Amazon SageMaker AI vs Determined

Amazon SageMaker AI8.4Visit DDetermined8.2Visit

Higher score Amazon SageMaker AI #5 in Experiment Tracking 8.4/10 Paid ✓ 0 of 3 features Visit SageMaker AI

vs

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

• Amazon SageMaker AI fits best

  AWS teams building and tracking models end to end

• Determined fits best

  Teams training distributed models on their own

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Side by side

Feature	Amazon SageMaker AI 8.4/10 Visit ↗	DDetermined 8.2/10 Visit ↗
At a glance
Editor score	8.4	8.2
Ranking	#5 in Experiment Tracking	#7 in Experiment Tracking
Best for	AWS teams building and tracking models end to end	Teams training distributed models on their own
Pricing model	Paid	Free
Starting price	Not checked yet	Free plan
Free plan	—	✓ (best)
Free trial	—	—
Deployment	Cloud	Cloud, Self-hosted
Platforms	Web	Web, Linux
Support	Docs	Community
Integrations	6 integrations	6 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Amazon SageMaker AI 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	MLflow-based experiment tracking with comparison and model registry Managed training, distributed workloads, pipelines, and multiple inference modes AWS ecosystem integrations plus SOC 2 and ISO 27001 compliance	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	Usage-based billing spans compute, storage, data processing, and related capabilities AWS Free Tier applies for two months but is not an unlimited free plan Cloud-only deployment does not provide an on-premises option	Experiment tracking is narrower than leading suites Workflows are centered on Python Support is community-based
Our verdict	Amazon SageMaker AI is a fully managed machine-learning service for data scientists, developers, and ML engineers building models on AWS. Its web-based Studio environment covers development through deployment, while managed infrastructure… 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 →

Strengths and trade-offs

• Amazon SageMaker AI — where it wins

  • MLflow-based experiment tracking with comparison and model registry
  • Managed training, distributed workloads, pipelines, and multiple inference modes
  • AWS ecosystem integrations plus SOC 2 and ISO 27001 compliance

  Where it doesn't

  • Usage-based billing spans compute, storage, data processing, and related capabilities
  • AWS Free Tier applies for two months but is not an unlimited free plan
  • Cloud-only deployment does not provide an on-premises option

• 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

• Amazon SageMaker AI8.4/10 · Paid

  End-to-end AWS model development with MLflow tracking, pipelines, registry, and managed deployment.

  Visit SageMaker AIFull verdict →
• DDetermined8.2/10 · Free plan

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

  Visit DeterminedFull verdict →