Outgrown Superannotate? Scale up with Labelbox, the enterprise-grade AI data engine built to grow with you.
Compare Labelbox versus SuperAnnotate
Build better AI, faster
Labelbox promotes iterative labeling practices with built-in features like model-assisted labeling, a dynamic queuing system, and having a human-in-the-loop to help you label less in the long run and get to production AI faster.
Everything in one place
Leverage an end-to-end system that empowers teams of all technical proficiency levels by uniting data visualization, annotation, curation, services and model improvement into one flexible, scalable platform.
Powerful integrations
Labelbox has partnerships with Snowflake, Databricks, DataRobot, and Google Cloud Platform (GCP) to ensure seamless connections as teams store and manage data as well as train and deploy their models.
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
What if I'm also looking for dataset management capabilities?
While SuperAnnotate's machine learning studio offers data curation capabilities, the interface itself mirrors a folder structure and only offers data management for three data types–Image, Video, and Documents.
Labelbox Catalog is designed to be the central hub for your data, with systems and workflows to help manage disparate datasets. Catalog capabilities also extend to Image, Video, Text, Geospatial Audio, PDF, etc.
What if I want to debug my model and identify data quality issues?
SuperAnnotate lacks the ability to accurately and quickly debug your model performance. While they offer rudimentary training metrics such as IoU and total loss, they aren’t sufficient in providing you with data-centric tools to improve your data quality and model performance.
Whether you are developing a model or already have one in production, Labelbox can help you effectively measure your model’s performance. You can leverage auto-generated metrics, compare models and model runs, and surface where ground truth and predictions agree or disagree. You can understand how your model is performing against specific data, allowing you to make targeted improvements in the data labeling process.
What if I need in-depth analytics to measure performance?
While SuperAnnotate’s machine learning and data curation studio offers broad-based analytics that help monitor a project’s labeling quality, they lack specific insights into granular metrics such as labeler productivity or throughput.
Labelbox has built-in quality assurance features that also keep track of labeling quality on both the project and individual level. Get full visibility into your labeling spend, productivity metrics, and model predictions to minimize cost and get to performant AI faster.
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.
What if I need a tool that's easy for my team to use?
SuperAnnotate is a code-forward platform, requiring that users know SQL and Python to perform basic data exploration, discovery and analysis tasks. Combined with a user interface that reviewers report is 'difficult to learn' and 'takes time to get used to' -- these onboarding obstacles stall machine learning projects or demand specialized talent just to get them off the ground.
Labelbox's intuitive user interface empowers professionals of all technical proficiency levels to engage throughout the entire end-to-end machine learning model development and improvement process. To enable rapid adoption, teams can even set up custom workflows that fit their unique data curation, review, and improvement processes.
I work with sensitive data and am concerned about security…
Labelbox was built to reduce data privacy and IP concerns—all data is customer owned with easy export and deletion options. Labelbox is also headquartered in the United States, SOC2 Type II certified, and GDPR and HIPAA compliant with annual external audits.
While SuperAnnotate complies with certain industry-specific security regulations like HIPAA, most of their development team and data storage options are based in Armenia, which may cause difficulties when it comes to security and data protection.
I don’t just want to get data labeled, I'm looking for a better solution that will improve model performance
Labelbox is an all-in-one, enterprise-grade platform that helps you connect and manage data to improve model performance. 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.
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%.