmentation - Instance segmentation - Panoptic segmentation Point Cloud Vector Labeling - Cuboids / 3D bounding boxes - Keypoints - Polygons and polylines Video labeling - Label sequences of data fast with interpolation and ML assistance. - Label merged 3D point clouds of unlimited size. - Label 3D sequences faster with batch mode and merged point cloud view. Sensor fusion: visualize and label multiple modalities in the same interface Build your clever annotation workflow exactly how you want, with the flexibility you need to get the job done quickly and efficiently. Segments.ai is a self-serve platform with dedicated support from our core team of engineers when you need it. - A Python SDK that finally makes sense - Documentation to make the setup feel like a breeze - Self-serve with support only when you are stuck, so we don't slow you down - Automatically trigger actions using webhooks - Connect your cloud provider (AWS, Google Cloud, Azure) - Export to popular ML frameworks (PyTorch, TensorFlow, Hugging Face 🤗) Onboard your workforce or use one of our workforce partners. Our management tools make it easy to label and review large datasets together. Get started with a free trial today at https://segments.ai/join


**Average Rating:** 4.6/5.0
**Total Reviews:** 22
**How Do G2 Users Rate Segments.ai?**

- **Labeler Quality:** 8.9/10 (Category avg: 8.9/10)
- **Object Detection:** 8.3/10 (Category avg: 8.9/10)
- **Data Types:** 8.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.6/10 (Category avg: 8.8/10)

**Who Is the Company Behind Segments.ai?**

- **Seller:** [Segments.ai](https://www.g2.com/sellers/segments-ai)
- **Year Founded:** 2020
- **HQ Location:** Leuven, Vlaams-Brabant, Belgium
- **Twitter:** @SegmentsAI (483 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/segmentsai/ (11 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Research, Computer Software
- **Company Size:** 95% Small-Business, 5% Mid-Market


#### What Are Segments.ai's Pros and Cons?

**Pros:**

- Features (3 reviews)
- Data Labeling (2 reviews)
- Efficiency (2 reviews)
- Time-Saving (2 reviews)
- Annotation Efficiency (1 reviews)

**Cons:**

- Difficult Learning (2 reviews)
- Learning Curve (2 reviews)
- Annotation Issues (1 reviews)
- Lack of Features (1 reviews)
- Lack of Tools (1 reviews)


### What Do G2 Reviewers Say About Segments.ai?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **ease of use and efficiency** of Segments.ai for organizing and annotating datasets quickly.
- Users praise the **specialization and efficiency in multi-sensor data labeling** , enhancing their experience for autonomous systems.
- Users appreciate the **efficiency** of Segments.ai, making data labeling and organization significantly quicker and easier.
- Users value the **time-saving capabilities** of Segments.ai, enjoying efficient dataset organization and streamlined annotation processes.
- Users appreciate the **annotation efficiency** of Segments.ai, noting its speed and ease compared to other tools.

**Cons:**

- Users find the **difficult learning** curve in mastering Segments.ai's complex multi-sensor annotation tools challenging.
- Users experience a noticeable **learning curve** when mastering Segments.ai's multi-sensor annotation tools and interface.
- Users feel that the **annotation process needs additional configuration and more automation options** to enhance efficiency.
- Users note a **lack of features** in Segments.ai, suggesting enhancements for automation and configuration to improve usability.
- Users note a **lack of tools** for advanced features, suggesting more automation options for improved annotation efficiency.

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

**"[Fast, All-in-One Platform for Labeling and Reviewing Data](https://www.g2.com/survey_responses/segments-ai-review-12736485)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Computer Software*

[Read full review](https://www.g2.com/survey_responses/segments-ai-review-12736485)

---

**"[Effortless Dataset Management with Intuitive Collaboration](https://www.g2.com/survey_responses/segments-ai-review-11998627)"**

**Rating:** 4.5/5.0 stars
*— Claudio S.*

[Read full review](https://www.g2.com/survey_responses/segments-ai-review-11998627)

---




## What Is Data Labeling Software?

[Artificial Intelligence Software](https://www.g2.com/categories/artificial-intelligence)

## What Software Categories Are Similar to Data Labeling Software?

- [Data Science and Machine Learning Platforms](https://www.g2.com/categories/data-science-and-machine-learning-platforms)
- [MLOps Platforms](https://www.g2.com/categories/mlops-platforms)
- [Active Learning Tools](https://www.g2.com/categories/active-learning-tools)


---

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

### What You Should Know About Data Labeling Software

### What is Data Labeling Software?

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

Many types of data labeling solutions are available, ranging from simple tools that allow users to label data manually to more advanced tools that use machine learning algorithms to automate the labeling process. Some data labeling software also includes features such as image annotation tools, which allow users to label and annotate images and other visual data.

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?

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.

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



---
## What Are the Most Common Questions About Data Labeling Software?
*AI-generated · 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.