Opacus
Library for training PyTorch models with differential privacy.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Opacus?
Opacus is a library that enables developers to train machine learning models using PyTorch while ensuring the privacy of individual data points through differential privacy techniques. This tool is essential for organizations and researchers who need to comply with strict privacy regulations or protect sensitive user information during model training.
Key differentiator
“Opacus stands out by providing a specialized library for integrating differential privacy directly into PyTorch training pipelines, ensuring compliance and privacy without sacrificing performance.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
Opacus is tightly integrated with PyTorch, which primarily supports Python
Configuring noise multipliers and clipping norms requires deep understanding of differential privacy concepts
Adding noise for privacy can significantly slow down training processes, especially with large datasets
The official documentation focuses on basic integration and does not cover complex scenarios or optimizations in depth
Fit analysis
Who is it for?
✓ Best for
Teams that need to train PyTorch models on sensitive data while ensuring differential privacy compliance.
Researchers working on projects where individual data privacy is a critical concern.
✕ Not a fit for
Projects requiring real-time model training and inference, as Opacus focuses on privacy-preserving batch training.
Teams that do not require or are not concerned with differential privacy in their machine learning models.
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
Next step
Get Started with Opacus
Step-by-step setup guide with code examples and common gotchas.