Suggestions appear as you type. Use the up and down arrows to choose one and Enter to open it.

This page's audience our own tag's counts — open to see them
–Visits
–Page views
–Clicks to vendors
–Time on page
–Reading now
Clicks to vendors, by tool
  • –
Top countries
  • –
Devices
  • –

– · visits, page views and clicks counted by our own tag, which counts more than our checked analytics; every figure rounded down · how we count

Head-to-head · ML Experiment Tracking Software

Amazon SageMaker Experiments vs Valohai

  • Updated Oct 2026
  • Both researched from official sources
  • 5 checks side by side
Higher score Amazon SageMaker Experiments #4 in ML Experiment Tracking Software 8.6/10 Paid ✓ 4 of 5 features Visit AWS
Valohai #6 in ML Experiment Tracking Software 8.3/10 Pricing on request · 14-day trial Free trial✓ 1 of 5 features Visit Valohai

Amazon SageMaker Experiments leads on 0 checks, Valohai on 0, and 5 are even. Who comes out ahead on the 5 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 trialonly Valohai
  • Most featuresAmazon SageMaker Experiments · 4 of 5

Amazon SageMaker Experiments scores higher on our rubric for ml experiment tracking software: 8.6 against 8.3 out of 10; our editors rank them #4 and #6.

Amazon SageMaker Experiments is the better fit for AWS-native ML teams. Valohai is the better fit for governed multi-cloud enterprise ML teams.

  • Amazon SageMaker Experiments fits best

    AWS-native ML teams

  • Valohai fits best

    Governed multi-cloud enterprise ML teams

Advertiser disclosure: iTechGuides is reader-supported. Vendors can pay for top positions in our rankings and for a place on other products' pages, and we may earn a commission when you click some links. Bought places stand at the number bought; this page and our disclosure say so. Paying vendors may get a fuller, more detailed review from our editors, and that review can change their score. How we rank.

Side by side

Feature Amazon SageMaker Experiments 8.6/10 Visit ↗ Valohai 8.3/10 Visit ↗
At a glance
Editor score 8.6 8.3
Ranking #4 in ML Experiment Tracking Software #6 in ML Experiment Tracking Software
Best for AWS-native ML teams Governed multi-cloud enterprise ML teams
Pricing model Paid Paid
Starting price — Pricing on request · 14-day trial
Free plan — —
Free trial — 14 days
Deployment Cloud Cloud, Self-hosted
Platforms Web Web
Support Docs Email, Docs
Compliance SOC 2, ISO 27001, GDPR, PCI DSS SOC 2, GDPR, SSO/SAML, 2FA
Integrations 5 integrations 20 integrations
Built for Small business, Mid-market, Enterprise Mid-market, Enterprise
Features Amazon SageMaker Experiments 4/5 · Valohai 1/5
Run comparison ✓ (best) Not checked yet
Artifact tracking ✓ (best) Not checked yet
Dataset versioning Not checked yet Not checked yet
Model registry ✓ ✓
API and SDK access ✓ (best) Not checked yet
Specs
Deployment options Cloud Not checked yet
Our review
Pros
  • Tracks parameters, metrics, configurations, results, and artifacts
  • Compares experiment runs with metrics and charts
  • Connects MLflow, S3, registries, pipelines, APIs, and SDKs
  • 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
Cons
  • No free plan
  • Compute and storage are billed separately
  • Best suited to teams already centered on AWS services
  • Pricing requires contacting sales
  • There is no free plan
  • Its wider MLOps scope may exceed tracking-only requirements
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 →

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 →
  1. Amazon SageMaker ExperimentsML Experiment Tracking Software 8.6Paid
  2. ValohaiML Experiment Tracking Software 8.3Pricing on request · 14-day trial

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

  • 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
  • Amazon SageMaker Experiments8.6/10 · Paid

    A strong fit for AWS-native teams that need tracking, comparison, artifacts, and MLflow.

    Visit AWSFull verdict →
  • Valohai8.3/10 · Pricing on request · 14-day trial

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

    Visit ValohaiFull verdict →

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