Don’t settle for the basics with V7. Build a proven data engine with Labelbox.
Why choose Labelbox over V7?
Build better AI, faster
We aren’t just focused on data labeling. Our three core products: Catalog, Annotate, & Model work in tandem to help AI teams centralize the maintenance and creation of high-quality training data needed to accelerate model production.
Enterprise-grade features
Labelbox offers rich analytics, advanced workflows, and more data modalities to cater to all of your unique AI projects. Easily connect to tools you already use, like Databricks and Azure, through our extensive partner network.
Configurable and flexible
We understand that every ML project might look a little different. Create custom workflows to fit your ML pipeline with UI features and APIs to easily curate, annotate, and evaluate model performance.
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
I heard V7 is cheaper than Labelbox….
A cheaper solution doesn’t guarantee quality. When high-quality data is a differentiating factor for the most successful teams, you want to make sure you’re optimizing for quality over cost. When you optimize solely for cost, you often get what you pay for – which is why choosing a cheaper solution that doesn’t promote scalability is a significant risk.
Labelbox’s AI-first approach prioritizes obtaining high-quality training data. We’ve carefully designed our platform and workflows to help you achieve desired quality with the least possible spend. From model-assisted labeling to custom active learning workflows, investing in Labelbox ensures better lifetime value for your AI team. You don’t have to sacrifice cost vs quality – you get the best of both worlds with Labelbox.
Isn’t V7 an all-in-one platform for improving model performance?
While V7 might appear to be an all-in-one platform to improve model performance, they lack key offerings to truly optimize model performance. There’s a single place for teams to view all their data, but V7 doesn’t cater to active learning workflows or allow teams to visually identify model errors or find similar data. Their model training offering doesn’t offer insights into model diagnostics and isn’t readily available through the SDK.
Labelbox gives you a true end-to-end experience, going beyond just the labeling phase of the pipeline. With Labelbox, we encourage active learning workflows across Catalog and Model to help you prioritize the most high-impact data for labeling. Coupled with model diagnostics, you’re able to label in priority and customize review workflows to decrease review time, labeling time, and cost.
I heard V7 has stronger annotation features….
While V7 does have similar annotation capabilities when you’re getting started, teams are limited to only a few data modalities. V7 doesn’t currently cater to data annotation for Text, Audio, or Geospatial (tiled imagery). If you want the flexibility to expand to either of these data types in the future, you might want to reconsider using V7 as your labeling platform.
Labelbox offers labeling for a wide range of use cases, including Image, Video, Audio, Text, Conversational Text, PDF / Documents, Geospatial (Tiled Imagery), and Medical Imagery (including whole-slide pathology scans). Choosing a vendor that supports a wide variety of data types gives you the flexibility to scale as your AI team and projects grow.
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%.
But I'm using V7’s workforce and don't have a dedicated team in-house
While V7 does provide outsourced labeling services, Labelbox Boost offers superior quality and ML expertise. Our on-demand labeling services and AI expertise gives you the resources you need to iterate better. In addition, unlike V7 that charges per labeled object, Labelbox Boost bills per screen activity time so your costs stay the same even as projects scale.
Labelbox Boost emphasizes both speed and quality by employing specialized teams that will help take you from initiation to production-scale labeling in just a few days with consistent quality and accuracy.
But V7 meets my security and compliance needs
On the surface, it may seem like V7 meets all of your security and compliance requirements, however, reading the fine print may suggest otherwise. While they claim to be HIPAA compliant, their last annual HIPAA assessment seems to date to July 2020.
Labelbox is designed with security in mind, with features that give you full control over your data. In addition to offering hybrid cloud integration, Labelbox is SOC2 Type II certified, and GDPR and HIPAA compliant with annual external audits.
I don’t want a V7 replacement, I’m looking to improve model performance with a better solution.
Labelbox is more than just a V7 replacement. Our all-in-one platform allows you to search, visualize, and label your data like never before.
In addition, our platform unlocks customizable workflows, model-assisted labeling, active learning, and advanced data selection methods to dramatically improve model performance while keeping data labeling costs to a minimum.
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.