Determined vs Azure Machine Learning

DDetermined8.2Visit Azure Machine Learning8.1Visit

Higher score D Determined #7 in Experiment Tracking 8.2/10 Free plan Free plan✓ 0 of 3 features Visit Determined

vs

Azure Machine Learning #8 in Experiment Tracking 8.1/10 Paid ✓ 0 of 3 features Visit Azure

• Determined fits best

  Teams training distributed models on their own

• Azure Machine Learning fits best

  Azure teams managing experiments in MLOps workflows

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Side by side

Feature	DDetermined 8.2/10 Visit ↗	Azure Machine Learning 8.1/10 Visit ↗
At a glance
Editor score	8.2	8.1
Ranking	#7 in Experiment Tracking	#8 in Experiment Tracking
Best for	Teams training distributed models on their own	Azure teams managing experiments in MLOps workflows
Pricing model	Free	Paid
Starting price	Free plan	—
Free plan	✓ (best)	—
Free trial	—	—
Deployment	Cloud, Self-hosted	Cloud
Platforms	Web, Linux	Web
Support	Community	Docs
Integrations	6 integrations	6 integrations
Built for	Small business, Mid-market, Enterprise	Mid-market, Enterprise
Features Determined 0/3 · Azure Machine Learning 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 code versions, metrics, checkpoints, and hyperparameters Combines distributed training with GPU scheduling and resource management Supports deployments to AWS, GCP, Kubernetes, and Slurm/PBS	Model metrics, charts, and evaluation workflows support experiment review Automated ML, training jobs, and reusable pipelines cover model development Batch and real-time endpoints support different deployment modes
Cons	Experiment tracking is narrower than leading suites Workflows are centered on Python Support is community-based	Compute and other Azure resources are billed separately No free plan is listed Some documented data connections are preview features scheduled for retirement
Our verdict	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 →	Azure Machine Learning is a cloud service for data scientists, ML professionals, and engineers managing the machine-learning lifecycle. It combines model development, automated machine learning, training jobs, pipelines, evaluation, MLOps,… Read the review →

Strengths and trade-offs

• 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

• Azure Machine Learning — where it wins

  • Model metrics, charts, and evaluation workflows support experiment review
  • Automated ML, training jobs, and reusable pipelines cover model development
  • Batch and real-time endpoints support different deployment modes

  Where it doesn't

  • Compute and other Azure resources are billed separately
  • No free plan is listed
  • Some documented data connections are preview features scheduled for retirement

• DDetermined8.2/10 · Free plan

  A focused option for teams training distributed models on their own infrastructure.

  Visit DeterminedFull verdict →
• Azure Machine Learning8.1/10 · Paid

  A broad Azure ML workflow with evaluation tools and usage-based compute costs.

  Visit AzureFull verdict →