Head-to-head · ML Experiment Tracking Software
Amazon SageMaker Experiments vs Hopsworks AI Lakehouse
Amazon SageMaker Experiments leads on 0 checks, Hopsworks AI Lakehouse on 1, and 5 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 scoreAmazon SageMaker Experiments · 8.6/10
- Free planonly Hopsworks AI Lakehouse
- Most featuresHopsworks AI Lakehouse · 5 of 5
Amazon SageMaker Experiments scores higher on our rubric for ml experiment tracking software: 8.6 against 8.2 out of 10; our editors rank them #4 and #7.
Hopsworks AI Lakehouse offers free plan; Amazon SageMaker Experiments doesn't.
Amazon SageMaker Experiments is the better fit for AWS-native ML teams. Hopsworks AI Lakehouse is the better fit for teams needing tracking plus feature governance.
- Amazon SageMaker Experiments fits best
AWS-native ML teams
- Hopsworks AI Lakehouse fits best
Teams needing tracking plus feature governance
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Side by side
| Feature | Amazon SageMaker Experiments 8.6/10 Visit ↗ | Hopsworks AI Lakehouse 8.2/10 Visit ↗ |
|---|---|---|
| At a glance | ||
| Editor score | 8.6 | 8.2 |
| Ranking | #4 in ML Experiment Tracking Software | #7 in ML Experiment Tracking Software |
| Best for | AWS-native ML teams | Teams needing tracking plus feature governance |
| Pricing model | Paid | Free plan + paid |
| Starting price | — | — |
| Free plan | — | ✓ (best) |
| Free trial | — | — |
| Deployment | Cloud | Cloud, Self-hosted |
| Platforms | Web | Web |
| Support | Docs | Community, Docs |
| Compliance | SOC 2, ISO 27001, GDPR, PCI DSS | SOC 2, ISO 27001, GDPR, 2FA |
| Integrations | 5 integrations | 32+ integrations |
| Built for | Small business, Mid-market, Enterprise | Mid-market, Enterprise |
| Features Amazon SageMaker Experiments 4/5 · Hopsworks AI Lakehouse 5/5 | ||
| Run comparison | ✓ | ✓ |
| Artifact tracking | ✓ | ✓ |
| Dataset versioning | Not checked yet | ✓ (best) |
| Model registry | ✓ | ✓ |
| API and SDK access | ✓ | ✓ |
| Specs | ||
| Deployment options | Cloud | Not checked yet |
| Our review | ||
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| Our verdict | Amazon SageMaker Experiments helps data scientists and developers organize, track, analyze, and compare machine learning experiments and training runs. It supports inputs, parameters, configurations, metrics, results, and output artifacts,… Read the review → |
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 → |
Strengths and trade-offs
Amazon SageMaker Experiments — where it wins
- Tracks parameters, metrics, configurations, results, and artifacts
- Compares experiment runs with metrics and charts
- Connects MLflow, S3, registries, pipelines, APIs, and SDKs
Where it doesn't
- No free plan
- Compute and storage are billed separately
- Best suited to teams already centered on AWS services
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
- Amazon SageMaker Experiments8.6/10 · Paid
A strong fit for AWS-native teams that need tracking, comparison, artifacts, and MLflow.
Visit AWSFull verdict → - Hopsworks AI Lakehouse8.2/10 · Free plan
A broad AI lakehouse for teams that need tracking alongside feature and model governance.
Visit HopsworksFull verdict →
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Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026
Last updated · How we research and update