# Best Data Labeling Software - Page 2

*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.






## G2 Grid® for Data Labeling Software
![G2 Grid® for Data Labeling Software plotting products by satisfaction and market presence](https://www.g2.com/categories/data-labeling/grids.png?focus%5B%5D=128515&focus%5B%5D=125020&focus%5B%5D=168222&focus%5B%5D=78925&focus%5B%5D=87452&focus%5B%5D=126287&focus%5B%5D=142739&focus%5B%5D=1563734)
Highlighted products: SuperAnnotate, Roboflow, Encord, Labelbox, Amazon Sagemaker Ground Truth, V7 Darwin, Sama, and Outlier AI.
Underlying data: [Grid® JSON](https://www.g2.com/categories/data-labeling/grids.json?focus%5B%5D=superannotate&focus%5B%5D=roboflow&focus%5B%5D=encord&focus%5B%5D=labelbox&focus%5B%5D=amazon-sagemaker-ground-truth&focus%5B%5D=v7-darwin&focus%5B%5D=sama&focus%5B%5D=outlier-ai-outlier-ai)


## How Many Data Labeling Software Products Does G2 Track?
**Total Products under this Category:** 111

### Category Stats (Jul 2026)
- **Average Rating**: 4.51/5 (↓0.01 vs Jun 2026) The average rating of products in this category, based on all submitted ratings
- **Top Trending Product**: Clarifai (+0.26%) - Among all products in this category, Clarifai recorded the largest rating increase compared to last month
*Last updated: July 12, 2026*


## How Does G2 Rank Data Labeling Software Products?

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

- 30 Analysts and Data Experts
- 1,800+ Authentic Reviews
- 111+ 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:** [Datasaur](https://www.g2.com/products/datasaur/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)


---

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---

## What Are the Top-Rated Data Labeling Software Products in 2026?
### 1. [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 organizing annotated datasets** with Segments.ai, enhancing efficiency and collaboration in data labeling.
- Users highlight the **efficiency of multi-sensor data labeling** in Segments.ai, enhancing their work with autonomy and robotics.
- Users value the **efficiency in creating organized datasets** with Segments.ai, significantly saving time and enhancing collaboration.
- Users value the **time-saving capabilities** of Segments.ai, facilitating quick and organized dataset annotation.
- Users appreciate the **annotation efficiency** of Segments.ai, finding the process quick and easy even with low resolution images.

**Cons:**

- Users find the **difficult learning curve** for annotating complex data can hinder their overall experience with Segments.ai.
- Users indicate that the **learning curve** for mastering the multi-sensor annotation tools can be steep and challenging.
- Users feel that **annotation issues** arise due to a lack of configuration options and automation clarity in Segments.ai.
- Users feel there is a **lack of features** , especially regarding advanced configurations and built-in automation options.
- Users feel a **lack of tools** hinders the full potential of advanced features and the annotation process.

#### 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)

---



### 2. [Text Classifier with auto Deep Learning](https://www.g2.com/products/text-classifier-with-auto-deep-learning/reviews)
This solution automatically identifies and trains the best performing deep learning model for text classification.


**Average Rating:** 4.4/5.0
**Total Reviews:** 13
**How Do G2 Users Rate Text Classifier with auto Deep Learning?**

- **Labeler Quality:** 9.7/10 (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 Text Classifier with auto Deep Learning?**

- **Seller:** [Mphasis](https://www.g2.com/sellers/mphasis-5a2b4772-cd1c-4cbd-bf88-54fc79a85d25)
- **Year Founded:** 2007
- **HQ Location:** Reston, VA
- **Twitter:** @Stelligent (1,106 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/220927 (14 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 46% Mid-Market, 38% Enterprise



#### What Are Recent G2 Reviews of Text Classifier with auto Deep Learning?

**"[Perfect AI technology for text analysing](https://www.g2.com/survey_responses/text-classifier-with-auto-deep-learning-review-7942001)"**

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

[Read full review](https://www.g2.com/survey_responses/text-classifier-with-auto-deep-learning-review-7942001)

---

**"[Awesome tool with excellent solutions!!](https://www.g2.com/survey_responses/text-classifier-with-auto-deep-learning-review-7815728)"**

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

[Read full review](https://www.g2.com/survey_responses/text-classifier-with-auto-deep-learning-review-7815728)

---


#### What Are G2 Users Discussing About Text Classifier with auto Deep Learning?

- [What is Text Classifier with auto Deep Learning used for?](https://www.g2.com/discussions/what-is-text-classifier-with-auto-deep-learning-used-for)

### 3. [UBIAI Text Annotation Tool](https://www.g2.com/products/ubiai-text-annotation-tool/reviews)
UBIAI makes easy-to-use NLP tools to help companies analyze and extract actionable insights from their unstructured data.


**Average Rating:** 4.8/5.0
**Total Reviews:** 17
**How Do G2 Users Rate UBIAI Text Annotation Tool?**

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

**Who Is the Company Behind UBIAI Text Annotation Tool?**

- **Seller:** [UBIAI](https://www.g2.com/sellers/ubiai)
- **Year Founded:** 2020
- **HQ Location:** Carlsbad, US
- **Twitter:** @UBIAI5 (127 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/ubiai/ (15 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 47% Small-Business, 35% Mid-Market



#### What Are Recent G2 Reviews of UBIAI Text Annotation Tool?

**"[Stand out annotation tool.](https://www.g2.com/survey_responses/ubiai-text-annotation-tool-review-8780821)"**

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

[Read full review](https://www.g2.com/survey_responses/ubiai-text-annotation-tool-review-8780821)

---

**"[Very convenient tool and amazingly responsive team](https://www.g2.com/survey_responses/ubiai-text-annotation-tool-review-7643347)"**

**Rating:** 4.5/5.0 stars
*— Yassin E.*

[Read full review](https://www.g2.com/survey_responses/ubiai-text-annotation-tool-review-7643347)

---


#### What Are G2 Users Discussing About UBIAI Text Annotation Tool?

- [What is UBIAI Text Annotation Tool used for?](https://www.g2.com/discussions/what-is-ubiai-text-annotation-tool-used-for)

### 4. [SUPA](https://www.g2.com/products/supa/reviews)
Supercharge your AI with human expertise. SUPA is here to help you streamline your data at any stage: collection, curation, annotation, model validation and human feedback. SUPA is trusted by AI teams to solve their human data needs. Our lightning-fast machine-led labeling platform integrates with our diverse workforce to provide high-quality data at scale, making it the most cost-efficient solution for your AI. Visit us at https://www.supa.so/


**Average Rating:** 4.9/5.0
**Total Reviews:** 11
**How Do G2 Users Rate SUPA?**

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

**Who Is the Company Behind SUPA?**

- **Seller:** [SUPA](https://www.g2.com/sellers/supa)
- **Year Founded:** 2014
- **HQ Location:** Damansara Heights, MY
- **Twitter:** @SUPABOLT (12 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/supa-ai/ (63 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 45% Mid-Market, 45% Small-Business



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

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

**Rating:** 5.0/5.0 stars
*— Verified User in Design*

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

---

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

**Rating:** 5.0/5.0 stars
*— Dominic  C.*

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

---



### 5. [LinkedAI](https://www.g2.com/products/linkedai/reviews)
Build better AI data faster! LinkedAI is a complete solution for taking control of your training data, with fast labeling tools, human workforce, data management, and automation features. An AI model is only as good as its Training Data. We provide an end-to-end solution for image annotation with fast labeling tools, synthetic data generation, data management, automation features and annotation services on-demand with integrated tooling to accelerate and finish computer vision projects. Our website is the best place to start, as it has a wealth of information that should be able to answer most of your questions. However, if you need further assistance, don't hesitate to reach out to us directly.


**Average Rating:** 4.6/5.0
**Total Reviews:** 20
**How Do G2 Users Rate LinkedAI?**

- **Labeler Quality:** 9.4/10 (Category avg: 8.9/10)
- **Object Detection:** 8.5/10 (Category avg: 8.9/10)
- **Data Types:** 8.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.7/10 (Category avg: 8.8/10)

**Who Is the Company Behind LinkedAI?**

- **Seller:** [LinkedAI](https://www.g2.com/sellers/linkedai)
- **Year Founded:** 2018
- **HQ Location:** Sunnyvale, CA
- **Twitter:** @LinkedAI (111 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/linked-ai/ (11 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 43% Mid-Market, 30% Small-Business



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

**"[Data annotating and modelling tool](https://www.g2.com/survey_responses/linkedai-review-7860091)"**

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

[Read full review](https://www.g2.com/survey_responses/linkedai-review-7860091)

---

**"[AI tool for easy data modelling](https://www.g2.com/survey_responses/linkedai-review-7872466)"**

**Rating:** 4.5/5.0 stars
*— deekshita s.*

[Read full review](https://www.g2.com/survey_responses/linkedai-review-7872466)

---


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

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

### 6. [super.AI](https://www.g2.com/products/super-ai/reviews)
Super.AI Intelligent Document Processing (IDP) extracts data from any document, ensuring seamless automation, reduced costs, and smarter decisions. 91-99%+ Accuracy $100M+ in Costs Saved 1M+Hours Saved Trusted by industry leaders like Bureau Veritas, Aldi, Accenture, Saint Gobain, Siemens, Nexi, Lano and more. Our AI automatically processes: Logistics Documents Airway Bill, Bills of Lading (BOLs), Delivery Notes, Packing Slips, and countless others. Operational & Production Documents Work Orders, Job Sheets, Production Schedules, Inventory Reports, etc. Contracts & Legal Agreements Supplier Contracts, Service Agreements, Customer Contracts, among others. Financial & Transactional Documents Invoices, Receipts, Purchase Orders, and more. Identity & Authorization Documents Employee IDs, Permits, and Access Forms, to name a few. Other Super.AI processes any document type, there’s no limit to what we can automate.


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

- **Labeler Quality:** 8.3/10 (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:** 8.1/10 (Category avg: 8.8/10)

**Who Is the Company Behind super.AI?**

- **Seller:** [super.AI](https://www.g2.com/sellers/super-ai)
- **Year Founded:** 2018
- **HQ Location:** Bellevue, Washington
- **Twitter:** @mysuperai (401 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/mysuperai/ (48 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market, 42% Small-Business


#### What Are super.AI's Pros and Cons?

**Pros:**

- Helpful (1 reviews)



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

**Pros:**

- Users find Super.AI **extremely helpful** in generating content and reducing human effort in various tasks.


#### What Are Recent G2 Reviews of super.AI?

**"[Amazing AI product for unstructured data](https://www.g2.com/survey_responses/super-ai-review-9514015)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Education Management*

[Read full review](https://www.g2.com/survey_responses/super-ai-review-9514015)

---

**"[New technology to digest](https://www.g2.com/survey_responses/super-ai-review-8011276)"**

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

[Read full review](https://www.g2.com/survey_responses/super-ai-review-8011276)

---


#### What Are G2 Users Discussing About super.AI?

- [What is Super AI used for?](https://www.g2.com/discussions/what-is-super-ai-used-for) - 1 comment

### 7. [Innotescus Video and Image Annotation Platform](https://www.g2.com/products/innotescus-video-and-image-annotation-platform/reviews)
Innotescus is a collaborative video and image annotation platform built to streamline Computer Vision development processes via seamless data handling, smart annotation tools, and intuitive collaboration features. Additionally, its data visualization tools and cross-functional collaboration features identify data bias early, improve data accuracy, and enable faster, cost-efficient deployment of high-performance Artificial Intelligence.


**Average Rating:** 4.8/5.0
**Total Reviews:** 10
**How Do G2 Users Rate Innotescus Video and Image Annotation Platform?**

- **Labeler Quality:** 8.3/10 (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:** 9.0/10 (Category avg: 8.8/10)

**Who Is the Company Behind Innotescus Video and Image Annotation Platform?**

- **Seller:** [Innotescus](https://www.g2.com/sellers/innotescus)
- **Year Founded:** 2018
- **HQ Location:** Pittsburgh
- **Twitter:** @innotescus (124 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/No-Linkedin-Presence-Added-Intentionally-By-DataOps (1 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 70% Small-Business, 10% Enterprise



#### What Are Recent G2 Reviews of Innotescus Video and Image Annotation Platform?

**"[A solid annotation tool for instance segmetation annotation](https://www.g2.com/survey_responses/innotescus-video-and-image-annotation-platform-review-5200689)"**

**Rating:** 5.0/5.0 stars
*— Thomas C.*

[Read full review](https://www.g2.com/survey_responses/innotescus-video-and-image-annotation-platform-review-5200689)

---

**"[image innotation is very powerful.less manual efforts](https://www.g2.com/survey_responses/innotescus-video-and-image-annotation-platform-review-6725642)"**

**Rating:** 4.5/5.0 stars
*— mona m.*

[Read full review](https://www.g2.com/survey_responses/innotescus-video-and-image-annotation-platform-review-6725642)

---


#### What Are G2 Users Discussing About Innotescus Video and Image Annotation Platform?

- [What is Innotescus Video and Image Annotation Platform used for?](https://www.g2.com/discussions/what-is-innotescus-video-and-image-annotation-platform-used-for)

### 8. [Swivl](https://www.g2.com/products/swivl/reviews)
The Only AI Assistant for Self Storage. 80% of your repetitive tasks on autopilot. swivl augments your existing team to understand what works and automatically tune property-level decisions every day to attract ready-to-lease customers. Our self storage AI assistant can answer common questions, identify and qualify new leads, and get them to the lease that meets their needs. Automation is the future of self storage. In the age of hyper-informed, store-anywhere consumer, your business needs to be always on, data driven, and customer-centered. Your renters demand it.


**Average Rating:** 4.2/5.0
**Total Reviews:** 16
**How Do G2 Users Rate Swivl?**

- **Labeler Quality:** 7.6/10 (Category avg: 8.9/10)
- **Object Detection:** 7.7/10 (Category avg: 8.9/10)
- **Data Types:** 7.9/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.0/10 (Category avg: 8.8/10)

**Who Is the Company Behind Swivl?**

- **Seller:** [Swivl](https://www.g2.com/sellers/swivl)
- **Year Founded:** 2018
- **HQ Location:** Atlanta, Georgia
- **Twitter:** @tryswivl (438 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/27000747 (17 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 88% Mid-Market, 19% Small-Business



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

**"[What a useful tool!](https://www.g2.com/survey_responses/swivl-review-8204683)"**

**Rating:** 4.0/5.0 stars
*— Trisha J.*

[Read full review](https://www.g2.com/survey_responses/swivl-review-8204683)

---

**"[Great working and interacting platform](https://www.g2.com/survey_responses/swivl-review-7953742)"**

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

[Read full review](https://www.g2.com/survey_responses/swivl-review-7953742)

---



### 9. [Jaxon.ai](https://www.g2.com/products/jaxon-ai/reviews)
Jaxon’s an AI platform that guides data science teams through the research-design-build process. It combines formal reasoning with an LLM-driven agent to ensure data science teams adhere to best practices. Jaxon explores tradeoffs using simulations to quickly figure out what will work best for each use case. Jaxon continuously improves by accumulating usage patterns and retraining itself on what works and when.


**Average Rating:** 4.5/5.0
**Total Reviews:** 12
**How Do G2 Users Rate Jaxon.ai?**

- **Labeler Quality:** 8.1/10 (Category avg: 8.9/10)
- **Object Detection:** 8.0/10 (Category avg: 8.9/10)
- **Data Types:** 8.1/10 (Category avg: 8.8/10)
- **Ease of Use:** 7.9/10 (Category avg: 8.8/10)

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

- **Seller:** [Jaxon.AI](https://www.g2.com/sellers/jaxon-ai)
- **Year Founded:** 2017
- **HQ Location:** Boston, US
- **LinkedIn® Page:** https://www.linkedin.com/company/27103003 (36 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 58% Small-Business, 25% Mid-Market



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

**"[A state of the Art Artificial Intelligence](https://www.g2.com/survey_responses/jaxon-ai-review-8549112)"**

**Rating:** 4.5/5.0 stars
*— Ar. Nilanjan P.*

[Read full review](https://www.g2.com/survey_responses/jaxon-ai-review-8549112)

---

**"[Generates Amazing Synthetic data that beats real data!](https://www.g2.com/survey_responses/jaxon-ai-review-8558570)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Information Technology and Services*

[Read full review](https://www.g2.com/survey_responses/jaxon-ai-review-8558570)

---



### 10. [Predictly](https://www.g2.com/products/predictly/reviews)
Predictly Tech Labs aims to enhance the usage and adoption of Artificial Intelligence technologies into different industries to experience its benefits in their products and services. For this reason, Predictly provides various kinds of services to their clients, such as data annotation, datasets, Pre-trained models, AI-transformation services.


**Average Rating:** 4.4/5.0
**Total Reviews:** 15
**How Do G2 Users Rate Predictly?**

- **Labeler Quality:** 8.6/10 (Category avg: 8.9/10)
- **Object Detection:** 6.7/10 (Category avg: 8.9/10)
- **Data Types:** 7.2/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.0/10 (Category avg: 8.8/10)

**Who Is the Company Behind Predictly?**

- **Seller:** [Predictly Tech Labs](https://www.g2.com/sellers/predictly-tech-labs)
- **Year Founded:** 2015
- **HQ Location:** Bangalore, IN
- **Twitter:** @prdictly (516 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/predictly-tech-labs/ (4 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 53% Enterprise, 33% Mid-Market



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

**"[Predictly is expemlary in performance!!!](https://www.g2.com/survey_responses/predictly-review-7713873)"**

**Rating:** 4.5/5.0 stars
*— Harshad V.*

[Read full review](https://www.g2.com/survey_responses/predictly-review-7713873)

---

**"[A great tool for creating ML model based solutions](https://www.g2.com/survey_responses/predictly-review-6964270)"**

**Rating:** 4.5/5.0 stars
*— Parasuram V.*

[Read full review](https://www.g2.com/survey_responses/predictly-review-6964270)

---



### 11. [iMerit Ango Hub Multimodal AI Platform](https://www.g2.com/products/imerit-ango-hub-multimodal-ai-platform/reviews)
For organizations driving advancements in traditional AI and generative AI, iMerit delivers comprehensive, software-delivered solutions that encompass high-quality data annotation, enrichment, and model fine-tuning across multimodal datasets, including text, images, audio, video, and sensor data. By combining state-of-the-art technology with human expertise, iMerit empowers businesses to develop precise AI models, from traditional supervised learning systems to cutting-edge generative AI applications. Unlike generic service providers, iMerit specializes in secure, scalable, and domain-specific solutions, enabling innovation and performance in the most demanding AI and machine learning initiatives. For developers of traditional AI applications iMerit provides best in class data annotation tooling, workflow automation and a highly skilled workforce within a single end-to-end solution. The unique combination of technology, talent and techniques produces the highest quality data in the industry for machine learning. For developers of Generative AI applications iMerit provides the tools, automation and domain experts for accurate model evaluation and fine turning. The solution combines the technology and human-in-the-loop domain experts for all forms of supervised reinforcement learning. Services include corpus creation, data augmentation, RLHF, RAG fine tuning, chain of thought reasoning and red-teaming for greater model precision. Visit www.imerit.net to learn more.


**Average Rating:** 4.8/5.0
**Total Reviews:** 11
**How Do G2 Users Rate iMerit Ango Hub Multimodal AI Platform?**

- **Labeler Quality:** 6.7/10 (Category avg: 8.9/10)
- **Object Detection:** 6.7/10 (Category avg: 8.9/10)
- **Data Types:** 6.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.8/10)

**Who Is the Company Behind iMerit Ango Hub Multimodal AI Platform?**

- **Seller:** [iMerit Technology](https://www.g2.com/sellers/imerit-technology)
- **Year Founded:** 2012
- **HQ Location:** San Jose, US
- **Twitter:** @iMeritDigital (1,654 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/imerit (6,415 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Small-Business, 25% Enterprise


#### What Are iMerit Ango Hub Multimodal AI Platform's Pros and Cons?

**Pros:**

- AI Integration (1 reviews)
- Annotation Efficiency (1 reviews)
- Customization (1 reviews)
- Data Accuracy (1 reviews)
- Machine Learning (1 reviews)

**Cons:**

- Complexity (1 reviews)
- Steep Learning Curve (1 reviews)


### What Do G2 Reviewers Say About iMerit Ango Hub Multimodal AI Platform?
*AI-generated summary from verified user reviews*

**Pros:**

- Users value the **AI integration** of Ango Hub, enhancing their capacity to manage complex data effectively.
- Users appreciate the **annotation efficiency** of iMerit Ango Hub, enhancing high-quality data management for complex AI projects.
- Users value the **customization capabilities** of iMerit Ango Hub, enhancing workflow management for complex AI/ML projects.
- Users value the **high-quality data accuracy** of iMerit Ango Hub, essential for complex AI/ML projects and workflow management.
- Users value the **effective machine learning capabilities** of Ango Hub for managing complex AI/ML annotation tasks seamlessly.

**Cons:**

- Users note that the **platform's complexity** may necessitate extensive training for effective utilization.
- Users find the **steep learning curve** of iMerit Ango Hub necessitates significant training to navigate effectively.

#### What Are Recent G2 Reviews of iMerit Ango Hub Multimodal AI Platform?

**"[Performant for video-based tasks](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-5412382)"**

**Rating:** 5.0/5.0 stars
*— Ezra O.*

[Read full review](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-5412382)

---

**"[Easy to use. Very intuitive](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-10867815)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Automotive*

[Read full review](https://www.g2.com/survey_responses/imerit-ango-hub-multimodal-ai-platform-review-10867815)

---


#### What Are G2 Users Discussing About iMerit Ango Hub Multimodal AI Platform?

- [What is Ango Hub used for?](https://www.g2.com/discussions/what-is-ango-hub-used-for)

### 12. [TrainingData.io](https://www.g2.com/products/trainingdata-io/reviews)
Model assisted image and video training data labeling for radiology, pathology and other forms of medical data used for building machine learning models. The #1 tool trusted by medical companies, research scientists and technicians.


**Average Rating:** 4.1/5.0
**Total Reviews:** 10
**How Do G2 Users Rate TrainingData.io?**

- **Labeler Quality:** 7.8/10 (Category avg: 8.9/10)
- **Object Detection:** 9.0/10 (Category avg: 8.9/10)
- **Data Types:** 8.3/10 (Category avg: 8.8/10)
- **Ease of Use:** 7.1/10 (Category avg: 8.8/10)

**Who Is the Company Behind TrainingData.io?**

- **Seller:** [TrainingData.io](https://www.g2.com/sellers/trainingdata-io)
- **Year Founded:** 2021
- **HQ Location:** Palo Alto, California
- **Twitter:** @TrainingDataIO (5 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/14390321 (4 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 60% Small-Business, 30% Mid-Market



#### What Are Recent G2 Reviews of TrainingData.io?

**"[" SaaS Solution Designed for Data Labeling"](https://www.g2.com/survey_responses/trainingdata-io-review-8821931)"**

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

[Read full review](https://www.g2.com/survey_responses/trainingdata-io-review-8821931)

---

**"[Reliable platform for AI data labeling](https://www.g2.com/survey_responses/trainingdata-io-review-8637525)"**

**Rating:** 4.0/5.0 stars
*— virender s.*

[Read full review](https://www.g2.com/survey_responses/trainingdata-io-review-8637525)

---


#### What Are G2 Users Discussing About TrainingData.io?

- [What is TrainingData.io used for?](https://www.g2.com/discussions/what-is-trainingdata-io-used-for)

### 13. [Supervisely Computer Vision Platform](https://www.g2.com/products/supervisely-computer-vision-platform/reviews)
Supervisely Enterprise is fully self-hosted and cloud frendly: install it on your servers or in the cloud, keep everything private. We provide API, SDK and backend source codes. So it is highly customizable and can be integrated into any technology stack.


**Average Rating:** 4.7/5.0
**Total Reviews:** 9
**How Do G2 Users Rate Supervisely Computer Vision Platform?**

- **Labeler Quality:** 9.0/10 (Category avg: 8.9/10)
- **Object Detection:** 8.3/10 (Category avg: 8.9/10)
- **Data Types:** 9.3/10 (Category avg: 8.8/10)
- **Ease of Use:** 10.0/10 (Category avg: 8.8/10)

**Who Is the Company Behind Supervisely Computer Vision Platform?**

- **Seller:** [Supervisely](https://www.g2.com/sellers/supervisely)
- **Year Founded:** 2017
- **HQ Location:** Tallinn, Harjumaa
- **Twitter:** @supervisely_ai (120 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/18496236 (12 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Information Technology and Services
- **Company Size:** 40% Small-Business, 40% Mid-Market



#### What Are Recent G2 Reviews of Supervisely Computer Vision Platform?

**"[Supervisely Computer Vision Platform is by far one one of the best programs of this type.](https://www.g2.com/survey_responses/supervisely-computer-vision-platform-review-6982057)"**

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

[Read full review](https://www.g2.com/survey_responses/supervisely-computer-vision-platform-review-6982057)

---

**"[It is quite pleasing to see the final result and admire the impact it generates when reproducing.](https://www.g2.com/survey_responses/supervisely-computer-vision-platform-review-6971491)"**

**Rating:** 5.0/5.0 stars
*— David R.*

[Read full review](https://www.g2.com/survey_responses/supervisely-computer-vision-platform-review-6971491)

---


#### What Are G2 Users Discussing About Supervisely Computer Vision Platform?

- [What is Supervisely Computer Vision Platform used for?](https://www.g2.com/discussions/what-is-supervisely-computer-vision-platform-used-for)

### 14. [Superb AI Suite](https://www.g2.com/products/superb-ai-suite/reviews)
Superb AI provides the most advanced computer vision platform that makes data preparation, curation and model deployment faster and easier than ever before. Specializing in adaptable automation for labeling, curation and model diagnosis, our solutions help companies drastically reduce the time and cost of building and deploying computer vision models.


**Average Rating:** 4.7/5.0
**Total Reviews:** 6
**How Do G2 Users Rate Superb AI Suite?**

- **Labeler Quality:** 9.7/10 (Category avg: 8.9/10)
- **Object Detection:** 9.0/10 (Category avg: 8.9/10)
- **Data Types:** 9.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.3/10 (Category avg: 8.8/10)

**Who Is the Company Behind Superb AI Suite?**

- **Seller:** [Superb AI, Inc](https://www.g2.com/sellers/superb-ai-inc)
- **Year Founded:** 2018
- **HQ Location:** San Mateo , US
- **Twitter:** @superb_hq (419 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/superb-ai (57 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market, 50% Small-Business



#### What Are Recent G2 Reviews of Superb AI Suite?

**"[The most unique, versatile and complete annotation platform I've ever used!](https://www.g2.com/survey_responses/superb-ai-suite-review-9588768)"**

**Rating:** 5.0/5.0 stars
*— Roy O.*

[Read full review](https://www.g2.com/survey_responses/superb-ai-suite-review-9588768)

---

**"[Great platform to serve a variety of Data Annotation needs and excellent team to cooperate with](https://www.g2.com/survey_responses/superb-ai-suite-review-9222068)"**

**Rating:** 4.5/5.0 stars
*— Yana B.*

[Read full review](https://www.g2.com/survey_responses/superb-ai-suite-review-9222068)

---



### 15. [M47 AI](https://www.g2.com/products/m47-ai/reviews)
M47 AI is a powerful AI Data Training platform for Natural Language Processing projects. It is designed to simplify, speed up and consolidate the dataset lifecycle for Machine Learning and NLP based applications. Create the Training Data for your NLP model in the cloud and easily internationalize your ML language model for multiple countries and use cases. Use the cloud platform with your own team or use our international team of NLP engineers and professional annotators covering more than 40 languages. With M47 AI, you can: - Create, manage and upload datasets and perform ML-driven quality assurance checks - Annotate text, audios, documents & images to create Training Data for more than 20 different customized business cases. - Request on demand Data and Model Training services in more than 40 languages - Use state-of-the-art AI models to detect and clean ML bias - Use our model hub to connect to Hugging Face and test and run the latest ML models or import your own. - Monitor metrics & performance on every step in the training process - Manage internal and external teams, track progress and consolidate dataset generation efforts - Custom build APIs to easily integrate with your existing data pipeline and MLOps Try out now for free our AI Data Platform.


**Average Rating:** 4.8/5.0
**Total Reviews:** 5
**How Do G2 Users Rate M47 AI?**

- **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:** 10.0/10 (Category avg: 8.8/10)

**Who Is the Company Behind M47 AI?**

- **Seller:** [M47 Labs](https://www.g2.com/sellers/m47-labs)
- **Year Founded:** 2018
- **HQ Location:** Barcelona, ES
- **Twitter:** @M47Labs (65 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/m47-labs/ (190 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 40% Mid-Market, 40% Small-Business



#### What Are Recent G2 Reviews of M47 AI?

**"[Great multi-project tool that allows using different LLM models](https://www.g2.com/survey_responses/m47-ai-review-7839460)"**

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

[Read full review](https://www.g2.com/survey_responses/m47-ai-review-7839460)

---

**"[Very useful and intuitive platform](https://www.g2.com/survey_responses/m47-ai-review-7670696)"**

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

[Read full review](https://www.g2.com/survey_responses/m47-ai-review-7670696)

---



### 16. [Diffgram Training Data Software](https://www.g2.com/products/diffgram-training-data-software/reviews)
Standard, Safe, Flexible AI Data Annotation, Catalog, & Workflow


**Average Rating:** 5.0/5.0
**Total Reviews:** 4
**How Do G2 Users Rate Diffgram Training Data Software?**

- **Labeler Quality:** 9.4/10 (Category avg: 8.9/10)
- **Object Detection:** 10.0/10 (Category avg: 8.9/10)
- **Data Types:** 9.4/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.6/10 (Category avg: 8.8/10)

**Who Is the Company Behind Diffgram Training Data Software?**

- **Seller:** [Diffgram](https://www.g2.com/sellers/diffgram)
- **Year Founded:** 2018
- **HQ Location:** N/A
- **Twitter:** @diffgram (91 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/diffgram (2 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market, 50% Small-Business



#### What Are Recent G2 Reviews of Diffgram Training Data Software?

**"[User Centered Purpose Driven Product](https://www.g2.com/survey_responses/diffgram-training-data-software-review-8211714)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Computer & Network Security*

[Read full review](https://www.g2.com/survey_responses/diffgram-training-data-software-review-8211714)

---

**"[Helpful and User Friendly](https://www.g2.com/survey_responses/diffgram-training-data-software-review-7391678)"**

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

[Read full review](https://www.g2.com/survey_responses/diffgram-training-data-software-review-7391678)

---


#### What Are G2 Users Discussing About Diffgram Training Data Software?

- [What is Diffgram Training Data Software used for?](https://www.g2.com/discussions/what-is-diffgram-training-data-software-used-for)

### 17. [DagsHub](https://www.g2.com/products/dagshub/reviews)
DagsHub is a platform that allows you to easily create high-quality datasets for better model performance A single AI platform to curate vision, audio, and document data - automate labeling workflows, and evaluate models. Enterprises with sensitive data, can run on their own infrastructure on-prem and get a full AI platform. Data curation - create the very best datasets. Data annotation - annotate your vision, audio, and document data. Auto labeling - automate your annotation flow with pre-built templates and active learning. Data versioning - version your datasets for reproducibility. Experiment tracking - track your experiment progress, understand trends, and compare results. Model registry - manage your models and deployments in one place. The top data scientists build AI with DagsHub including teams at: Google, Harvard Medicine, Beewise, Macso, and Mana.bio


**Average Rating:** 4.8/5.0
**Total Reviews:** 14
**How Do G2 Users Rate DagsHub?**

- **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:** 9.2/10 (Category avg: 8.8/10)

**Who Is the Company Behind DagsHub?**

- **Seller:** [DagsHub](https://www.g2.com/sellers/dagshub)
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/dagshub (12 employees on LinkedIn®)

**Who Uses This Product?**
- **Top Industries:** Computer Software
- **Company Size:** 50% Small-Business, 43% Mid-Market


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

**Pros:**

- Data Management (12 reviews)
- Model Management (12 reviews)
- Collaboration (11 reviews)
- Features (10 reviews)
- Integrated Platform (10 reviews)

**Cons:**

- Limited Functionality (2 reviews)
- Error Handling (1 reviews)
- Expensive (1 reviews)
- Limited Customization (1 reviews)
- Limited Free Access (1 reviews)


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

**Pros:**

- Users appreciate the **effective data management** of DagsHub, enhancing reproducibility and collaboration in machine learning projects.
- Users value the **integrated model management** of DagsHub, which enhances productivity and collaboration across teams.
- Users value the **seamless collaboration** features of DagsHub, enhancing productivity across teams while managing complex data workflows.
- Users commend DagsHub for its **seamless integration of data, experiments, and models** , enhancing productivity and collaboration in ML projects.
- Users value the **integrated platform** of DagsHub, enhancing productivity and collaboration in managing data and experiments.

**Cons:**

- Users are frustrated by the **limited functionality** of DagsHub, particularly regarding collaboration restrictions on the free plan.
- Users often face **errors when pushing files** to DagsHub, impacting their workflow and project management.
- Users feel DagsHub is **expensive** due to limited free plan options and access to the full version.
- Users are frustrated by the **limited customization options** in DagsHub, particularly on the free plan for teams.
- Users find the **strict limitations of the free plan** frustrating, especially with team size constraints.

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

**"[Simplifies LLM Dataset Versioning and Experiment Tracking](https://www.g2.com/survey_responses/dagshub-review-11144209)"**

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

[Read full review](https://www.g2.com/survey_responses/dagshub-review-11144209)

---

**"[Reliable Infrastructure for LLM Data and Model Iteration](https://www.g2.com/survey_responses/dagshub-review-11087413)"**

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

[Read full review](https://www.g2.com/survey_responses/dagshub-review-11087413)

---



### 18. [KeyLabs](https://www.g2.com/products/keylabs/reviews)
Keylabs is a state-of-the-art labeling platform for images and videos that boosts up the process of preparing visual data for machine learning. Our annotation platform is built with user in mind. Just a couple of clicks and you are ready to go. Supported annotation types: - cuboid - bounding box axes aligned oriented - segmentation polygon bit mask - lines and multilines - named points points skeleton mesh Watch your project come to life. Easily convert your data to JSON.


**Average Rating:** 4.8/5.0
**Total Reviews:** 3
**How Do G2 Users Rate KeyLabs?**

- **Labeler Quality:** 9.2/10 (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:** 9.2/10 (Category avg: 8.8/10)

**Who Is the Company Behind KeyLabs?**

- **Seller:** [Keylabs](https://www.g2.com/sellers/keylabs)
- **HQ Location:** Holon, IL
- **Twitter:** @KeylabsA (47 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/keylabsai/ (8 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 33% Mid-Market, 33% Small-Business



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

**"[Data team manager](https://www.g2.com/survey_responses/keylabs-review-6862643)"**

**Rating:** 4.5/5.0 stars
*— Yalon T.*

[Read full review](https://www.g2.com/survey_responses/keylabs-review-6862643)

---

**"[Great platform for image and video annotation](https://www.g2.com/survey_responses/keylabs-review-6656390)"**

**Rating:** 5.0/5.0 stars
*— Inna N.*

[Read full review](https://www.g2.com/survey_responses/keylabs-review-6656390)

---



### 19. [SentiSight.ai](https://www.g2.com/products/sentisight-ai/reviews)
SentiSight.ai is a web-based platform that can be used for image labeling and for developing AI-based image recognition applications. It has two major goals: the first is to make the image annotation task as convenient and efficient as possible, even for large projects with many people working on image labeling, and the second is to provide a smooth and user-friendly interface for training and deploying deep neural network models. The ability to perform both of these tasks on the same platform provides the advantage of being able to label images and then train and improve models in an iterative way. SentiSight.ai offers powerful features, such as: Image labeling. Our labeling tool allows adding classification labels, bounding boxes, polygons, points, polylines, and bitmaps. Bitmaps can be easily converted to polygons and vice versa. Moreover, each labeled object can have several child objects, such as key-points or attributes. The labeled images can be directly used for model training on the SentiSight.ai platform, or they can be downloaded and used for in-house model training. Smart labeling tool. This tool can be used to significantly increase the speed of bitmap labeling. The smart labeling tool allows users to select a few points in the foreground and the background and let the AI extract the labeled object. Shared labeling projects and time tracking. To make large annotation project handling easier, SentiSight.ai allows a project to be shared among multiple users so that multiple people can label images in the same project. The project manager can quickly filter and review the images labeled by a particular project member, track each person’s progress and time spent on labeling, as well as manage user roles and permissions. Classification model training. This type of model can be used to identify certain objects in an image, such as a cat or a dog, but without specifying their location. They can also be trained to identify more abstract concepts, such as “summer” or “winter”. Object detection model training. This type of model can be used not to only identify a certain object, but also to predict its exact location in an image. For each object predicted to be inside the image, the model also predicts a rectangular bounding box that denotes the object’s location. This is very useful when you need to know not only what is inside the image, but also the relative location and number of objects. Online and offline models (free 30-day trial available). SentiSight.ai offers a possibility to use your deep learning models both online and offline. Online models can be used via REST API or web interface. Both of these options require internet connection. Another option is to download and use the image recognition model offline. An offline model can be downloaded as a free 30-day trial after which the user has an option to buy a license. The price of the license depends on the speed of the model, and it is a single time payment. Pre-trained models. In addition to the possibility of training image recognition models yourself, SentiSight.ai also provides several pre-trained models that can be used out-of-the-box without any additional training. These pre-trained models can be used for several tasks, such as content moderation, goods classification, automatic hashtags, people counting and more. Image Similarity search. This is another ready-to-use feature that allows users to upload an image and find all similar images to this query in their data set. It also allows users to perform NvN similarity searches in their data set where all similar image pairs are retrieved.


**Average Rating:** 4.8/5.0
**Total Reviews:** 3
**How Do G2 Users Rate SentiSight.ai?**

- **Ease of Use:** 8.3/10 (Category avg: 8.8/10)

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

- **Seller:** [NeuroTechnology](https://www.g2.com/sellers/neurotechnology)
- **Year Founded:** 1990
- **HQ Location:** Vilnius, LT
- **Twitter:** @StockGeist (273 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/neurotechnology/ (89 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 67% Small-Business



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

**"[Useful API for integration](https://www.g2.com/survey_responses/sentisight-ai-review-7365033)"**

**Rating:** 5.0/5.0 stars
*— Sam C.*

[Read full review](https://www.g2.com/survey_responses/sentisight-ai-review-7365033)

---

**"[An advanced image labeling platform that is easy to navigate and use](https://www.g2.com/survey_responses/sentisight-ai-review-7329240)"**

**Rating:** 5.0/5.0 stars
*— Verified User in Retail*

[Read full review](https://www.g2.com/survey_responses/sentisight-ai-review-7329240)

---



### 20. [CrowdAI](https://www.g2.com/products/crowdai/reviews)
Everything you need to go from pixels to value


**Average Rating:** 4.3/5.0
**Total Reviews:** 2
**How Do G2 Users Rate CrowdAI?**

- **Labeler Quality:** 10.0/10 (Category avg: 8.9/10)
- **Object Detection:** 8.3/10 (Category avg: 8.9/10)
- **Data Types:** 10.0/10 (Category avg: 8.8/10)
- **Ease of Use:** 9.2/10 (Category avg: 8.8/10)

**Who Is the Company Behind CrowdAI?**

- **Seller:** [CrowdAI](https://www.g2.com/sellers/crowdai)
- **Year Founded:** 2016
- **HQ Location:** San Francisco, US
- **Twitter:** @CrowdAIinc (261 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/crowdai/ (7 employees on LinkedIn®)

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



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

**"[Exploring Crowd.AI](https://www.g2.com/survey_responses/crowdai-review-8769301)"**

**Rating:** 4.0/5.0 stars
*— Harsha T.*

[Read full review](https://www.g2.com/survey_responses/crowdai-review-8769301)

---

**"[Crowd AI - The most powerful AI tool](https://www.g2.com/survey_responses/crowdai-review-8785253)"**

**Rating:** 4.5/5.0 stars
*— varsha n.*

[Read full review](https://www.g2.com/survey_responses/crowdai-review-8785253)

---



### 21. [Label Studio](https://www.g2.com/products/label-studio/reviews)
Label Studio Enterprise enables you to deliver the highest-quality ML/AI models faster. Evaluate model outputs and label high-quality datasets to train and fine-tune models that are aligned, accurate and compliant. The leading data science organizations choose Label Studio Enterprise due to its: - Accuracy: end-to-end quality review workflows, including auto validators, inter-annotator consensus scoring & quality reporting - Speed: Leverage auto-labeling, AI-assisted labeling & automation to optimize annotation workflows -Flexibility: Supports all data types & model integrations with an intuitive API and SDK for extensibility -Security & compliance: SOC2 & HIPAA certified, SSO/LDAP/SAML, role-based access control, and audit logs for on-prem or SaaS deployments - Open source community: Backed by the largest open source project & community focused on high-quality data for ML/AI


**Average Rating:** 3.5/5.0
**Total Reviews:** 2
**How Do G2 Users Rate Label Studio?**

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

**Who Is the Company Behind Label Studio?**

- **Seller:** [HumanSignal](https://www.g2.com/sellers/humansignal)
- **Year Founded:** 2019
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** https://www.linkedin.com/company/humansignal/ (51 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 100% Enterprise, 100% Small-Business




#### What Are G2 Users Discussing About Label Studio?

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

### 22. [NLP Lab](https://www.g2.com/products/nlp-lab/reviews)
NLP Lab (previously known as Annotation Lab) is a Free End-to-End No-Code platform for document labeling and AI/ML model training. It enables domain experts - nurses, doctors, lawyers, accountants, investors, etc. – to extract meaningful facts from text documents, images or PDFs and train models that will automatically predict those facts on new documents. This is done by using state-of-the-art Spark NLP and Spark OCR pre-trained models or by tuning models to better handle specific use cases. John Snow Labs’ NLP Lab supports the end-to-end process from starting an annotation project to the deployment of a trained model, all without writing a line of code. Based on an auto-scaling architecture powered by Kubernetes, it can scale to many teams and projects. Enterprise-grade security is provided for free including support for air-gap environments, zero data sharing, role-based access, full audit trails, MFA, and identity provider integrations. It allows powerful experiments for model training and finetuning, model testing, and model deployment as API endpoints.


**Average Rating:** 4.0/5.0
**Total Reviews:** 2
**How Do G2 Users Rate NLP Lab?**

- **Labeler Quality:** 8.3/10 (Category avg: 8.9/10)
- **Object Detection:** 8.3/10 (Category avg: 8.9/10)
- **Data Types:** 6.7/10 (Category avg: 8.8/10)
- **Ease of Use:** 8.3/10 (Category avg: 8.8/10)

**Who Is the Company Behind NLP Lab?**

- **Seller:** [John Snow Labs](https://www.g2.com/sellers/john-snow-labs)
- **Year Founded:** 2015
- **HQ Location:** Lewes, US
- **Twitter:** @JohnSnowLabs (44,139 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/johnsnowlabs (98 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 50% Mid-Market, 50% Small-Business



#### What Are Recent G2 Reviews of NLP Lab?

**"[Great annotation tool](https://www.g2.com/survey_responses/nlp-lab-review-7171774)"**

**Rating:** 4.5/5.0 stars
*— Eric L.*

[Read full review](https://www.g2.com/survey_responses/nlp-lab-review-7171774)

---



### 23. [Plainsight](https://www.g2.com/products/plainsight/reviews)
Plainsight is a Vision AI platform that helps enterprises turn images and video into actionable operational intelligence. The Plainsight Platform simplifies the full computer vision lifecycle, from data collection and model training to deployment, monitoring, and continuous improvement. With Plainsight, teams can build, manage, and scale custom computer vision solutions that improve visibility, automate manual processes, reduce risk, and uncover insights from real-world environments. Plainsight is designed for enterprises that need production-ready Vision AI across industries such as restaurants, retail, manufacturing, logistics, and other operationally complex environments.For more information, visit https://plainsight.ai.


**Average Rating:** 4.5/5.0
**Total Reviews:** 5
**How Do G2 Users Rate Plainsight?**

- **Ease of Use:** 10.0/10 (Category avg: 8.8/10)

**Who Is the Company Behind Plainsight?**

- **Seller:** [Plainsight](https://www.g2.com/sellers/plainsight)
- **Year Founded:** 2024
- **HQ Location:** Greater Seattle Area, US
- **Twitter:** @PlainsightAI (1,456 Twitter followers)
- **LinkedIn® Page:** https://www.linkedin.com/company/plainsightai/ (22 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 80% Small-Business, 20% Mid-Market


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

**Pros:**

- AI Capabilities (1 reviews)
- AI Integration (1 reviews)
- AI Modeling (1 reviews)
- AI Technology (1 reviews)
- Innovation (1 reviews)

**Cons:**

- Required Expertise (1 reviews)
- Required Knowledge (1 reviews)


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

**Pros:**

- Users value the **no-code and low-code options** of Plainsight, enhancing accessibility for AI vision implementation.
- Users appreciate the **no-code and low-code options** , making AI vision accessible and user-friendly for all.
- Users find the **no-code and low-code options** of Plainsight make AI vision more accessible and user-friendly.
- Users love the **no-code and low-code options** , making AI vision accessible for everyone.
- Users value the **no-code and low-code options** of Plainsight, enhancing accessibility to AI vision technology.

**Cons:**

- Users find that some **advanced features** of Plainsight demand technical expertise despite no-code options.
- Users find that some **advanced features require technical knowledge** , despite the availability of no-code/low-code options.

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

**"[Simplifying AI Vision with Powerful End-to-End Solutions](https://www.g2.com/survey_responses/plainsight-review-10956680)"**

**Rating:** 4.5/5.0 stars
*— Ajit S.*

[Read full review](https://www.g2.com/survey_responses/plainsight-review-10956680)

---

**"[An end-to-end innovative AI solution](https://www.g2.com/survey_responses/plainsight-review-9581873)"**

**Rating:** 5.0/5.0 stars
*— Nimish G.*

[Read full review](https://www.g2.com/survey_responses/plainsight-review-9581873)

---


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

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

### 24. [Acodis](https://www.g2.com/products/acodis/reviews)
Acodis has been pioneering document data automation since its founding in 2016. Today, global industry leaders in Life Sciences use Acodis to accelerate to accelerate their go-to-market motions in Quality and Regulatory. By automating repetitive document-based processes, Acodis decreases manual workload, increases data quality and enables many automation, genAI, and analytical use-cases. For instance, Acodis can turn clinical studies and certificates of analysis into structured and validated data, or automate the review of Batch Record documents. The solutions are based on one configurable platform which can absorb diverse inputs (pdfs, scans, xls, etc.), turn these documents into machine-readable data and take specific actions (extracting values, checking signatures, checking process steps, etc.). Powered by proprietary machine learning algorithm (e.g. GxP suitable), the solution is made available in dedicated instances in a secure cloud setup. Acodis can process any document type in any language and seamlessly integrates with your systems. You can easily export your data from Acodis via API to feed and enhance your ERP, CRM, DMS, RIM system of choice, including a standard integration in Veeva.


**Average Rating:** 4.8/5.0
**Total Reviews:** 28
**How Do G2 Users Rate Acodis?**

- **Ease of Use:** 9.1/10 (Category avg: 8.8/10)

**Who Is the Company Behind Acodis?**

- **Seller:** [Acodis](https://www.g2.com/sellers/acodis)
- **Company Website:** https://www.acodis.io/
- **Year Founded:** 2016
- **HQ Location:** Winterthur, CH
- **Twitter:** @acodis
- **LinkedIn® Page:** https://www.linkedin.com/company/acodis-i-o/ (26 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 45% Enterprise, 31% Mid-Market


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

**Pros:**

- Customer Support (9 reviews)
- Ease of Use (8 reviews)
- Features (5 reviews)
- Data Capture (4 reviews)
- Data Extraction (4 reviews)

**Cons:**

- OCR Issues (2 reviews)
- Technical Issues (2 reviews)
- Communication Issues (1 reviews)
- Complexity (1 reviews)
- Data Inaccuracy (1 reviews)


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

**Pros:**

- Users appreciate the **responsive customer support** of Acodis, enhancing their experience with timely and effective assistance.
- Users find Acodis extremely user-friendly, highlighting its **ease of use in mapping diverse formats seamlessly**.
- Users appreciate the **quick training with minimal documents** , enabling fast usability and valuable results with Acodis.
- Users appreciate the **responsive and proactive team** of Acodis, ensuring personalized support and efficient document processing.
- Users commend Acodis for its **easy data extraction capabilities** , enabling efficient processing of complex documents effortlessly.

**Cons:**

- Users face **OCR issues** with Acodis, particularly with table data extraction and document allocation inconsistencies.
- Users face **technical issues** with document allocation and processing structured data, leading to reliability concerns and minor bugs.
- Users experience **communication issues** with Acodis, suggesting a need for more proactive user engagement and clearer processes.
- Users find the **complexity of features** in Acodis to be overwhelming, which can hinder initial usability.
- Users experience significant **data inaccuracies** in Acodis, leading to challenges in document processing and workflow management.

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

**"[High-Performance Document Research with Superior Support](https://www.g2.com/survey_responses/acodis-review-12645844)"**

**Rating:** 4.5/5.0 stars
*— Anja W.*

[Read full review](https://www.g2.com/survey_responses/acodis-review-12645844)

---

**"[Overall, a very professional and pleasant service](https://www.g2.com/survey_responses/acodis-review-11881995)"**

**Rating:** 4.5/5.0 stars
*— Verified User in Chemicals*

[Read full review](https://www.g2.com/survey_responses/acodis-review-11881995)

---


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

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

### 25. [Avala](https://www.g2.com/products/avala/reviews)
Avala provides more accurately labeled AI data faster, with minimal setup and training time. Avala's comprehensive, open platform caters to the entire AI Ops workflow, combining dataset curation and management, world-class expertise for data labeling and human feedback, and model training, verification, and deployment. - Curate, label, and deploy your datasets and models 10x faster. - Audit models with ease, with intuitive data visualization and management - Drag and drop annotation project builder with built in training material Avala provides ethical and equitable data labeling without sacrificing quality or security. Pioneering a radically different approach to ethical AI deployment, revolutionizing how people can contribute to, develop, and benefit from AI with a collaborative marketplace of datasets, labelers, and models in an ecosystem of products and services that directly address the challenges of AI alignment. Avala offers a unique 'manufacturing pipeline' approach to labeling: - Divides labeling tasks into smaller, simpler pieces, allowing labelers to become expert in each task more quickly. - Saves ML engineers hundreds of hours of effort in developing training materials per labeling project. - Delivers the fastest, most accurate data labeling with reduced algorithmic bias and improved data quality


**Average Rating:** 4.0/5.0
**Total Reviews:** 1
**How Do G2 Users Rate Avala?**

- **Labeler Quality:** 6.7/10 (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:** 6.7/10 (Category avg: 8.8/10)

**Who Is the Company Behind Avala?**

- **Seller:** [Avala AI](https://www.g2.com/sellers/avala-ai)
- **Year Founded:** 2020
- **HQ Location:** San Francisco, US
- **LinkedIn® Page:** http://www.linkedin.com/company/avala-ai (84 employees on LinkedIn®)

**Who Uses This Product?**
- **Company Size:** 100% Mid-Market



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

**"[Best labelling for Point cloud](https://www.g2.com/survey_responses/avala-review-9694897)"**

**Rating:** 4.0/5.0 stars
*— Verified User in Research*

[Read full review](https://www.g2.com/survey_responses/avala-review-9694897)

---




## 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)



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## 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.