BigML
7.7#1 in predictive analytics softwareFrom $10,000 a yearFree planWebAPImacOSSelf-hosted
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| Fact | BigML | dotData Enterprise |
|---|---|---|
| Deployment | Private deployments can run on a preferred cloud provider, ISP, or on-premises behind a corporate firewall, as managed or self-managed deployments. | The product page describes private-cloud deployment on customer AWS or Azure infrastructure and a hosted, managed dotData Cloud environment. |
| Explainability | Models provide interactive visualizations, prediction explanations, field importances and partial-dependence plots. | The product provides explainable features and models, including feature explanations and blueprint diagrams. |
| Founded | 2011 | 2018 |
| Headquarters | BigML lists Corvallis, Oregon as headquarters and Valencia, Spain as its European headquarters. | San Mateo, California, USA |
| Integrations | Official tools include Google Sheets, Zapier, Node-RED, Alexa, PredictServer and a native BigMLX macOS application. | Listed data sources include Amazon Redshift, Google BigQuery, Snowflake, Azure Synapse, Oracle, Teradata, Microsoft SQL Server, CSV, and Tableau Hyper files. |
| Security | BigML states that connections use HTTPS, resources are private by default, and staff cannot access customer data without explicit consent. | dotData reports completing a SOC 2 Type II audit for the fourth consecutive year, with the 2026 audit scope including generative AI integrations and self-managed deployments. |
| Support | BigML Lite includes standard 8x5 email and chat support with a 48-hour maximum response time; Enterprise lists customized email and chat with a 24-hour maximum response time. | dotData offers tailored support and asks prospective customers to request a demo to discuss their needs and use cases. |
BigML suits analysts, software developers, and scientists who need machine-learning tools, workflow automation, or model deployment options. Its API and language bindings may suit teams integrating models into software.
dotData Enterprise suits businesses building predictive models without requiring every user to have data-science knowledge. It also fits enterprise workflows needing large datasets, explainability, and private or hosted deployment.