10 Best Data Labeling Software With G2 User Reviews

As the prominence of AI grows, it is being commercialized at a lightning-fast speed.

But why do businesses still fail to develop and prototype AI models? The main challenges are centered around end-to-end data management, data validation, and prediction accuracy. 

This happens because businesses fail to collect data samples from heterogeneous data sets and label them with complete precision.  Having a data labeling platform in place allows you to label data efficiently, build robust ML models, and improve auto-assist qualities in AI models.

I recently teamed up with Matthew Miller, the Principal Analyst for Artificial Intelligence and Machine Learning at G2, to analyze the data labeling platforms in more detail. We tried and tested around 20+ data labeling platforms based on features, pros and cons, and pricing. Based on our evaluation, we came up with this list of the top 10 vendors.

How did we select and evaluate the best data labeling platform?

In general, at G2, we rank data labeling tools using a proprietary algorithm that considers customer satisfaction and market presence based on authentic user reviews. Our tenured troupe of market research analysts and writers (Matthew and I, in this case) spend weeks testing solutions against multiple criteria set for a software category. We give you unbiased software evaluations. We don’t accept payment or exchange links for product placements on our list. Please read our G2 Research Scoring Methodology for more details.

1. SuperAnnotate: best known for data quality

SuperAnnotate offers services such as human-in-the-loop (HITL), automated data management, model generation, and model versioning, ensuring businesses can build their machine-learning application with maximum security. It also provides application programming interface  (API) integration to connect a data studio with enterprise resource planning (ERP) software to run machine learning projects in a remote setting.

Matthew and I liked how SuperAnnotate offers pre-labeling for image segmentation, object detection, and object tracking. It offers a variety of ML integrations and data security features to support your large language models and natural language processing apps.

What users like best:

“While their work quality stays superior, as agreed upon by many others in the industry, the best traits are customer support and communication. Anytime I had a question, they were ready to answer, and if SuperAnnotate needed information, the questions were clear and to the point. Even the personnel changes are carried out seamlessly and after a complete KT.”

- SuperAnnotate Review, Sai Bharadwaj A. 

What users dislike:

"Steeper learning curve for advanced features: While the basic interface is praised for its user-friendliness, some advanced features and functionalities might require a steeper learning curve, especially for less technical users."

- SuperAnnotate Review, Jesus D.

2. Encord: best known for machine learning pre-labeling

Encord offers data annotation, query support, optical character recognition (OCR) labeling services, and model evaluation. Not only does the tool help run, test, and deploy ML models faster into production, but Encord also helps shortlist the best data annotators to label and classify your data. Businesses then use it to build efficient AI pipelines and make strategic decisions. 

What stood out to us was Encord’s ability to create efficient training data pipelines and its use for active learning collaboration to build efficient ML models and MLOps pipelines.

What users like best:

"This was the first tool we found that could handle the enormous labeling taxonomy we had. We have to catalog many different types of products, and Encord’s ontology feature was extremely useful in packing everything into a usable structure. The interface is also quite intuitive, and the hotkeys make it easy for our team to navigate and speed up the annotation process."

- Encord Review, Samuel A. 

What users dislike:

"Role-based access control can get a bit cumbersome to use at times, given that you have to add people manually to projects/datasets/ontologies instead of just being able to assign people permissions in bulk."

- Encord Review, Miguel E. 

3. Dataloop: best known for human-in-the-loop 

Dataloop provides efficient data sourcing, classification, and human-in-the-loop services to businesses. It engages in building external query committees to annotate data accurately and build high-quality ML models. The tool has been consistently renowned as a secure, trustworthy, and reliable tool for handling machine learning workflows. 

Dataloop’s versatility makes it suitable for any task, such as object detection, tracking, and recognition. Matthew and I especially liked its adaptability and quick customer support. 

What users like best:

"Dataloop excels at constructing quality data infrastructure for unstructured data, streamlining computer-vision pipelines, and ensuring seamless integration with robust security measures. A reliable ally in modern data management."

- Dataloop Review, George M.

What users dislike:

"What I dislike the most about Dataloop is the frequent updates that sometimes cause the links not to work."

- Dataloop Review, Mzamil J.

4. Appen: best known for data quality and cleansing

Appen specializes in sourcing, cleansing, and preparing raw data while maintaining the highest quality maintenance and security. It also protects databases with features like data masking, role-based access management (RBAC), and continuous data monitoring. Appen provides support for ML production with various beta integrations and API keys to create faster and more efficient ML models. 

What impressed us most was Appen’s quick rise in popularity for data cleansing, masking, and user authentication, which confirmed its strong data security and privacy features. 

What users like best:

"Appen is an easy to use platform for side income by completing small tasks and projects in it. Appen pays its users monthly. Implementation of collected data will be on artificial intelligence and machine learning. Based on the completion of tasks and frequency of use, users are paid. It also supports and features automatic invoice and manual invoice generation for completed tasks.”

- Appen Review, Mattaparthi V.

What users dislike:

"The links are just confusing. Sometimes you'd have an access issue, because you logged in on an incorrect link. Apparently, there exists multiple links and thus you need to sign up for multiple accounts."

- Appen Review, Mark G.

5. Kili: best known for classification and data modeling

Kili provides data indexing, data search, data annotation, and external Oracle services. It sources high-quality data points to create efficient and agile models. Not only does it help with data management, but it also provides secure cloud storage services to protect machine learning data. It has a high-quality labeler and annotator service that can optimize the model creation, validation, and delivery process. 

Matthew and I loved how Kili creates efficient training data pipelines and shortlists a handful of external data annotators to provide ML automation. 

What users like best:

"What I appreciate most about Kili is their excellent team and quality control. It's incredibly user-friendly for working on projects with large teams and allows for precise review of who is responsible for what tasks while also monitoring for any potential quality issues.

Furthermore, the API is highly practical and easy to use. It enables the swift creation of projects, the addition of data, and the management of labeled data. Given the multitude of projects we handle, it would be a daunting task without such a fantastic API."

- Killi Review, Shashank A.

What users dislike:

"The tool doesn't accept Excel and Word docs, so I have to transform them before importing them. The team can do it with the API, but it still costs time and is quite painful."

- Killi Review, Emelie A.

6. Amazon Sagemaker Ground Truth: best known for machine learning labeling 

Amazon Sagemaker Ground Truth finds high-quality annotators and active learning agents at every step of the ML lifecycle. It supports the creation of high-quality ML pipelines through multiple labeling workforce support and monitors the labeling lifecycle. Amazon Sagemaker also helps attend to other parts of an ML production cycle and retrieve data fast from the AWS cloud.  

Matthew and I liked how this tool uses a human annotator to classify important data and use it for industry-specific tasks. It can label any type of data for several industries and companies. 

What users like best:

“I like the endpoint creation, which can infer our model through the lambda function. Along with Sagemaker, I used an API gateway as well as to use the model in a local environment.”

- Amazon Sagemaker Ground Truth Review, Shyam P.

What users dislike:

"User Interface could be less cluttered and controlled, needs to be more web-like. At the moment, it looks and feels like a client tool hosted on the web. CI/CD can be more self-managed."

- Amazon Sagemaker Ground Truth Review, Avineet A.

7. V7: best known for automatic routing of labeling

V7 is an advanced active learning tool that labels unstructured data based on an informative score. The provider has an in-built query committee that segregates data with high uncertainties and less informative scores and passes it to human annotators. The tool has been consistently praised for automating ML workflows and data operations for smooth automation and app delivery. V7 follows techniques like entropy, query by committee, diversity sampling, and margin sampling to prepare raw data and convert it into good data. 

We appreciated how V7 offers strong integrated data support, data encryption, network access control, and Python documentation support for data pre-labeling and holistic ML operationalization. 

What users like best:

“After several tries trying out various tools to annotate my data, I stumbled on V7 and immediately realized that V7 had exactly what I needed. My datasets have a lot of similar images, and V7's copy annotations feature helps save a ton of time and allows me to work through my datasets swiftly. Furthermore, I never knew I needed the image manipulation options that V7 provides until I used it. It allowed me to completely isolate my items from the noise for more accurate annotations.

Also, V7's UI looks amazing and is incredibly simple to use. There's no learning curve."

- V7 Review, Suneth T.

What users dislike:

"Finding or filtering the documents from the mass data is quite tricky sometimes. Its sorting or filtering feature does not provide accurate results or lags when there are more files."

- V7 Review, Kirti P.

8. Labellerr: best known for labeler quality

Labellerr is an all-inclusive AI development platform specializing in data preprocessing and AI pipeline management. It offers integrated APIs for cloud security management and a centralized dashboard to manage your ML operations all in one place. Labeller is compatible with different data types and builds correlations to create powerful machine learning models (MLMs) for the companies. 

Labeller’s help with feature extraction and pooling for image segmentation and object detection was definitely a plus for us. What’s more, Labeller provides custom API, security and integrations, and LiDAR support for large volumes of data.

What users like best:

"Labellerr's Smart Labeling is a game-changer for our diverse data needs, seamlessly covering image, text, and audio annotations. It adapts to tasks like transcribing customer calls and extracting insights from sales rep notes. The in-browser ML models streamline our data structuring, ensuring precision in crafting high-converting bundles and simplifying the buying process for our customers. We also appreciate its versatility in semantic annotation, showcasing furniture items in natural home spaces, just like the physical store experience."

- Labellerr Review, Kamal K.

What users dislike:

"While Labellerr offers integration capabilities with popular machine learning frameworks, it may not have direct integrations with all the tools or platforms you use in your machine learning workflows. This could result in additional effort and manual steps required to transfer annotated data to your preferred tools."

 - Labeller Review, Stavroula P.

9. Shaip Cloud: best known for image segmentation

Shaip Cloud maintains cloud registries of data, allowing for the storage, retrieval, analysis, and sourcing of data from multiple drives into one platform. It also provides multipurpose data annotation, ML monitoring and deployment support to improve your AI maturity and AI functionality. Shaip Cloud is compatible with virtual machines as well as operating systems. It also has built-in active learning support for uncertain and unlabeled data points to improve model performance and agility.

Shaip Cloud’s data security for cloud-native applications really stood out to us. You can source, annotate, and retrieve multiple databases while maintaining data privacy and labeling quality. 

What users like best:

"Shaip Cloud gave me admittance to top-notch pre-prepared vision, NLP, and discourse models with a straightforward, intuitive point of interaction. I could rapidly try out various models like picture grouping, text synopsis, feeling discovery, and so forth by simply calling their APIs. This assisted me with investigating different use cases and concluded which computer-based intelligence highlights would offer the most benefit."

-Shaip Cloud Review, Dhawlandra S.

What users dislike:

"ShaipCloud works with major cloud providers, so users must have access to these services in order to use the platform. The cost of using the platform may be higher compared to other data management solutions, especially for small or startup businesses. To get the full benefits of ShaipCloud™, users may need technical expertise, especially in the area of AI and ML models."

-Shaip Cloud Review, Richa S.

10. Datature: best known for model registry

Datature supports your MLOps operations with automated data lineage, transfer, modeling, and analysis. Customers favor Datature for its straightforward user interface that makes data preparation and labeling easy. Overall, Datature supports multiple data and file formats and has high annotation efficiency. It also offers additional ground truth services for validating and quality assurance of ML models.

What stood out was how Datature supports computer vision, object detection, and object tracking and monitoring.

What users like best:

"Datature's platform is super easy to use, making it great for beginners who aren't experts in deep learning. It has tons of features to choose from, and its data tools are super efficient, which is perfect if you don't have a lot of data. I love how it's so straightforward and practical. Plus, it's great for teamwork because you can collaborate online with your team."

-Datature Review, Jing Ying Y.

What users dislike:

"The free plan is very limited for experiencing all the functions over the Datature´s platform. Also, I am a PhD student at Simón Bolívar University, and there is no plan for that."

-Datature Review, Arelis Milagros G.

Bonus Data Labeling Software:

Matthew and I also liked following data labeling solutions and their use cases while testing and vetting all the software providers.

• BasicAI Cloud, best known for human-in-the-loop
• Keymakr, best known for task quality
• Playment, best known for diverse and ground truth datasets
• Labelbox, best known for named entity recognition
• Scale Rapid, best known for production quality labeling data

Data labeling software: Frequently asked questions (FAQs)

How should businesses negotiate the cost of data labeling software?

Businesses should first survey their existing ML production workflows and database management systems across on-premise and cloud directories. They need to account for data protection, accuracy, and quality. Once the analysis is complete, they can draft a software contract that enlists their requirements. Based on the document, businesses can justify requirements and negotiate costs effectively.

How does data labeling software integrate with the existing ML pipeline?

Data labeling software is compatible with existing ERP systems and can be implemented for various operating systems, including virtual machines. It comes with pre-installed software packages and libraries that make it easy for developers to set function calls. The software can access on-premise and cloud data pools to retrieve and label uncategorized data.

What key features should I look for in data labeling software?

Look for features such as annotation tools, security and quality control, MLOps integrations, access permissions, and dynamic data masking. Furthermore, it would be great to look for automation capabilities for AI-assisting labeling within your data labeling software to speed up production turnarounds and delivery.

Why do we need data labeling software?

Having data labeling software is the first step in strengthening your AI pipeline. By actively labeling the most uncertain data, you can train ML models to perform complex predictions.

What are the pricing models for data labeling software, and how do they fit my budget?

Common pricing models for data labeling software include subscription, tiered pricing, pay-as-you-go, and enterprise. Evaluate the cost-benefit ratio for each plan based on your requirement to map ROI better. For more information, visit G2.com.

Beyond the data door

Many companies fail to compete in this competitive AI era due to their inability to support their ML ground operations. Powering your machine learning models with accurately labeled data is the first step in establishing model accuracy. Properly treating your training data in the right way and generating active queries can support your machine learning production and delivery.  Adopting a holistic approach to AI development and securing your strategies are crucial to a business’s success.

Learn how active learning tools can evaluate the correct category of your data and improve your model accuracy with advanced capabilities. 

Edited by Monishka Agrawal