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Determined AI vs ClearML

Determined AIClearML

Bottom line: Determined AI for deep learning research teams; ClearML for mL engineering teams.

Determined AIDetermined AI

Open-source deep learning platform for distributed training and hyperparameter tuning

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ClearMLClearML

Open-source MLOps platform for experiments, pipelines, and model management

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Votes00
PricingFreemiumFreemium
CategoryMlopsMlops
Tags
distributed-traininghyperparameter-tuningmlopsopen-sourcegpu-management
mlopsexperiment-trackingpipelinesdata-versioningopen-source
Best for
  • Deep learning research teams
  • Organizations sharing GPU clusters
  • Teams needing distributed training
  • ML engineering teams
  • Research groups
  • Data science orgs
Pros
  • Open source and free to self-host
  • Built-in distributed training
  • Automated hyperparameter tuning
  • Efficient GPU resource management and scheduling
  • Works with PyTorch and TensorFlow
  • Capable, production-grade open-source core
  • End-to-end coverage from tracking to serving
  • Fully self-hostable for data control
  • Strong experiment tracking and pipelines
  • Dataset versioning aids reproducibility
Cons
  • Focused on training, not full MLOps breadth
  • Quieter momentum than newer tools
  • Requires infrastructure to self-host
  • Enterprise features tied to HPE MLDE
  • Steeper setup than hosted services
  • Broad platform means a steeper learning curve
  • Self-hosting requires infrastructure effort
  • Pro overages are usage-based
  • Some advanced controls only on Scale/Enterprise
  • Smaller community than the largest MLOps tools

Comparison generated from each tool's listing. Add or remove tools above to change it.