How does Labelbox compare to other data labeling solutions?

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Labelbox is a data annotation and labeling software developed to enable organizations to build computer vision models. Some of the features offered by Labelbox include interactive 3D draw-from-2D tool, custom export functionality, auto-labeling tools, and configurable ML models.

Recently, Labelbox raised a $40M Series C led by B Capital Group, making the total amount of money raised by the company now up to $79M.

In this article, we will discuss how the software developed by Labelbox compares to other data labeling solutions specifically looking at aspects such as accuracy of annotation and labeling information, speed of labeling process and cost effectiveness for organizations. In addition, we will also be sharing insights on how technology like computer vision is helping organizations improve their processes for image annotations and understanding customer needs better.

Labelbox, which develops data annotation and labeling software, raises $40M Series C led by B Capital Group, bringing its total raised to $79M (Kyle Wiggers/VentureBeat)

Labelbox is a data annotation and labeling software developed by Labelbox that helps organizations manage, process, and analyze their data.

Labelbox has recently raised $40M in Series C, bringing their total to $79M.

In this article, we’ll look at how Labelbox compares to other data labeling solutions and what makes it a strong choice for businesses.

Features and Benefits

Labelbox is a data annotation and labeling software platform that enables organizations to create, manage, and integrate labeled data sets efficiently. The platform provides easy-to-use labeling tools for custom datasets accessible from any device with an internet connection. By streamlining the process of creating labeled data sets, Labelbox helps users to accelerate their machine learning initiatives.

Labelbox offers many features, making it an attractive data labeling solution for businesses. These features include:

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  • Accessible Tools: Labelbox provides tools tailored to each customer’s specific needs. The software supports batch processing to quickly label large amounts of data and deep learning model training capabilities to speed up the development process.
  • Scalable Platform: Labelbox can be deployed in cloud and on-premise configurations, allowing organizations of all sizes to scale up their projects easily.
  • Automation and Quality Control features: To save time and resources while ensuring high-quality outcomes, the platform offers automated checks such as real-time rule validation and conflict resolution—ensuring accuracy across all labels.
  • Collaboration Tools: To encourage collaboration among teams working on different projects or tasks, Labelbox provides access controls for tasks management and reporting capabilities for progress tracking purposes.
  • Extensible API: Users can access external services via Labelbox’s extensible API, making integrating into existing architectures or batch processes easier.

On top of these features, Labelbox provides additional support services such as customer success management and project consulting so customers can customize their projects according to their needs or industry best practices.


Labelbox offers an enterprise-grade data annotation platform for large-scale training efforts. It supports various data types, including images, video, audio, text and sensor data. In addition, this platform offers several pricing plans for users who want to use the Labelbox software.

The Free plan is designed for smaller teams who need some of the basic features that Labelbox offers but do not require enterprise support or extra features. The Professional plan covers most users’ basic needs and includes unlimited data annotation. The Business plan is great for companies that have large projects and need custom workflows or multi-project support. Lastly, the Enterprise plan is best suited for large organizations with high needs; this plan embeds Labelbox managers in new projects to ensure fast onboarding and quick ROI goals are achieved.

Each pricing plan has two options: Core only or Core + Services (which provides extra services like integration API setup, workflow definitions and professional services). In addition to these prices, which vary according to the number of user accounts needed per project or organization, customized quotations are available for larger organizations with specific requirements.

Comparison with other Data Labeling Solutions

Labelbox, a leading data annotation and labeling software, recently raised $40M Series C, bringing the total amount raised to $79M. With this funding, Labelbox has become one of the market’s most popular data annotation and labeling software solutions.

But how does Labelbox compare to other data labeling solutions? This section will discuss the features, pros and cons, and pricing of Labelbox compared to other data labeling solutions.

Labelbox vs. Amazon SageMaker Ground Truth

Labelbox is a cloud-based platform used to label and annotate data. It enables teams to quickly and accurately label large datasets, building strong models and enabling organizations to get more accurate results from data-powered decisions. It also allows users to work with the latest annotation tools and accurate version control and streamline the workflow process with features such as automated AI-assisted labeling.

Amazon SageMaker Ground Truth is an object detection tool used to help label images and videos quickly at scale. Amazon Rekognition can identify objects within images, while Amazon Transcribe can help with text transcription from audio clips, eliminating manual labor from the annotation process. Additionally, Amazon provides access to a wide range of other services that allow for automated annotation of multimedia files for common tasks such as recognizing nouns within text documents.

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Overall, Labelbox offers enhanced accuracy and customization in its data labeling compared to existing solutions like Amazon SageMaker Ground Truth which use pre-defined rules for labeling data automatically. Furthermore, Labelbox’s collaboration tools allow teams of up to 200 users from multiple stages in their workflow — including machine learning experts, decision makers, and data analysts — reducing the human effort needed compared with other solutions requiring input from multiple stakeholders without an integrated platform.

Additionally, its ability to use custom predictive models such as convolutional neural networks provides even more accuracy when compared with pre-labeled datasets acquired through other services like Amazon’s AutoML mechanism or OneAPI’s MultiML systems which don’t afford this level of customization when labeling data manually or using AI algorithms respectively.

Labelbox vs. SuperAnnotate

Labelbox and SuperAnnotate are data annotation and labeling software solutions developed by rapidly growing startups. Both solutions have been created to provide customers with a convenient way to label and annotate large-scale image and video datasets for use in many industries, from healthcare to self-driving cars.

Labelbox is the leader in data annotation and labeling thanks to their comprehensive platform, which offers a wide range of features including multiuser support, custom labeling tools, built-in validation tools, automated data enrichment options, API access, customizable labeling tasks, machine learning models and much more. Labelbox recently raised $40 million in funding, bringing their total raised to $79 million.

SuperAnnotate is relatively new on the market but has managed to carve out its own space in the industry, focusing on human-assisted computer vision models for faster annotation results. The platform is designed for faster annotations compared to human manual labor.

In addition, it gives customers access to many amazing features such as automated machine learning-based model building, multiprocessor computing (for upscaling your projects), versatile collaboration tools (for teams) and optimized workflows etc. SuperAnnotate also has raised significant funding with $25 million from Plug & Play and Endure Capital coming into the company recently.

Labelbox is largely seen as more comprehensive than SuperAnnotate when it comes down to finding a suitable data annotation solution as it offers great customer support, detailed training material (guides) and a vast array of features make data labeling easy. However, when looking at speedier results, SuperAnnotate provides better automation capabilities, aiding in quicker annotation speeds while still providing great accuracy.

Labelbox vs. Dataturks

Labelbox is software that provides annotation and labeling solutions for businesses. It helps organizations to create learning data sets, relatively quickly by replacing their manual workflows, so that machine learning systems can be trained more accurately. By leveraging automated workflows and an intuitive user interface, Labelbox allows rapid data labeling of images and other media formats.

Dataturks is another software that optimizes teams’ efforts to create high-quality training datasets for various machine learning tasks. It offers an intuitive platform with integrated workflow engineering and advanced capabilities such as active learning, predictive analysis and visualization. Its advanced feature set enables businesses to understand how much their training dataset resembles the real-world data they will leverage when applying ML models in production scenarios. In addition, it has several pricing plans that allow users to pay per active project or use case.

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While both tools are designed to help label data more efficiently and effectively build datasets for machine learning algorithms, Labelbox is best suited for enterprise customers looking for a comprehensive platform. At the same time, Dataturks focuses on quick setup with a comprehensive understanding of the labeling process. Both products focus on the same thing – creating accurate datasets faster – but differ in terms of features, ease of use, and pricing models available.


In conclusion, Labelbox stands apart from other data labeling solutions for many reasons. It provides various data annotation tools and features designed to streamline and simplify the labeling process, making it more efficient and cost-effective. Compared to other solutions, Labelbox also offers an intuitive user interface, with tools and features that are easy-to-use and understandable. Its custom APIs allow users to integrate Labelbox into existing software applications quickly and easily.

With the backing of B Capital Group–and a total funding round of $79M–Labelbox is poised to continue innovating in this space in the months ahead.