BigML

Name: BigML
Brand: BigML
Rating: 4.7 (33 reviews)

BigML removes the complexities of Machine Learning so you can focus on what matters most, automating decision making.

Est 2011|Valuation $72.2M|Raised $13.1M|68 Ref Customers

LinkedIn 7K|Web traffic 13K|FTEs 25|HQ Corvallis, Oregon, US

MarketsData Science & Machine Learning Platforms

01Competitive Landscape

Narrative competitive analysis

**BigML vs DataRobot** - DataRobot is positioned as an enterprise AutoML platform with extensive governance and deployment patterns. BigML publishes lower-cost entry points and may be suitable where a lighter-weight, API-centric approach is acceptable. **BigML vs Dataiku** - Dataiku emphasizes enterprise collaboration, governance, and extensive data ingestion/wrangling. BigML provides a more focused, lower-friction path for tabular ML and API deployment. **BigML vs Alteryx** - Alteryx targets organizations seeking advanced data preparation and analytics workflows. BigML focuses on the modeling and deployment layer for structured ML problems; Alteryx addresses a broader analytics problem set.

Market share / adoption signals

Third-party engagement metrics place BigML at a modest share in some Data Science Platform categories. Vendor-published adoption metrics indicate a sizable user community, but self-reported counts do not directly equate to enterprise penetration.

	BigML	Dataiku	Alteryx (Designer Cloud)
Starting Price	Free tier available; trial options	Free edition available; paid editions require inquiry	Published per-user pricing for some tiers; enterprise pricing varies
Suitable For	Small teams and analysts seeking a low-code path to tabular ML with API deployment	Enterprises seeking broader data/AI collaboration, governance, and operationalization	Organizations focused on analytics and data preparation with drag-and-drop workflows
Key Strength	Shorter path from data to deployed predictive models and API-first deployment options	Broad data handling, collaboration, and governance features	Extensive data preparation and analytics workflow tooling
Key Weakness	Limited publicly documented enterprise certifications and a narrower scope for enterprise-scale data engineering compared with some competitors	Pricing complexity and platform complexity at enterprise scale	Higher per-user price for some tiers; may be broader than required for simple ML tasks

01Competitive Landscape

Narrative competitive analysis

BigML vs DataRobot

DataRobot is positioned as an enterprise AutoML platform with extensive governance and deployment patterns. BigML publishes lower-cost entry points and may be suitable where a lighter-weight, API-centric approach is acceptable.

BigML vs Dataiku

Dataiku emphasizes enterprise collaboration, governance, and extensive data ingestion/wrangling. BigML provides a more focused, lower-friction path for tabular ML and API deployment.

BigML vs Alteryx

Alteryx targets organizations seeking advanced data preparation and analytics workflows. BigML focuses on the modeling and deployment layer for structured ML problems; Alteryx addresses a broader analytics problem set.

Market share / adoption signals

Third-party engagement metrics place BigML at a modest share in some Data Science Platform categories. Vendor-published adoption metrics indicate a sizable user community, but self-reported counts do not directly equate to enterprise penetration.

	BigML	Dataiku	Alteryx (Designer Cloud)
Starting Price	Free tier available; trial options	Free edition available; paid editions require inquiry	Published per-user pricing for some tiers; enterprise pricing varies
Suitable For	Small teams and analysts seeking a low-code path to tabular ML with API deployment	Enterprises seeking broader data/AI collaboration, governance, and operationalization	Organizations focused on analytics and data preparation with drag-and-drop workflows
Key Strength	Shorter path from data to deployed predictive models and API-first deployment options	Broad data handling, collaboration, and governance features	Extensive data preparation and analytics workflow tooling
Key Weakness	Limited publicly documented enterprise certifications and a narrower scope for enterprise-scale data engineering compared with some competitors	Pricing complexity and platform complexity at enterprise scale	Higher per-user price for some tiers; may be broader than required for simple ML tasks