Startups > Snorkel AI

Snorkel AI

Snorkel AI helps enterprises programmatically label training data at scale.
Series D $238M total Founded 2019 Redwood City, California 747 employees
Snorkel AI is a data development platform that enables enterprises to programmatically label and curate training data for AI systems, including LLMs and RAG pipelines. Rather than expensive manual labeling, it uses labeling functions that encode expert intuition through heuristics, legacy models, and LLM calls to scale data preparation. Used by Fortune 500 companies, top US banks, and government agencies to build production AI applications faster and more cost-effectively. It represents a shift toward data-centric AI, where high-quality labeled data is the bottleneck rather than model architecture.
Problem solved
Manually labeling large training datasets is prohibitively expensive, time-consuming, and prevents enterprises from efficiently developing high-quality AI systems.
Target customer
Enterprise Fortune 500 companies and government agencies in financial services, insurance, pharma, healthcare, manufacturing, and retail sectors building machine learning and AI applications.
Website LinkedIn Crunchbase Twitter / X
Founders
A
Alexander Ratner
CEO & Co-Founder
Stanford PhD in Computer Science, Harvard A.B. in Physics, affiliate assistant professor at University of Washington, led Snorkel open-source project.
C
Christopher Ré
Co-Founder
Stanford AI Lab researcher, PhD advisor to Alexander Ratner.
P
Paroma Varma
Co-Founder & Head of Solutions
Stanford AI Lab alumni specializing in programmatic labeling and weak supervision.
B
Braden Hancock
Co-Founder & Head of Technology
Stanford AI Lab alumni focused on platform development.
H
Henry Ehrenberg
Co-Founder & Head of Engineering
Stanford AI Lab alumni leading engineering efforts.
Funding history
Seed $3M January 2019 Led by Unknown · Unknown
Series A $12M July 2020 Led by Unknown · Greylock
Series B $35M April 2021 Led by Unknown · Lightspeed Venture Partners, Walden Venture Capital
Series C $85M August 2021 Led by Unknown · GV, Accenture Ventures
Series D $100M 2024 Led by Addition · Greylock, Lightspeed Venture Partners, BlackRock
Total raised: $238M
Industries
Artificial Intelligence (AI) Data Collection and Labeling Enterprise Software Machine Learning
Pricing
Custom enterprise pricing. AWS Marketplace shows $60,000 for 12-month contract. Industry estimates suggest starting around $50,000/year, scaling to six-figures for larger deployments with more data, users, and professional services.
Notable customers
Apple, Intel, Google, Uber, LinkedIn, BNY Mellon, Wayfair, Chubb, Tide, Stanford Medicine, 5 of top 10 US banks, Fortune 500 companies, U.S. government agencies
Tech stack
jQuery (JavaScript libraries) Bootstrap (UI frameworks) Google Analytics (Analytics) Google Tag Manager (Tag managers) Marketo (Marketing automation) Netlify (PaaS)
Website
snorkel.ai/
Competitors
Dataiku
Broader data analytics and AutoML platform; less specialized in programmatic data labeling.
DataRobot
Focuses on AutoML and model development; doesn't emphasize programmatic labeling functions.
Labelbox
Manual and crowdsourced labeling platform; lacks programmatic labeling through labeling functions.
CloudFactory
Crowdsourced labeling service; doesn't provide programmatic automation.
Seldon
Model deployment and monitoring focused; not a data development and labeling platform.
Why this matters: Snorkel AI addresses a fundamental bottleneck in enterprise AI: the prohibitive cost of training data labeling. By pioneering programmatic labeling at enterprise scale, it has achieved unicorn status and attracted leading Fortune 500 customers, government agencies, and tech giants. As AI applications proliferate, the ability to rapidly curate high-quality training data becomes increasingly competitive.
Best for: Enterprise data science and ML teams building AI applications who need to rapidly create high-quality labeled datasets without manual annotation bottlenecks.
Use cases
Document Classification and Information Extraction
A top US bank used Snorkel Flow to classify and extract information from financial documents at scale. By encoding document classification rules as labeling functions, they reduced manual labeling effort by orders of magnitude while maintaining accuracy, enabling faster deployment of document processing AI applications.
Large-Scale Model Training Data Replacement
Google used Snorkel to replace 100,000+ hand-annotated labels in critical machine learning pipelines. This allowed them to scale model training without proportional increases in manual labeling costs, significantly accelerating their AI development cycles.
Customer Support LLM Training Data
A Fortune 500 telecom wanted an LLM to automatically answer customer billing questions. Using Snorkel's programmatic labeling to incorporate expert knowledge and external data sources, they curated training data covering all expected question types and improved model performance, enabling deployment of 10+ supported use cases to production.
Alternatives
Labelbox Choose Labelbox for primarily manual and crowdsourced labeling workflows; choose Snorkel for programmatic automation at scale.
Dataiku Choose Dataiku for broader data preparation and analytics; choose Snorkel for specialized programmatic data labeling.
Scale AI Choose Scale AI for high-quality human-labeled data with managed services; choose Snorkel for programmatic labeling with internal expertise.
FAQ
What does Snorkel AI do? +
Snorkel AI is a data development platform that enables enterprises to programmatically label and curate large training datasets using labeling functions instead of manual human annotation. Labeling functions encode expert knowledge through heuristics, legacy models, and LLM calls to automate data labeling at scale. This approach dramatically reduces the cost and time required to prepare high-quality training data for AI applications.
How much does Snorkel AI cost? +
Snorkel AI uses custom enterprise pricing. Published information suggests starting around $50,000-$60,000 per year for standard deployments, scaling to six-figures for larger projects with more data, users, and professional services. Contact their sales team for specific quotes.
What are alternatives to Snorkel AI? +
Labelbox offers manual and crowdsourced labeling; Dataiku provides broader data preparation and analytics; Scale AI delivers managed human labeling services; CloudFactory offers crowdsourced annotation. Snorkel's unique strength is programmatic, automated labeling at enterprise scale.
Who uses Snorkel AI? +
Enterprise customers include Fortune 500 companies, 5 of the top 10 US banks, government agencies, and major tech companies like Apple, Google, Uber, LinkedIn, and Intel. Primary users are data science and ML teams in financial services, insurance, pharma, healthcare, manufacturing, and retail building production AI applications.
How does Snorkel AI compare to Labelbox? +
Snorkel AI specializes in programmatic data labeling using labeling functions and weak supervision, enabling automation and scale. Labelbox focuses on manual annotation interfaces and crowdsourced labeling workflows. Snorkel is better for enterprises with internal expertise who can write labeling rules; Labelbox suits teams preferring human annotators.
What makes Snorkel AI different from competitors? +
Snorkel pioneered programmatic labeling through labeling functions, allowing enterprises to encode expert knowledge and automate data annotation. It's the only platform offering this approach at scale. This represents a data-centric AI philosophy where high-quality labeled data is the bottleneck, not model architecture—a paradigm shift from traditional ML platforms.
Tags
programmatic labeling weak supervision data curation machine learning training data data-centric AI enterprise AI