Noisy labels make noisy AI. Get a complete solution for improving your models and data with Labelbox.

AI teams choose Labelbox over Snorkel AI for an intuitive end-to-end platform that can manage your entire labeling data pipeline.

Compare Labelbox versus Snorkel AI

Everything in one place

In addition to labeling tools, Labelbox offers data curation, AI-assisted labeling, model training and diagnostics, along with labeling services, all in one platform.

Easy 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.

Advanced configurability and flexibility

Create customizable QA and review workflows, flexible ontology setups, and unique task structures to meet your project-specific needs.


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

I’m looking for a solution that includes the software along with labeling services….

I’m looking for a solution that includes the software along with labeling services….

Snorkel is a software-only solution, their main product offering is creating heuristics to label data. They do not offer outsourced labeling services. If you do not have your own internal labeling team or a dedicated BPO then you'll have to bring on other vendors just to make Snorkel's labeling tool work.

In comparison, Labelbox offers the best of both worlds. You can choose to label your own data through either an internal team or BPO, or use Labelbox Boost’s on-demand labeling services and AI expertise. Our specialized machine learning teams are experienced in many AI use cases and give you the resources you need to iterate better.

What is the main difference between Snorkel and Labelbox?

What is the main difference between Snorkel and Labelbox?

In Labelbox Catalog, users can efficiently pre-label assets in bulk with state-of-the-art embeddings that understands the semantic meaning in images and text. This embedding-based function enables weak supervision with a no-code, visual, and intuitive interface to quickly pre-assign labels to your assets. Using a human-in-the-loop review process means your labels will be 100% accurate, decreasing time to a performant model with faster iteration cycles to help you get to production AI faster.

In addition, Labelbox supports active learning through AI-assisted labeling, while Snorkel does not. You can leverage your own model or a state-of-the art one to generate pre-labels, embeddings, and confidence scores to your assets. From there, Labelbox then helps you surface the most valuable assets to label to improve your model, reveal the low-confident ones that requires more human review, and assign pre-labels to speed up labeling workflow.

Isn’t Snorkel more cost-effective?

Isn’t Snorkel more cost-effective?

While a weak supervision approach is more cost-effective for programmatically labeling massive amounts of data, it may cost you more in the long run to organize, QA, and actually use that data for model training.

In cases where labeling is incorrect, your team would need to invest extra time to fix it which would cause delays in your labeling project. In addition, you'd then have multiple models to improve and correct - the model being built for production AI and the model that is supposed to help your labeling process.

Labelbox offers a more straightforward process that doesn't require you to juggle models. Our approach is designed to cut labeling costs by up to 70% and improve labeling efficiency by giving you insight into model predictions and labeled and unlabeled data.

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
But Snorkel claims to have strong use cases

But Snorkel claims to have strong use cases

Snorkel’s approach may work for some use cases, but programmatic labeling doesn’t actually work across all data modalities. Using heuristics for image and video projects becomes much harder than hand labeling and using ground truth. If you want the flexibility to expand to either of these data types in the future, you might want to reconsider using Snorkel AI as your labeling platform.

Labelbox offers labeling for a wide range of use cases, including Image, Audio, Video, 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.

But my data is sensitive and I’m concerned about security & privacy…

But my data is sensitive and I’m concerned about security & privacy…

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.

In comparison, Snorkel AI’s security processes are opaque and not readily available. It’s unclear how your data is stored, processed, and protected. This information is only available with a back and forth through their security team, which can often be a slower process and you might not get the answers you need.

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 goes beyond data labeling. Our all-in-one platform allows you to connect and manage data like never before. 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.

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

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