# 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.





## Best Data Labeling Software At A Glance

- **Leader:** [Roboflow](https://www.g2.com/products/roboflow/reviews)
- **Highest Performer:** [BasicAI Data Annotation Platform](https://www.g2.com/products/basicai-data-annotation-platform/reviews)
- **Easiest to Use:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)
- **Top Trending:** [Encord](https://www.g2.com/products/encord/reviews)
- **Best Free Software:** [SuperAnnotate](https://www.g2.com/products/superannotate/reviews)


---

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---

## Top-Rated Products (Ranked by G2 Score)
  ### 1. [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:** 4.8/5.0
  **Total Reviews:** 139

**User Satisfaction Scores:**

- **Labeler Quality:** 8.9/10 (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)


**Seller Details:**

- **Seller:** [Roboflow](https://www.g2.com/sellers/roboflow)
- **Year Founded:** 2019
- **HQ Location:** Remote, US
- **Twitter:** @roboflow (12,986 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/36096640 (123 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Founder, Researcher
  - **Top Industries:** Computer Software, Research
  - **Company Size:** 77% Small-Business, 14% Mid-Market


#### Pros & 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)

  ### 2. [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:** 4.9/5.0
  **Total Reviews:** 265

**User Satisfaction Scores:**

- **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:** 9.6/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [SuperAnnotate](https://www.g2.com/sellers/superannotate)
- **Company Website:** https://superannotate.com/
- **Year Founded:** 2018
- **HQ Location:** San Francisco, CA
- **Twitter:** @superannotate (704 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/18999422/ (315 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Who Uses This:** Student, CEO
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 55% Small-Business, 25% Mid-Market


#### Pros & 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)

  ### 3. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Labelbox](https://www.g2.com/sellers/labelbox)
- **Year Founded:** 2018
- **HQ Location:** San Francisco, California
- **Twitter:** @labelbox (3,394 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/labelbox/ (427 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 46% Small-Business, 38% Mid-Market


#### Pros & 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)

  ### 4. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Encord](https://www.g2.com/sellers/encord)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **Twitter:** @encord_team (939 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/69557125 (163 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Hospital & Health Care
  - **Company Size:** 51% Small-Business, 40% Mid-Market


#### Pros & 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)

  ### 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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (2,220,862 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/amazon-web-services/ (156,424 employees on LinkedIn®)
- **Ownership:** NASDAQ: AMZN

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 37% Enterprise, 37% Small-Business


  ### 6. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Keymakr](https://www.g2.com/sellers/keymakr)
- **Year Founded:** 2015
- **HQ Location:** New York, NY
- **Twitter:** @keymakr_com (355 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/keymakr/ (63 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 52% Small-Business, 22% Mid-Market


#### Pros & 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)

  ### 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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Sama](https://www.g2.com/sellers/sama)
- **Year Founded:** 2008
- **HQ Location:** San Francisco, US
- **Twitter:** @SamaAI (228,933 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/410136 (4,307 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 55% Small-Business, 36% Enterprise


#### Pros & 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)

  ### 8. [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:** 4.8/5.0
  **Total Reviews:** 54

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [V7](https://www.g2.com/sellers/v7)
- **Year Founded:** 2018
- **HQ Location:** London, England
- **Twitter:** @v7labs (3,458 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/v7labs/ (104 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 54% Small-Business, 35% Mid-Market


#### Pros & 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)

  ### 9. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Taskmonk](https://www.g2.com/sellers/taskmonk)
- **Year Founded:** 2018
- **HQ Location:** Bengaluru, Karnataka, India
- **Twitter:** @TaskmonkAI (17 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/taskmonk/ (29 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 72% Small-Business, 22% Enterprise


#### Pros & 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)

  ### 10. [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:** 32

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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 (19,630 employees on LinkedIn®)
- **Ownership:** ASX:APX
- **Total Revenue (USD mm):** $244,900

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 56% Small-Business, 26% Enterprise


#### Pros & 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)

  ### 11. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Clarifai](https://www.g2.com/sellers/clarifai)
- **Year Founded:** 2013
- **HQ Location:** Wilmington, Delaware
- **Twitter:** @clarifai (10,760 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/10064814/ (86 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 61% Small-Business, 27% Mid-Market


#### Pros & Cons

**Pros:**

- Features (13 reviews)
- AI Technology (10 reviews)
- Model Variety (10 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)

  ### 12. [Kili](https://www.g2.com/products/kili/reviews)
  Kili Technology is a collaborative AI data platform designed to assist enterprise customers in creating production-ready AI training data. 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 10 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:** 51

**User Satisfaction Scores:**

- **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:** 8.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **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 (439 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/33266852 (48 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 37% Mid-Market, 35% Small-Business


#### Pros & 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)

  ### 13. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Playment](https://www.g2.com/sellers/playment)
- **Year Founded:** 2005
- **HQ Location:** Las Vegas, US
- **LinkedIn® Page:** https://www.linkedin.com/company/6611939 (5,335 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 36% Enterprise, 36% Small-Business


  ### 14. [FiftyOne](https://www.g2.com/products/voxel51-fiftyone/reviews)
  FiftyOne by Voxel51 - the most powerful visual AI and computer vision data platform. 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 streamlines visual data curation and model analysis with workflows to simplify the labor-intensive processes of visualizing and analyzing insights during data curation and model refinement—addressing a major challenge in large-scale data pipelines with billions of samples. Proven impact with FiftyOne: ⬆️30% increase in model accuracy ⏱️5+ months of development time saved 📈30% boost in team productivity Learn more about FiftyOne: 🔍Data Curation & 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:** 24

**User Satisfaction Scores:**

- **Ease of Use:** 8.1/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Voxel51](https://www.g2.com/sellers/voxel51)
- **Year Founded:** 2018
- **HQ Location:** Ann Arbor, US
- **Twitter:** @Voxel51 (1,595 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/voxel51 (65 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 58% Small-Business, 38% Mid-Market


  ### 15. [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:** 88

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Dataloop](https://www.g2.com/sellers/dataloop)
- **Year Founded:** 2017
- **HQ Location:** Herzliya, IL
- **LinkedIn® Page:** https://www.linkedin.com/company/dataloop (69 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Information Technology and Services
  - **Company Size:** 39% Mid-Market, 32% Small-Business


#### Pros & 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)

  ### 16. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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/ (28 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software, Research
  - **Company Size:** 63% Small-Business, 29% Enterprise


#### Pros & 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)

  ### 17. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Prolific](https://www.g2.com/sellers/prolific)
- **Company Website:** https://www.prolific.com/
- **Year Founded:** 2014
- **HQ Location:** London, England
- **Twitter:** @Prolific (13,484 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/5168486 (867 employees on LinkedIn®)

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


#### Pros & 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)

  ### 18. [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:** 19

**User Satisfaction Scores:**

- **Labeler Quality:** 9.4/10 (Category avg: 8.9/10)
- **Object Detection:** 9.0/10 (Category avg: 8.9/10)
- **Data Types:** 8.6/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.9/10 (Category avg: 8.8/10)


**Seller Details:**

- **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/ (101 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 58% Small-Business, 26% Mid-Market


#### Pros & 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)

  ### 19. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Shaip](https://www.g2.com/sellers/shaip)
- **Year Founded:** 2018
- **HQ Location:** Louisville, Kentucky
- **Twitter:** @weareShaip (228 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/66611098 (351 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 41% Enterprise, 36% Small-Business


  ### 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:** 4.5/5.0
  **Total Reviews:** 13

**User Satisfaction Scores:**

- **Labeler Quality:** 8.8/10 (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:** 8.8/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Alegion](https://www.g2.com/sellers/alegion)
- **Year Founded:** 2012
- **HQ Location:** Austin, US
- **Twitter:** @Alegion (2 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/2756641 (43 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 38% Small-Business, 31% Enterprise


#### Pros & 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)

  ### 21. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Hive.ai](https://www.g2.com/sellers/hive-ai)
- **Year Founded:** 2013
- **HQ Location:** San Francisco, California
- **Twitter:** @hive_ai (3,618 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/hiveai (510 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Company Size:** 50% Enterprise, 40% Small-Business


  ### 22. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [BasicAI](https://www.g2.com/sellers/basicai)
- **Year Founded:** 2019
- **HQ Location:** Irvine, CA
- **Twitter:** @BasicAIteam (92 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/basicaius/about/?viewAsMember=true (15 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services, Computer Software
  - **Company Size:** 44% Small-Business, 31% Mid-Market


  ### 23. [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

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **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®)

**Reviewer Demographics:**
  - **Top Industries:** Information Technology and Services
  - **Company Size:** 57% Small-Business, 38% Mid-Market


#### Pros & 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)

  ### 24. [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:** 21

**User Satisfaction Scores:**

- **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)


**Seller Details:**

- **Seller:** [Segments.ai](https://www.g2.com/sellers/segments-ai)
- **Year Founded:** 2020
- **HQ Location:** Leuven, Vlaams-Brabant, Belgium
- **Twitter:** @SegmentsAI (481 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/segmentsai/ (12 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Research
  - **Company Size:** 95% Small-Business, 5% Mid-Market


#### Pros & 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)

  ### 25. [Datasaur](https://www.g2.com/products/datasaur/reviews)
  Datasaur offers the most intuitive interface for all your Natural Language Processing related tasks.


  **Average Rating:** 4.5/5.0
  **Total Reviews:** 29

**User Satisfaction Scores:**

- **Labeler Quality:** 8.9/10 (Category avg: 8.9/10)
- **Object Detection:** 8.2/10 (Category avg: 8.9/10)
- **Data Types:** 8.3/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.8/10)


**Seller Details:**

- **Seller:** [Datasaur](https://www.g2.com/sellers/datasaur)
- **Year Founded:** 2019
- **HQ Location:** San Francisco Bay Area, California
- **Twitter:** @datasaurai (261 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/datasaur/ (67 employees on LinkedIn®)

**Reviewer Demographics:**
  - **Top Industries:** Computer Software
  - **Company Size:** 52% Mid-Market, 41% Small-Business




## Parent Category

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



## Related Categories

- [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)



---

## Buyer Guide

### 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)