Sacred
Python experiment management for reproducibility and organization.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Sacred?
Sacred is a Python tool designed to help configure, organize, log, and reproduce experiments. It provides a standard framework that can be extended with various add-ons, making it useful for researchers and developers in machine learning and data science.
Key differentiator
“Sacred stands out as an open-source, flexible tool that provides a standardized framework for experiment management and reproducibility in Python.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns and idioms, which can be challenging for developers unfamiliar with the language.
The official documentation lacks comprehensive examples and troubleshooting guides. Community forums and Q&A sites have limited activity compared to more popular tools.
Historical updates, such as the transition from v0.1 to v0.2, required significant rework of existing configurations and experiment definitions.
Performance bottlenecks can occur with a high volume of concurrent experiments or very complex configurations, leading to slower execution times.
Fit analysis
Who is it for?
✓ Best for
Researchers who need a standardized way to log and reproduce experiments
Teams working on machine learning projects that require detailed tracking of hyperparameters and results
Developers looking for an open-source solution for experiment management
✕ Not a fit for
Projects requiring real-time data processing or streaming analytics
Users who prefer a cloud-based service with managed infrastructure over self-hosted solutions
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
Alternatives
Works well with
Next step
Get Started with Sacred
Step-by-step setup guide with code examples and common gotchas.