CatBoost
Gradient boosting library with native categorical feature support.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Verified · Jul 12, 2026Overview
What is CatBoost?
CatBoost is a general-purpose gradient boosting library that supports categorical features out of the box. It offers fast inference and can run on CPU or GPU, including multi-GPU setups.
Key differentiator
“CatBoost stands out with its native support for categorical features, making it particularly effective in scenarios where feature preprocessing would otherwise be complex or time-consuming.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
Official support is primarily for Python and C++, other languages rely on community efforts
Requires specific configurations and dependencies that can be challenging to set up correctly
Fit analysis
Who is it for?
✓ Best for
Teams needing high-performance gradient boosting models with categorical feature support
Developers working on CPU or GPU environments who require fast inference times
Projects that benefit from easy installation and integration
✕ Not a fit for
Scenarios where real-time streaming data processing is required (CatBoost focuses on batch processing)
Use cases requiring a web-based UI for model training and deployment
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Next step
Get Started with CatBoost
Step-by-step setup guide with code examples and common gotchas.