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BigML vs MLJAR AutoML

BigML

7.7#1 in predictive analytics softwareFrom $10,000 a year
Free planWebAPImacOSSelf-hosted
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MLJAR AutoML

7.1#3 in automl softwareFrom $20 a month
Free planWebLinuxmacOSSelf-hosted
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What both publish

FactBigMLMLJAR AutoML
DeploymentPrivate deployments can run on a preferred cloud provider, ISP, or on-premises behind a corporate firewall, as managed or self-managed deployments.Generated Mercury apps can be previewed locally, published through MLJAR Platform, or self-hosted with Docker.
HeadquartersBigML lists Corvallis, Oregon as headquarters and Valencia, Spain as its European headquarters.Łapy, Poland
IntegrationsOfficial tools include Google Sheets, Zapier, Node-RED, Alexa, PredictServer and a native BigMLX macOS application.The Python package integrates with Mercury to generate prediction apps that support single predictions and batch CSV predictions.
SupportBigML 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.MLJAR’s About page invites users to contact the team with questions and says they are happy to help.

BigML plans

  • FREEFree
  • Basic BigML training$800, paid once
  • BigML Lite$1,000 a month
  • Extra Support$3,000 a month
  • BigML Lite (annual)$10,000 a year
  • Bronze Enterprise$45,000 a year

MLJAR AutoML plans

  • FreeFree
  • mljar-supervised Python packageFree
  • Pro$20 a month
  • Business$60 a month
  • Perpetual license$199, paid once

Pick BigML if…

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.

Pick MLJAR AutoML if…

MLJAR AutoML suits people building supervised models from tabular data who want notebook or Python workflows. It is not aimed at forecasting or time-series modeling, and users should account for variation in reports and explanations.

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