EvalML vs H2O AutoML
Side-by-side — pricing, strengths, tradeoffs, and fit.
EvalML
Automates ML pipeline creation and evaluation with domain-specific functions.
H2O AutoML
Automates machine learning workflow for faster model development.
At a glance
Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0
Overview
One-liner
One-liner
Automates ML pipeline creation and evaluation with domain-specific functions.
One-liner
Automates machine learning workflow for faster model development.
Maturity
Maturity
DecliningMaturity
Not enough dataOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Teams needing to automate the creation and optimization of ML pipelines
Projects where domain-specific functions can significantly improve model accuracy
✓ Best for
Teams needing rapid prototyping and model development without extensive ML expertise.
Projects where time-to-market is critical, and manual tuning of models is not feasible.
Not ideal for
✕ Not for
Developers looking for a cloud-based managed service
Projects requiring real-time data processing capabilities
✕ Not for
Real-time applications requiring low-latency predictions
Scenarios with extremely limited computational resources
Strengths
↑ Strengths
Automated pipeline creation and optimization
Domain-specific functions for ML tasks
Evaluation of pipelines with various metrics
↑ Strengths
Automated model training and tuning
Supports multiple algorithms for classification and regression tasks
Automatic feature engineering
Built-in cross-validation
Weaknesses
↓ Weaknesses
Limited integrations with non-Python data sources and tools
Performance issues with large datasets
↓ Weaknesses
Limited support for advanced customization
Performance issues with very large datasets