Machine learning teams are seeking cost-effective ways to produce accurate deep learning models. Some resorted to using free data labeling tools to minimize expenditure when producing training data sets. But do such moves prove beneficial for machine learning engineers and their organizations?
In this article, we'll compare free data labeling platforms and those offered by reputable data labeling vendors. We'll provide a list of the best data labeling tools you can get started for free. More importantly, you'll learn the cost of using strictly zero-cost data labeling solutions compared with professional ones by trustworthy vendors.
Free data labeling tools vs. vendor data labeling tools: key differences
For this article, we define free data labeling tools as software that doesn't impose any changes nor provide upgrades to users. Meanwhile, vendor data labeling tools are solutions that may offer a free plan along with paid subscriptions. This distinction is important because it determines the data labeling experience for organizations.
Free data labeling software may limit annotators to basic labeling tools. For example, they provide basic bounding boxes but not advanced features like AI-assisted annotation, which is helpful in annotating computer vision data. On the other hand, paid vendors' software consists of advanced tools that enable efficiency in the data labeling workflow. For example, Kili Technology provides tools that support annotation for natural language processing, computer vision research, and other machine learning tasks.
Most organizations start their machine learning journey at a smaller scale with minimum labeling needs. At this point, using free data labeling tools may meet their needs. However, organizations would need to evaluate their options as they train more complex machine learning models with hundreds of thousands of datasets. They choose professional data labeling software with better capabilities, such as collaboration and automation tools that support data management and workflow amongst annotators.
Free data labeling tools offer limited or no support to users. If you require assistance when using specific features, you may be limited to asking questions in the community or via trial and error. Meanwhile, paid solutions from trusted vendors are accompanied by dedicated support. You'll have access to documentation, live chat, and other means to resolve your issues.
Platform integration is essential to streamline the entire machine learning pipeline. Unfortunately, free data labeling tools are often standalone solutions, introducing gaps in your machine learning development workflow. On the other hand, paid annotation tools include integration capabilities, allowing the tool to connect with cloud storage, ML stacks, and other helpful services.
Free data labeling tools are not guaranteed to manage and store raw data safely within the operating environment. Conversely, vendors offering paid annotation tools are more conscious of data security. This enables organizations to ensure data privacy and regulatory compliance when developing training datasets.
Best free Data Labeling Tools
We share natively-free tools and data labeling solutions that offer free plans below.
Kili Technology is a data annotation tool that allows you to create accurate training datasets affordably and with ease. It provides labelers with advanced and automated tools for the following unstructured data.
Document data (e.g. PDFs)
With an intuitive centralized project management dashboard, you can coordinate and distribute labeling tasks to meet complex machine learning needs.
Kili is trusted by notable brands, such as IBM, Michelin, and Airbus, to support their LabelingOps. It is designed to scale with project growth by integrating cloud infrastructure and your existing machine learning pipeline. More importantly, Kili applies security features like encryption and authentication to safeguard training data.
Specifics: Data quality features such as analytics, AI-assisted pre-labeling, and issue trackers. A flexible API for accessing data on Kili from external platforms.
Users: Machine Learning Engineers, Data Scientists, Labeling Service Providers, Labelers, Researchers, and Scholars.Website: https://kili-technology.com/
Ango Hub is a versatile data annotation platform for creating high-quality training samples from diverse data types. The tool, which comes with a free plan, provides labelers and reviewers with powerful labeling capabilities. Whether annotating text, audio, video, or image data, Ango Hub injects efficiency into the process.
Designed with large annotation teams in mind, Ango Hub allows labelers and reviewers to easily collaborate when generating datasets. Besides enabling real-time issue tracking, the data labeling software also features advanced tools, such as rotated bounding boxes, table-based labeling, and a customized annotation dashboard.
Specifics: Minimalist and customizable labeling interface. Advanced tools like multi-page annotation, label relation creation, and nested classifications.
Users: Labelers, Reviewers, and Project ManagersWebsite: https://ango.ai/
Clarifai offers one of the best data labeling tools to create data sets for object detection, language procession, and other machine learning models. It took pride in its AI-powered annotation, significantly reducing labeling time by automatically tagging the selected data. Clarifai's drag-and-drop labeling interface also helps to improve labeling productivity.
This data labeling tool is part of an end-to-end AI development platform. Organizations across industries have used Clarifai to power advanced machine learning applications with high-quality training data. They customize labeling workflows, automate them and continuously monitor annotation metrics with features that Clarifai provides.
Specifics: Auto-annotation tool to speed up data labeling. Secure annotation platform to maintain data integrity when labeling documents.
Users: Data scientists, labelers, software developersWebsite: https://www.clarifai.com/
Encord is a free data labeling tool dedicated to annotating computer vision data. It frees up labelers from repetitive work with a suite of performance-driven automation tools. Encord applies active listening in its AI-assisted workflow to reduce labeling time for the image, video, DICOM, SAR, and ECG data formats.
Machine learning engineers and subject matter experts in healthcare, defense, and aerospace use Encord to train neural networks for mission-critical applications. Encord allows organizations to balance quality control and workflow efficiency with data transparency and fine-grained annotation management. It also provides SDK that integrates data labeling with your existing ML workflows.
Specifics: Advanced routers to create customized data labeling workflow. Debugging capabilities for discovering errors in labeled data.
Users: Machine learning engineers, annotators, healthcare, defense, and aerospace experts.Website: https://encord.com/
Keylabs is not precisely a free data labeling solution, but it offers a free trial to let users experience its premium features before subscribing. The software is primarily geared toward annotating image and video data with segmentation, classification, shape interpolation, and other automated tools.
Besides ensuring accurate data labeling, Keylabs allow collaborators to improve productivity. It tracks labeling tasks and reflects them in real-time analytics. This alerts project managers of discrepancies and enable them to remediate issues promptly. In addition, Keylabs provides advanced features, such as automated annotation, task distribution, and outlining, which further speed up the labeling workflow.
Market Segment: 33% Mid-Market, 33% Small-Business
Specifics: High-performance video and image labeling tools such as instance, polygon, and bitmasks annotation.
Users: Machine learning engineers, annotators, and subject matter experts.Website: https://keylabs.ai/
Keymakr is well-known for its high-quality annotation services but also offers software that lets you implement your own annotation workflow. The company was trusted by notable brands, such as Walmart, Shopify, and AMD, to empower its AI development efforts. Specifically, organizations use Keymakr to train foundational image and video models.
One of Keymakr's differentiating points is a team of experienced labelers is pivotal in engineering its labeling solution. It considers common labeling challenges and provides helpful features to overcome them. Besides supporting standard annotation types like segmentation, image classification, and interpolation, Keymakr enforces strict quality control and data visibility when annotating data.
Specifics: Automatic annotation enabling quick labeling with AI without prior training. Functional project management tools for creating custom annotation roadmaps.
Users: Labelers, project managers, and data scientists.Website: https://keymakr.com/
Labelbox is a versatile data labeling solution that supports almost any data type. Whether named entity recognition for textual data or polyline for images, organizations find this tool helpful in supporting their diverse AI development needs. The features were field-tested and promised cost and time efficiency to develop accurate training datasets.
Designed to incorporate human-in-the-loop, Labelbox lets data scientists collaborate with the internal and external labeling workforce effortlessly. It streamlines communications and facilitates feedback to resolve labeling issues promptly. Project managers also found its analytics helpful in improving labeling throughput and quality.
Specifics: Customized workflows that support collaborative reviews and rework. Automated labeling to reduce annotation time without compromising quality.
Users: Data scientists, labelers, and domain expertsWebsite: https://labelbox.com/
Plainsight is a computer vision annotation tool that empowers enterprises in their AI strategies. It features state-of-the-art labeling capabilities, such as smartpoly object selection, which automatically resolves a bounding box on the object's outline. That, along with track-forward and automated labeling, makes Plainsight a powerful annotation solution to maximize project efficiency.
Industry leaders like Google, Intel, and nVidia trust Plainsight to deliver the accuracy, convenience, and speed that complex vision annotation tasks sorely need. It consolidates data annotation on a centralized platform and integrates with secure cloud storage and popular ML frameworks. Plainsight does not disclose its pricing, but a free trial is available.
Specifics: AI-assisted annotation tools to speed up labeling. End-to-end ML workflow support with data source integration and various dataset export formats.
Users: Enterprise machine learning teams.Website: https://plainsight.ai/
Segment ai provides specialized 3D, video, and image annotation capabilities for self-driving vehicles and robotic applications. With features like sensor fusion annotation and AI-assisted labeling, data scientists can create precise datasets for multi-sensor AI systems. The tool was trusted by next-generational automotive and robotic firms, such as Flux Auto, Cartken, and Bloomfield Robotics.
One of Segment AI's differentiating features is its ability to improvise labeling accuracy with active learning. The tool compares model prediction with pre-determined standards to continuously enhance its AI-assisted labeler. Its ability to streamline collaborations between in-house and outsourced annotation teams is equally impressive.
Specifics: Sensor fusion labeling to merge annotated data from multiple sensors. Batch labeling to improve 3D cloud points annotation efficiency.
Users: Labeling teams, students, and academic researchers.Website: https://segments.ai/
SUPA is an easy-to-use data labeling platform that aims to empower AI/ML teams in creating trustworthy ground truth for their models. It offers a diverse annotation toolset, including object detection, text classification, and semantic segmentation for labeling multiple data types. Since its launch, SUPA has helped machine learning teams in companies like Carousell, Invigilo, and Greyscale AI to iterate their data annotation implementation.
SUPA combines label quality and speed to shorten AI development duration. Its handy collaboration capabilities allow project managers to view feedback while labelers tag the unstructured data. This greatly reduces an organization's dependency on third-party quality check tools.
Specifics: Segment anything model (SAM) with human validation for better labeling accuracy. QA analytics to address annotation issues.
Users: Data scientists, ML engineers, and AI development teams.Website: https://www.supa.so/
Superb AI offers a comprehensive AI development suite that includes Superb Label, a free-to-try labeling tool. It caters to machine learning teams working on computer vision systems by providing an effective way to generate accurate datasets for training neural networks. The platform supports common annotation methods, such as cuboid, polyline, and polygon segmentation.
Whether tagging image or video data, Superb AI provides organizations with an intuitive and collaborative-friendly interface to make labeling more effortless. Equally intriguing is its AI-assisted labeling, which speeds up dataset generation by tagging common objects with pre-built models. Other helpful features, such as performance tracking, serve to boost your machine learning workflows.
Specifics: Automated labeling speeds up annotation with pre-trained models. Performance tracking tools to reduce bias in training samples.
Users: Labelers, domain experts, and reviewers.Website: https://superb-ai.com/
V7 is one of the best data labeling tools for creating high-quality training data at scale. It provides labelers with an intuitive interface packed with powerful annotation capabilities to support various model training needs. Besides labeling with the UI, V7 delivers a powerful API that lets ML engineers create training datasets programmatically. According to V7, this method allows annotators to label data by up to 10 times faster.
V7's application spans generic and specialized use cases, including medical, energy, and automotive. It supports common raw data formats, as well as DICOM and NIfTI for medical systems. One of its key features is the one-click annotation, which creates a pixel-perfect polygon on the target object.
Specifics: AI-assisted labeling to label specific objects in one click. Data privacy with SOC2, HIPAA, and ISO27001 compliance and security measures on the platform.
Users: Data scientists, labelers, engineers, and researchers.Website: https://www.v7labs.com/
Label Studio by Heartex
Label Studio is an open-source data labeling platform developed by Heartex. You can use the community edition for free to annotate text, video, image, audio, and other supported data types. However, there is no official support or strict security measures for the free edition designed for individual data scientists.
Larger teams and enterprises chose Label Studio Enterprise, a paid cloud-hosted version with premium features. This version provides project management tools that let managers assign annotation tasks and perform quality checks. There are also advanced security features, such as role-based controls and data protection, to safeguard raw and labeled data.
Specifics: Annotate different data types, including image, audio, video, and text. API access enables programmatical annotation from external platforms.
Users: Data manager, annotators, and reviewers.Website: https://heartex.com/
What is the (real) cost of Free Data Annotation Tools?
Data annotation tools that allow free usage are attractive on the surface but may not be beneficial in the long run. Using free data labeling software may save you upfront or recurring costs but hampers your machine learning development in various ways.
Lack Of Features
Free annotation software may provide basic labeling tools, such as the bounding box and polyline, but lack features that genuinely make a difference in ML implementations. For example, the community version of Label Studio doesn't have a project management workspace that allows distributing tasks to labelers. Neither could such free tools enable reviewers to provide timely feedback on the labeled data.
Besides the limited or absence of quality control tools, there is no way to assess labeling performance and identify teething issues in the workflow. Often, project managers must resort to manually coordinating tasks and reviews amongst the annotation team. Such free tools also lack customization features that could help in tailoring the labeling process to unique project needs. And this contributes to considerable productivity loss.
Understandably, small organizations and ML teams might start with free data labeling solutions. However, upgrading to the paid versions is vital to scaling ML model development. In ML implementations, it's essential to plan for growth and not be hindered by limitations imposed by free-to-use labeling tools. Eventually, free tools will be incapable of supporting growing dataset requirements. Integrations connecting to cloud storage, ML stacks, and other services will be helpful when teams scale their datasets and model complexity.
Some data labeling tools offer free plans that limit specific capacities, such as the number of annotations, users, projects, or integrations. These plans allow teams to try the software and determine if it suits their ML needs.
No Dedicated Support
When choosing free or open-source data labeling software, you also sacrifice dedicated professional support. Without a fee, the provider cannot offer timely resolution when you encounter labeling or technical issues. Moreover, the company is not obligated to provide feature updates or bug fixes if it operates with a non-profit model. Instead, you'll need to take your issue to the community forum (if any), where fellow users or data experts take on the support roles. However, there is no guarantee of timely response as stipulated by a paid vendor's SLA.
Data Security Concerns
Data annotation involves moving large numbers of raw and labeled data between on-premise or cloud storage. Often, these data contain personal or sensitive information that organizations are compelled to protect. Using free annotation software might subject these data to privacy breaches as they lack security features. Instead, paid data annotation solutions offer better visibility and control throughout the labeling process.
Run Cost of Server and Backups
When considering a free data labeling solution, it's important to remember that running the server and managing backups will likely fall on your company's shoulders. This means you'll need to allocate resources to handle the costs associated with server maintenance and storage, which can quickly add up. It's crucial to assess whether your company has the infrastructure and budget to handle these ongoing expenses before committing to a free solution.
Maintenance Cost and Expertise
While free data labeling tools may not require upfront monetary investment, they often come with hidden costs in terms of maintenance. You'll need to allocate time and expertise within your team to handle the upkeep and troubleshooting of the tool. This can be particularly challenging if your team lacks the necessary knowledge or experience in managing the specific software. Consider whether your company has the capacity to dedicate resources to ongoing maintenance before opting for a free solution.
Free data labeling tools may lack the user-friendly interfaces and comprehensive documentation that paid solutions often provide. As a result, you may encounter a steeper learning curve when adopting a free tool, requiring more time and effort to become proficient. This can potentially slow down your data annotation processes and hinder productivity. Assess whether your team has the bandwidth to invest in training and familiarizing themselves with the intricacies of the free solution before making a decision.
Productivity and Data Annotation Quality
Free data labeling solutions might lack advanced features such as workflows and automation, which can significantly impact productivity and data annotation quality. These features streamline the annotation process, reduce human error, and enhance efficiency. Without them, your team may be left with manual and time-consuming annotation methods, potentially resulting in lower accuracy and efficiency levels. Consider whether the potential decrease in productivity and quality outweighs the initial cost savings offered by free solutions.
So, How to Choose a Data Labeling Tool?
We've made our case for why cost shouldn't be your sole consideration when choosing a data labeling tool. Instead, weigh these factors diligently before you decide on any specific software.
Consider the software's annotation features and your AI/ML needs. For example, software like Encord is developed for labeling computer vision data and unsuitable for language models.
Plan your budget and shortlist tools that meet your expected price point. Remember that paying for professional labeling tools is cheaper than the productivity loss, security concerns, and other hidden costs of using free ones.
Choose annotation tools built with scalability in mind. Look for integration and automation capabilities that allows you to create more complex models with larger datasets and teams on the same platform.
Make data security a priority. Use data labeling tools that enforce strict security practices and technologies. For example, trustworthy software provides data encryption, secure sign-on, and role-based access to protect clients' interests.
For further information, feel free to read our guide on how to compare data labeling tools.
Free data labeling tools are a convenient starting point in your machine learning journey. While they provide zero-cost access to annotation capabilities, such tools often provide limited features and lack sufficient data security controls. Instead of saving money, free annotation tools often incur more costs, delays, or penalties than they should.
We've explained the drawbacks of using free data labeling software and the benefits of using paid ones from trusted vendors. In addition, we've listed some of the best data labeling platforms where you can get started for free. Ultimately, it's essential to consider all factors and make an informed decision when choosing a data labeling tool.
As the various ML teams we’ve been helping to launch successful AI apps over the years allowed us to witness: most often, the best of both worlds is to start with a free data labeling tool before switching to a more paid version. Indeed, depending on your scaling or your ambitions, you might have to pivot to a paid offer. In any case, you should always ensure that your data labeling tool can provide you with advanced capabilities for labeling, quality management, and collaboration.