General Questions

What is Labelbox?

Labelbox is a data-labeling and training-data management platform.

What is data Labeling?

In order to train machines to make decisions on behalf of humans, they must learn to make those decisions. Learning to make decisions is called data labeling. Managing the decisions that are being made by machines is training data management.

What can I label with Labelbox?

With our labeling interface, the labeling applications are nearly endless. We have out-of-the-box labeling interfaces for images, video, and text data. We allow for the development of custom interfaces on our platform so nearly anything can be labeled.

Is Labelbox a fit for my machine learning project or business?

If your organization is using machine learning you are probably a good candidate for a data labeling and management solution. In most cases, Labelbox is used by companies developing and deploying machine learning models and who value efficient…

How will Labelbox help my data science team deploy ML/AI?

Labelbox is your one-stop-shop for all your training data needs, helping you to scale your data labeling process, manage the quality of your training data, and improve the performance of your machine learning model predictions.

  • Setup your labeling tasks in minutes using the Labelbox interface configurator
  • Leverage internal and external teams using Labelbox collaboration and management functionality.
  • Track labeler performance, project progress, and labeling quality

How do I know Labelbox will improve my machine learning model?

Labelbox allows you to connect your machine learning model and compare your model’s performance against your team of labelers. You can also track the consensus of your labeled dataset against entire labeling team to ensure that there is high labeling quality.

What makes Labelbox different?

Lablebox is the industry leading labeling and training data management platform. What sets us apart is our focus on three main pillars:

  • Our world-class labeling interface which is completely customizable and open source
  • Collaboration management
  • Quality and performance management

Do I need to be a data scientist to use Labelbox?

Generally it is beneficial to have someone experienced in data science on your team to help ensure that your machine learning project is successfully developed and deployed. However we designed our platform to require the minimal knowledge and expertise necessary, such that anyone can become a labeler.

How do I get started with Labelbox?

You can sign up at

Does Labelbox provide support?

Labelbox provides support to all Labelbox users. Our support team is located in San Francisco and Miami and hours of operation are between 9am - 5pm PST. You can best reach support via our chat system in the bottom right of the page. You can also reach us at

How much does Labelbox cost?

We have a community license which is free for evaluation, individuals, and small projects with a 2500 labels/year limit. For organizations building expert artificial intelligence systems and for business process outsourcing companies please contact us at

Check out for more info on the community and enterprise license.

Does Labelbox have a discount for academic users?

Yes we do. Please contact us via our chat in the bottom right or at for more information.

I have my data labeled by a service. Why would I use Labelbox?

With Labelbox you can still outsource your labeling projects as well as leverage your internal team on those same projects seamlessly. The big differentiator is that with Labelbox you can manage the quality and performance of your entire labeling team whether they are outsourced or internal, all in one place.

Labelbox does not provide in-house labeling services but we do work with several labeling companies (BPO Firms) that we have vetted and currently work with many of our customers in Labelbox. We simply recommend them as a third party to help our customers so you are always free to choose any labeling services company that meets your requirements.

My company built an internal labeling tool that seems to work okay. Why would I use Labelbox?

It’s quick and easy to start annotating data using locally installed tools. For most simple annotation tasks being performed by a single labeler, this solution architecture works well. As data labeling needs scale, data management and quality control processes are needed to produce accurate and consistent training data. A common cause of underperforming AI systems is low accuracy training data.

When building data labeling infrastructure, consider the following:

Total Cost of Ownership

Homegrown tools are built to exist and serve a particular function, but with new business demands comes the cost of upgrades. There is a high cost to ongoing maintenance, both in time and money. Technical debt accrues over time due to engineer turn-over, product neglect, and evolving product demands.

Unknown and Evolving Scope

Developing an internal product requires planning, resource allocation, and preparing for the unknown. Because feature flagging platforms are relatively new, it can be difficult to accurately define the scope and construct a solution for needs across engineering and product groups.

Minimum Viable Functionality

Internal tools are generally not built for usability, scalability, or cross-team support. They are built to solve an immediate pain point or provide minimum viable functionality as quickly as possible.

Data Labeling is Cross Functional

Turning raw data into accurate and consistent training data is a team effort. Engineers, domain experts (labelers), and managers must work together while playing different roles. Data labeling infrastructure must facilitate this by providing information and interfaces unique to these roles.

Enterprise Readiness

Productionizing AI systems takes fast, reliable, and scaled infrastructure across raw data collection, data labeling, and compute.

Check out our Build vs. Buy Calculator at

Will Labelbox improve the quality and consistency of my labeled data?

This is one of our core competencies and one of the main reasons we decided to build Labelbox. We saw that all the available options for labeling made it extremely difficult to ensure our training data was high enough in quality for a production-ready machine learning model and it cost us valuable time and money in the end. We developed a world-class quality and performance management interface within our platform to address this.

How did we do?