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Don't let Sagemaker make reaching production AI even harder. Labelbox is built to solve your business needs.

AI teams choose Labelbox over Amazon Sagemaker for an intuitive end-to-end platform that will improve model performance.

Compare Labelbox versus Amazon Sagemaker

Everything in one place

Everything in one place

From data curation and labeling operations to model training and diagnostic workflows, Labelbox enables you to build quality AI products, faster than ever.

Out-of-the-box setup & intuitive UI

Out-of-the-box setup & intuitive UI

Labelbox's intuitive interface is easy for any team to use, with self-serve onboarding and guided implementation across the platform to help you get set up quickly.

Maximum configurability & flexibility

Maximum configurability & flexibility

Easily customize tasks with features that give you full control. Create unique review queues, flexible ontologies, and custom tasks that integrate seamlessly into your workflows.

Genentech

With Labelbox, we’re able to generate high-quality annotations by allowing our team of domain experts and labelers to collaborate more efficiently. The workflow we’ve built queues up all the work for our labelers to create image annotations, which are then sampled and reviewed by experts, and fed into ML models to make better AI diagnoses.

Miao Zhang, AI Scientist

Isn’t it convenient to use Amazon Sagemaker if you’re already using S3 or on AWS?

Isn’t it convenient to use Amazon Sagemaker if you’re already using S3 or on AWS?

While it may seem convenient to use Sagemaker if you already use other Amazon tools like S3 and AWS, it might actually take you longer to get started. Rather than being an out-of-the-box platform, Sagemaker requires extensive investment, time, and expertise to customize and build.


In comparison to Sagemaker, Labelbox is built to save you time and resources. Our out-of-the-box platform and self-serve UI allows teams to get started right away. Pre-built configuration and workflows make it easy for teams to focus on model production. 


Labelbox also offers IAM delegated access integrations for all cloud users, including configurability with Amazon S3, Google Cloud Storage, and Microsoft Blob Azure Storage. Rather than being limited to just the S3 ecosystem, you can securely and seamlessly host data in your preferred cloud storage provider and use IAM roles and policies for access control.

But I'm looking for a tool that works for my whole team...

But I'm looking for a tool that works for my whole team...

While you can try to customize Sagemaker to work for the different roles in your AI team, pay attention to reviews that emphasize how the platform is not necessarily the most intuitive to set up, manage, or navigate.


Labelbox offers features that prioritize team collaboration with easy role-based access control and user management settings. Built-in features for visibility in team performance are also included like detailed feedback loops, adding multiple workforces to a project for additional capacity, and sharing labeling instructions directly in the editor.

Reducing our data requirements is huge because we can get the same amount of improvement in our model’s performance in half the time and with half the effort. This was enabled through targeting the model’s weaknesses with Labelbox’s Model product and then being able to prioritize the right data through Catalog. By doing so, we’ve reduced our labeling spend and data needs by over 50%.

Noe Barrell, ML Engineer
But I don't have a dedicated labeling team in-house

But I don't have a dedicated labeling team in-house

While both Labelbox and Sagemaker offer outsourced labeling services, there’s a big difference in pricing.


Sagemaker Ground Truth charges based on price per reviewed object. This means that your labeling spend will quickly grow proportionally to the volume of data you're labeling. In short? More labeled data means more money spent.


In comparison, Labelbox Boost bills per annotator per hour. Time based billing means you'll only pay for the time spent labeling, allowing for a more accurate assessment in project budgeting and time to completion.

How do I know which platform will work for me long term?

How do I know which platform will work for me long term?

Labelbox is a platform designed to grow with you. Our editors are highly configurable out of the box, allowing you to create customized labeling tasks and QA workflows with little to no coding. 


In comparison, Amazon Sagemaker isn’t necessarily designed to help you grow and scale AI projects. Once you get started, you'll quickly encounter the headaches of rigid task limitations and manual setup. This can cause roadblocks and lead to long term complications for your team, delaying your time to get to production AI.

I don’t just want to get data labeled, I'm looking for a better solution that will improve model performance

I don’t just want to get data labeled, I'm looking for a better solution that will improve model performance

Labelbox is an integrated, end-to-end platform designed to improve model performance, rather than just helping you get data labeled. Our approach enables AI teams to use workflows, model-assisted labeling, active learning, and advanced data selection methods to improve model performance while keeping data labeling costs to a minimum.

AI has been crucial for us to accomplish our goals and we’re using Labelbox in many of our projects and processes. It allows us to standardize how we create and manage data all in a single location and using their automation features, we’ve seen a reduction in labeling times by 2x.

Christian Howes, ML Engineer

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