AI teams choose Labelbox over SuperAnnotate to build models using the highest quality data
Why AI leaders choose Labelbox over SuperAnnotate
High-quality data
Generate powerful human data to differentiate your generative AI solutions, whether building frontier models or performing post-training and RLHF.
Rapid delivery with global workforce
Deliver data on-demand by accessing Labelbox’s network of highly-skilled experts and reach the scale you need to accelerate your AI project timelines.
Essential control
Create custom workflows and engage in real-time communication with external labelers to align on outputs through an open, transparent platform.
"Labelbox has enabled us to dramatically improve our model performance for our most critical AI initiatives by tapping into their network of expert labelers and platform for human evaluation. In the past two months, our document intelligence teams are seeing a 2X increase in data quality compared to previous vendors. We continue to work with Labelbox to further enhance our genAI capabilities and to hit our development timelines."
Human data lead
Generate guaranteed high-quality data with an expert labeling workforce
Although SuperAnnotate offers labeling services, their network is limited with only 400+ labelers available. Restrictive third-party partnerships offer basic language and domain expertise, but lack the specialized skills often required when building generative and task specific models.
In order to provide the highest quality data, Labelbox operates the Alignerr community, a network of 10,000+ highly educated experts that span numerous languages and subjects–with the ability to rapidly attract new talent in areas needed. We stand behind the quality of our labeling services, so if a labeled dataset does not meet a customer’s quality standards, we will happily do it again for free.
Increase performance with advanced model evaluation
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. By leveraging auto-generated metrics, comparing models and model runs, and surfacing 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.
Scale quickly with best-in-class software
SuperAnnotate offers some AI-assistive tooling, but lacks readily accessible foundation models to select and choose for tasks such as automatic pre-labeling and data enrichment, that can rapidly enable scale-up and time savings. Limited availability of public case studies demonstrating SuperAnnotate's ability to handle large-scale labeling projects, combined with user reports of performance issues, may cause concern for users working with millions of data rows or are looking to increase throughput quickly.
Labelbox delivers millions of labels every week through our unified platform that provides powerful labeling tools and AI-assisted automation to facilitate the rapid generation of high-quality data. Load, curate, label, and evaluate data from over 25+ different data sources in a unified, transparent and intuitive platform. Proven editors are available for a wide range of data types that include multimodal chat, LLM prompt/response, video, image, audio, PDF, geospatial, and more to support a wide range of tasks from a unified platform.
Create transparent, customizable projects
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.
We understand that every AI project looks a little different. For teams that just need data quickly, the labeling service powered by Alignerrs provides a hands-free process backed by software, expert labelers, and proven processes. Teams that want more control can use the Lablebox platform to create custom multi-step workflows, monitor key metrics to measure granular performance, and utilize real-time collaboration tools to track every stage of the labeling process—all with no additional coding required.
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%.