Valohai vs Determined

Valohai8.3Visit DDetermined8.2Visit

Higher score Valohai #6 in Experiment Tracking 8.3/10 Pricing on request · 14-day trial Free trial✓ 0 of 3 features Visit Valohai

vs

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

• Valohai fits best

  Mid-market and enterprise teams across cloud environments

• Determined fits best

  Teams training distributed models on their own

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Side by side

Feature	Valohai 8.3/10 Visit ↗	DDetermined 8.2/10 Visit ↗
At a glance
Editor score	8.3	8.2
Ranking	#6 in Experiment Tracking	#7 in Experiment Tracking
Best for	Mid-market and enterprise teams across cloud environments	Teams training distributed models on their own
Pricing model	Paid	Free
Starting price	Pricing on request · 14-day trial	Free plan
Free plan	—	✓ (best)
Free trial	14 days	—
Deployment	Cloud, Self-hosted	Cloud, Self-hosted
Platforms	Web	Web, Linux
Support	Email, Docs	Community
Integrations	20 integrations	6 integrations
Built for	Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Valohai 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, logs, parameters, and reproducibility information Links versioned models and data assets through lineage Runs workloads across clouds, Kubernetes, Slurm, and on-premises	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	No free plan; the trial lasts 14 days Paid pricing is quote-based rather than published Broad MLOps scope may exceed teams seeking tracking alone	Experiment tracking is narrower than leading suites Workflows are centered on Python Support is community-based
Our verdict	Valohai brings experiment tracking into a broader MLOps workflow for data scientists, ML engineers, and enterprise ML teams. It records metrics, logs, parameters, and reproducibility information, while connecting experiments to versioned… 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

• Valohai — where it wins

  • Tracks metrics, logs, parameters, and reproducibility information
  • Links versioned models and data assets through lineage
  • Runs workloads across clouds, Kubernetes, Slurm, and on-premises

  Where it doesn't

  • No free plan; the trial lasts 14 days
  • Paid pricing is quote-based rather than published
  • Broad MLOps scope may exceed teams seeking tracking alone

• 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

• Valohai8.3/10 · Pricing on request · 14-day trial

  A governed MLOps workspace spanning experiment tracking, pipelines, model management, and serving.

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

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

  Visit DeterminedFull verdict →