Sold by: Superb AI
Deployed on AWS
Superb AI Platform provides automation-based modules for data labeling, QA, curation and model training and deployment. These modules are designed to enable computer vision teams to create efficiencies across the full MLOps lifecycle for the purpose of rapid model building and deployment.
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Overview
Superb AI Platform provides automation-based modules for data labeling, QA, curation and model training and deployment. These modules are designed to enable computer vision teams to create efficiencies across the full MLOps lifecycle for the purpose of rapid model building and deployment.
Highlights
- Conveniently perform all major steps of the machine learning lifecycle in one platform : Curate, Label and Model.
- Curate : Auto-curate and build datasets that are well balanced, representative and most optimal for model performance. Teams can also leverage this technology to surface edge cases, mislabels and dataset imbalances. *Label : Manage large-scale data labeling projects with cloud-based project management functionalities and labeling tools that cover all data and annotation types for computer vision. Quickly build in-platform auto-label models that are specific to any use case. Use these models to l
- Get access to an expert computer vision team that can manage your project from start to finish from data curation and labeling to model training and deployment. Utilize a global network of data labeling providers that are uniquely sourced for every project based on needs of the customer.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Enterprise plan | 200K Label Data, 200K Curate Data, 200K AI Credit / a year | $40,000.00 |
Dimensions summary
You buy the Enterprise plan as a single annual contract. It bundles three usage allowances into one commitment: 200,000 units of Label Data, 200,000 units of Curate Data, and 200,000 AI Credits per year. Label Data covers data labeling work. Curate Data covers dataset organization and curation. AI Credits power the platform's AI-driven automation features. These allowances are not separate purchases; they come together under one yearly price. Your usage draws down each allowance over the 12-month term. This structure fits teams needing labeling, curation, and automation capacity in one package.
Top-of-mind questions for buyers
Label Data units cover data labeling work, such as annotating images, videos, or point clouds. Curate Data units cover organizing and reducing datasets for training or validation. AI Credits power AI-driven automation features like auto-labeling. Each allowance is counted separately as you use those functions.
The three allowances meter independently. Labeling-heavy teams draw down Label Data fastest, while dataset-organization work consumes Curate Data. Automation tasks spend AI Credits. Because they are tracked separately, one allowance can be exhausted while others remain. Your workload mix determines which depletes first.
The Enterprise plan includes 200,000 units each of Label Data and Curate Data, plus 200,000 AI Credits per year. The listing does not state overage behavior once an allowance is used up. For handling usage beyond these amounts, contact the vendor to confirm the options.
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Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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Customer reviews
Roy O.
The most unique, versatile and complete annotation platform I've ever used!
Reviewed on Apr 30, 2024
Review provided by G2
What do you like best about the product?
Superb AI streamlines annotation workflow and gives insightful analytics, you can manage your team on the platform with ease and the simplicity of navigation makes training AI models a rewarding experience.
What do you dislike about the product?
Nothing to say here, I prefere Superb AI over any other platform and I have not faced any problems at all.
What problems is the product solving and how is that benefiting you?
Superb AI provides the necessary tools I need to gather my data, set up a labeling team and monitor project processes in a concise and analytical manner, making this platform my favourite for data labeling and annotation.
Yana B.
Great platform to serve a variety of Data Annotation needs and excellent team to cooperate with
Reviewed on Feb 12, 2024
Review provided by G2
What do you like best about the product?
Superb AI platform makes it very easy to work with different types of data at ease thanks to a variety of features available and a user-friendly interface. The platform does all the heavy lifting for organizing image batches, editing the annotations, and task review abilities. The workflow is easy, understandable, and seamless with the Superb AI platform. With a smart and remarkable approach to sorting, curating, and labeling data, SuperbAI truly transforms the way you can process huge volumes of data quickly and efficiently.
What do you dislike about the product?
While we absolutely love the robust functionality of Superb AI, there are a few areas that can be tweaked. The Lidar tool can be further enhanced by allowing users to take less action to resize existing annotations and add new ones. Some of the reporting can be improved by eliminating manual intervention of users.
What problems is the product solving and how is that benefiting you?
Superb AI platform offers everything we need to have an efficient way to manage the data and lead the annotator teams while maintaining quality and productivity.
Johann B.
More than Data Labeling, as standard as ‘import pandas’ for AI/ML projects
Reviewed on Feb 01, 2024
Review provided by G2
What do you like best about the product?
They've consistently improved their product, listening to customers and bringing to market tools and features that's sped up the duration of not only Production projects, but also proof of concepts.
What do you dislike about the product?
Explaining to clients the data privacy rules and constraints, even though the Service is fully compliant. It's not really a product problem as much as it is just educating users who's just getting started with AI/ML projects.
What problems is the product solving and how is that benefiting you?
Instead of pulling together different tools, that often requires getting DevOps involved, it's one set of tools that works well within existing workflows, being it AzureML, Sagarmaker or Custom.
Less DevOps and Enginnering time means more resources available to focus on model development.
Less DevOps and Enginnering time means more resources available to focus on model development.
Timothy C.
It was great but the lagging made it frustrating.
Reviewed on Nov 30, 2023
Review provided by G2
What do you like best about the product?
-The platform is user friendly and the labelling tools are very simple to use.
What do you dislike about the product?
For large datasets, the platform lags a lot. Even when you try to filter the assets.
What problems is the product solving and how is that benefiting you?
The lagging issue. I believe that is something they are working and that will help with assigning the assets and also for data labelling.
Blaine B.
Excellent UX with the right features to accelerate labeling for computer vision
Reviewed on Jun 29, 2023
Review provided by G2
What do you like best about the product?
The annotation UI is very easy to use and more efficient than others I have used.
The labeling management tools such as team features are excellent. The ability to build auto label models on labeled data is extremely useful to accelerate labeling.
The labeling management tools such as team features are excellent. The ability to build auto label models on labeled data is extremely useful to accelerate labeling.
What do you dislike about the product?
Keeping track of multiple data sets, data tags, models etc. can be a challenge. It's best to plan ahead! Setting up a new labeling project can be confusing if you don't read the documentation.
What problems is the product solving and how is that benefiting you?
We label images for both object detection and segmentation using the Superb suite. Our goal is to improve our production models which detect objects and segment objects on production images.