lifelines vs Scikit-Survival
Side-by-side — pricing, strengths, tradeoffs, and fit.
lifelines
Survival analysis library in pure Python
Scikit-Survival
Survival analysis built on top of scikit-learn for Python.
At a glance
Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0
Overview
One-liner
One-liner
Survival analysis library in pure Python
One-liner
Survival analysis built on top of scikit-learn for Python.
Maturity
Maturity
EstablishedMaturity
EstablishedOpen 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
Research teams needing comprehensive survival analysis tools written in Python
Academics working on time-to-event data who prefer open-source solutions
Business analysts looking to predict customer churn using survival models
✓ Best for
Researchers needing to perform survival analysis within a familiar scikit-learn framework
Projects requiring integration with existing scikit-learn workflows for preprocessing and validation
Not ideal for
✕ Not for
Teams requiring real-time analysis as lifelines is designed for batch processing
Developers needing a web-based interface, as it's primarily a Python library
✕ Not for
Developers looking for a cloud-based service for survival analysis
Teams preferring proprietary software over open-source solutions
Strengths
↑ Strengths
Estimation of survival and hazard functions
Cox proportional hazards model
Aalen additive model
Kaplan-Meier estimator
↑ Strengths
Survival analysis built on top of scikit-learn
Integration with scikit-learn for preprocessing and cross-validation
Supports various survival models including Cox proportional hazards model
Weaknesses
↓ Weaknesses
Limited support for advanced survival analysis techniques
Poor documentation for certain features and methods
Performance issues with large datasets
Limited integration options with other data science tools
↓ Weaknesses
Limited model diversity compared to specialized survival analysis software
Performance issues with large datasets
Sparse community support and documentation