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Head-to-head · ML Experiment Tracking Software

Hopsworks AI Lakehouse vs DVCLive

  • Updated Oct 2026
  • Both researched from official sources
  • 6 checks side by side
Higher score Hopsworks AI Lakehouse #7 in ML Experiment Tracking Software 8.2/10 Free plan Free plan✓ 5 of 5 features Visit Hopsworks
DVCLive #8 in ML Experiment Tracking Software 8.1/10 Free plan Free plan✓ 3 of 5 features Visit DVCLive

Hopsworks AI Lakehouse leads on 0 checks, DVCLive on 0, and 6 are even. Who comes out ahead on the 6 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreHopsworks AI Lakehouse · 8.2/10
  • Free planboth
  • Most featuresHopsworks AI Lakehouse · 5 of 5

Hopsworks AI Lakehouse scores higher on our rubric for ml experiment tracking software: 8.2 against 8.1 out of 10; our editors rank them #7 and #8.

Hopsworks AI Lakehouse is the better fit for teams needing tracking plus feature governance. DVCLive is the better fit for open-source teams needing lightweight tracking.

  • Hopsworks AI Lakehouse fits best

    Teams needing tracking plus feature governance

  • DVCLive fits best

    Open-source teams needing lightweight tracking

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Side by side

Feature Hopsworks AI Lakehouse 8.2/10 Visit ↗ DVCLive 8.1/10 Visit ↗
At a glance
Editor score 8.2 8.1
Ranking #7 in ML Experiment Tracking Software #8 in ML Experiment Tracking Software
Best for Teams needing tracking plus feature governance Open-source teams needing lightweight tracking
Pricing model Free plan + paid Free
Starting price — Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud, Self-hosted Self-hosted
Support Community, Docs Community, Docs
Integrations 32+ integrations 14 integrations
Built for Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Features Hopsworks AI Lakehouse 5/5 · DVCLive 3/5
Run comparison ✓ ✓
Artifact tracking ✓ ✓
Dataset versioning ✓ (best) Not checked yet
Model registry ✓ (best) Not checked yet
API and SDK access ✓ ✓
Specs
Deployment options Not checked yet Self_hosted
Our review
Pros
  • Free plan includes experiment tracking, comparisons, artifacts, and one project
  • Covers datasets, features, models, serving, lineage, and monitoring
  • Supports web, API, self-hosted, cloud, on-premise, and air-gapped deployments
  • 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
  • The Free plan is limited to 1 project
  • SaaS costs vary with usage, complicating fixed-budget planning
  • Broader than a focused experiment-tracking product
  • Does not list verified model-registry features
  • Does not list verified dataset-versioning features
  • Platform coverage is not listed
Our verdict

Hopsworks AI Lakehouse brings experiment tracking into a wider platform for building and operating machine-learning and AI systems. It is suited to teams that need to record training runs, metrics, logs, hyperparameters, outputs, and…

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 →
  1. Hopsworks AI LakehouseML Experiment Tracking Software 8.2Free plan
  2. DVCLiveML Experiment Tracking Software 8.1Free plan

Strengths and trade-offs

  • Hopsworks AI Lakehouse — where it wins

    • Free plan includes experiment tracking, comparisons, artifacts, and one project
    • Covers datasets, features, models, serving, lineage, and monitoring
    • Supports web, API, self-hosted, cloud, on-premise, and air-gapped deployments

    Where it doesn't

    • The Free plan is limited to 1 project
    • SaaS costs vary with usage, complicating fixed-budget planning
    • Broader than a focused experiment-tracking product

  • 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

More comparisons

All ML experiment tracking software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update