PySyft
Secure and private Deep Learning library built on PyTorch and TensorFlow.
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is PySyft?
PySyft is a Python library that enables secure and private deep learning by providing tools for differential privacy, federated learning, and secure multi-party computation. It's built on top of PyTorch and TensorFlow, making it easy to integrate into existing workflows while ensuring data privacy.
Key differentiator
“PySyft stands out by providing a comprehensive set of tools for ensuring data privacy and security in deep learning, making it ideal for applications that handle sensitive information.”
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
Primary support is for PyTorch and TensorFlow, other ML frameworks are not officially supported
Differential privacy and secure multi-party computation can significantly slow down model training and inference processes
Fit analysis
Who is it for?
✓ Best for
Teams working with sensitive data that require strict privacy guarantees
Developers looking to integrate federated learning into their applications
Researchers who need tools for secure multi-party computation in deep learning
✕ Not a fit for
Projects where performance is prioritized over privacy
Applications requiring real-time processing of large datasets without privacy constraints
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
Works well with
Next step
Get Started with PySyft
Step-by-step setup guide with code examples and common gotchas.