Valohai vs DVCLive

Valohai8.3Visit DVCLive8.1Visit

Higher score Valohai #6 in ML Experiment Tracking Software 8.3/10 Pricing on request · 14-day trial Free trial✓ 1 of 5 features Visit Valohai

vs

DVCLive #8 in ML Experiment Tracking Software 8.1/10 Free plan Free plan✓ 3 of 5 features Visit DVCLive

• Valohai fits best

  Governed multi-cloud enterprise ML teams

• DVCLive fits best

  Open-source teams needing lightweight tracking

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Side by side

Feature	Valohai 8.3/10 Visit ↗	DVCLive 8.1/10 Visit ↗
At a glance
Editor score	8.3	8.1
Ranking	#6 in ML Experiment Tracking Software	#8 in ML Experiment Tracking Software
Best for	Governed multi-cloud enterprise ML teams	Open-source teams needing lightweight tracking
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	Self-hosted
Support	Email, Docs	Community, Docs
Integrations	20 integrations	14 integrations
Built for	Mid-market, Enterprise	Solo, Small business, Mid-market, Enterprise
Features Valohai 1/5 · DVCLive 3/5
Run comparison	Not checked yet	✓ (best)
Artifact tracking	Not checked yet	✓ (best)
Dataset versioning	Not checked yet	Not checked yet
Model registry	✓ (best)	Not checked yet
API and SDK access	Not checked yet	✓ (best)
Specs
Deployment options	Not checked yet	Self_hosted
Our review
Pros	Tracks metrics, logs, parameters, code, data, and reproducibility information Connects registries, pipelines, serving, and data versioning in one platform Supports multi-cloud, Kubernetes, Slurm, on-premises, and distributed training	Logs metrics, parameters, plots, images, and model artifacts Compares experiments through DVC CLI and DVC Studio Integrates with major ML frameworks, including PyTorch and TensorFlow
Cons	Pricing requires contacting sales There is no free plan Its wider MLOps scope may exceed tracking-only requirements	Does not list verified model-registry features Does not list verified dataset-versioning features Platform coverage is not listed
Our verdict	Valohai is an MLOps platform for data scientists, ML engineers, and enterprise ML teams. Its experiment tracking covers metrics, logs, parameters, and reproducibility information, while the wider platform manages code, data, models,… Read the review →	DVCLive is an open-source Python library for recording machine-learning metrics, parameters, plots, images, and artifacts in simple file formats. It is aimed at developers and machine-learning teams that want experiment tracking without… Read the review →

Strengths and trade-offs

• Valohai — where it wins

  • Tracks metrics, logs, parameters, code, data, and reproducibility information
  • Connects registries, pipelines, serving, and data versioning in one platform
  • Supports multi-cloud, Kubernetes, Slurm, on-premises, and distributed training

  Where it doesn't

  • Pricing requires contacting sales
  • There is no free plan
  • Its wider MLOps scope may exceed tracking-only requirements

• DVCLive — where it wins

  • Logs metrics, parameters, plots, images, and model artifacts
  • Compares experiments through DVC CLI and DVC Studio
  • Integrates with major ML frameworks, including PyTorch and TensorFlow

  Where it doesn't

  • Does not list verified model-registry features
  • Does not list verified dataset-versioning features
  • Platform coverage is not listed

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

  A broad MLOps platform for teams that need tracking plus production governance.

  Visit ValohaiFull verdict →
• DVCLive8.1/10 · Free plan

  A lightweight, free tracker for teams that prefer local files and open tooling.

  Visit DVCLiveFull verdict →