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Head-to-head · Experiment Tracking

ClearML vs Determined

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score ClearML #3 in Experiment Tracking 8.6/10 Free plan Free plan✓ 0 of 3 features Visit ClearML
Determined #7 in Experiment Tracking 8.2/10 Free plan Free plan✓ 0 of 3 features Visit Determined

ClearML leads on 0 checks, Determined on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreClearML · 8.6/10
  • Free planboth

ClearML scores higher on our rubric for experiment tracking: 8.6 against 8.2 out of 10; our editors rank them #3 and #7.

ClearML is the better fit for teams wanting open-source tracking with deployment options. Determined is the better fit for teams training distributed models on their own.

  • ClearML fits best

    Teams wanting open-source tracking with deployment options

  • Determined fits best

    Teams training distributed models on their own

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Side by side

Feature ClearML 8.6/10 Visit ↗ Determined 8.2/10 Visit ↗
At a glance
Editor score 8.6 8.2
Ranking #3 in Experiment Tracking #7 in Experiment Tracking
Best for Teams wanting open-source tracking with deployment options Teams training distributed models on their own
Pricing model Free plan + paid Free
Starting price $15/user/mo Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud, Self-hosted Cloud, Self-hosted
Platforms Web Web, Linux
Support Email, Community, Docs Community
Integrations 12 integrations 6 integrations
Built for Solo, Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features ClearML 0/3 · Determined 0/3
Self-hosted option Not checked yet Not checked yet
Artifact storage Not checked yet Not checked yet
Run comparison Not checked yet Not checked yet
Specs
Free plan seats Not checked yet Not checked yet
Version control Not checked yet Not checked yet
Deployment options Not checked yet Not checked yet
Our review
Pros
  • Tracks metrics, plots, logs, artifacts, and experiment comparisons
  • Connects dataset versioning, model management, and pipeline orchestration
  • Offers hosted SaaS and self-hosted deployment options
  • Tracks code versions, metrics, checkpoints, and hyperparameters
  • Combines distributed training with GPU scheduling and resource management
  • Supports deployments to AWS, GCP, Kubernetes, and Slurm/PBS
Cons
  • Pro adds usage-based storage and activity charges to its monthly price
  • Scale and Enterprise use quote-based pricing
  • Some capabilities, including RBAC and LDAP, are Enterprise-tier features
  • Experiment tracking is narrower than leading suites
  • Workflows are centered on Python
  • Support is community-based
Our verdict

ClearML brings experiment tracking together with dataset and model management, workflow orchestration, compute scheduling, and model serving. It is suited to data scientists and ML engineering teams that want to manage more of the model…

Read the review →

Determined brings distributed training, hyperparameter tuning, experiment tracking, and resource management into an open-source deep learning platform. It is aimed at researchers and machine learning teams that train models on their own…

Read the review →
  1. ClearMLExperiment Tracking 8.6Free plan
  2. DeterminedExperiment Tracking 8.2Free plan

Strengths and trade-offs

  • ClearML — where it wins

    • Tracks metrics, plots, logs, artifacts, and experiment comparisons
    • Connects dataset versioning, model management, and pipeline orchestration
    • Offers hosted SaaS and self-hosted deployment options

    Where it doesn't

    • Pro adds usage-based storage and activity charges to its monthly price
    • Scale and Enterprise use quote-based pricing
    • Some capabilities, including RBAC and LDAP, are Enterprise-tier features

  • Determined — where it wins

    • Tracks code versions, metrics, checkpoints, and hyperparameters
    • Combines distributed training with GPU scheduling and resource management
    • Supports deployments to AWS, GCP, Kubernetes, and Slurm/PBS

    Where it doesn't

    • Experiment tracking is narrower than leading suites
    • Workflows are centered on Python
    • Support is community-based

More comparisons

All experiment tracking comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update