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Software · head to head

Weights & Biases vs H2O.ai

Weights & Biases logo

Weights & Biases

Software

Developer tools for machine learning

From
Free
Rated
-
Full reviewWebsite
H2O.ai logo

H2O.ai

Software

AI Cloud for building and deploying AI applications

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Weights & Biases pricing can be prohibitive for large teams without enterprise discounts; H2O.ai java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
  • They diverge on capability: Weights & Biases covers Experiment tracking, H2O.ai covers AutoML.

Where they differ

Only the attributes on which Weights & Biases and H2O.ai actually diverge.

Attributes where Weights & Biases and H2O.ai differ
AttributeWeights & BiasesH2O.ai
Pricing modelUnknownfreemium
PlatformsWeb, Python SDK, REST APIWeb, Cloud
Founded20172011

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Weights & Biases

  • Experiment tracking
  • Dataset versioning
  • Model registry
  • Hyperparameter sweeps
  • Collaborative dashboards
  • PyTorch
  • TensorFlow
  • Keras

Only in H2O.ai

  • AutoML
  • Distributed computing
  • Feature engineering
  • Model explainability
  • Time series forecasting
  • Spark
  • Hadoop
  • Python

Both cover

  • Web support
  • Linux support
  • Mac support
  • Windows support

What people use each for

The jobs each tool is most often brought in to do.

Weights & Biases

  • Machine learningnot H2O.ai
  • Data analysisnot H2O.ai
  • Model trainingnot H2O.ai
  • Predictive analyticsnot H2O.ai

H2O.ai

  • Distributed in-memory machine learning over large datasetsnot Weights & Biases
  • Training and productionising models from R or Python against a shared H2O clusternot Weights & Biases

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Weights & Biases

  • Pricing can be prohibitive for large teams without enterprise discounts
  • Limited integrations compared to some competitors
  • Dashboard customization options limited on lower plans
  • Requires some setup and configuration knowledge

H2O.ai

  • Java is always required to run H2O-3 even when working from R or Python, and only a 64-bit JRE or JDK is supported
  • Supported Java versions stop at Java SE 17; newer versions only run by forcing an unsupported version flag and are guaranteed for experiments rather than production
  • H2O-3 only supports numpy below version 2, so a numpy 2 environment must be downgraded
  • Supported Python versions are limited to 3.7 through 3.11
  • The Flow web UI requires an internet browser and is the only graphical interface

Pricing, plan by plan

Weights & Biases

Free
  • FreeFree
    • 5 model seats
    • 5 GB storage
    • 1 GB/month Weave ingestion
  • Pro$60/month
    • 10 seats
    • 100 GB storage
    • Private projects
  • Teams$179/month
    • Team collaboration
    • Advanced analytics
    • Dedicated support

H2O.ai

Free
  • H2O-3 Open SourceFree
    • Core algorithms
    • AutoML
    • Community support
  • Driverless AIFree
    • Automatic feature engineering
    • Model explainability
    • Enterprise support

Which should you pick?

Choose Weights & Biases if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Web, Python SDK, REST API.
  • You also want dataset versioning.
Weights & Biases review

Choose H2O.ai if

  • You need automl.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want distributed computing.
H2O.ai review

Questions people ask

Is Weights & Biases or H2O.ai better?
Neither clearly leads. Weights & Biases starts at Free and H2O.ai at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Weights & Biases or H2O.ai?
Weights & Biases starts at Free and H2O.ai at Free.
Does Weights & Biases or H2O.ai run on more platforms?
Weights & Biases runs on Web, Python SDK, REST API. H2O.ai runs on Web, Cloud.
Can I use Weights & Biases for free?
Both have a free tier, so you can try either at no cost before committing.
What is Weights & Biases best used for?
Weights & Biases is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what H2O.ai is typically brought in for.
What can Weights & Biases do that H2O.ai cannot?
Weights & Biases covers Experiment tracking, Dataset versioning, Model registry, Hyperparameter sweeps. H2O.ai covers AutoML, Distributed computing, Feature engineering, Model explainability. Both handle Web support, Linux support, Mac support, Windows support.

Answered from the vendors’ own pages

Weights & Biases: Does Weights & Biases have a free plan?

Yes. The Free tier includes 5 model seats, 5 GB storage, and 1 GB/month Weave ingestion. Academic users get unlimited tracked hours, 200 GB storage, and 100 seats at no cost.

Source
H2O.ai: Is H2O open source and free?

Yes. H2O-3 OSS is free and Apache-licensed, designed for self-managed and experimental workflows. H2O.ai also offers enterprise cloud solutions with additional features.

Source
Weights & Biases: What are the paid plans for Weights & Biases?

Pro starts at $60/month with 10 seats and 100 GB storage. Team plans start at $179/month. Enterprise pricing is custom.

Source
H2O.ai: How many companies use H2O's open source platform?

Over 18,000 companies across Finance, Insurance, Healthcare, Retail, Telco, Sales, and Marketing use H2O's open-source machine learning platform.

Source
Weights & Biases: What machine learning features does W&B provide?

Weights & Biases captures hyperparameters, metrics, and model outputs automatically. Features include experiment tracking, interactive Reports for sharing findings, Artifacts for managing datasets and models, advanced hyperparameter sweeps, and model deployment tools.

Source
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