PyGrid
Peer-to-peer network for federated learning with PySyft
Pricing
Free tier
Flat rate
Adoption
↘CoolingLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is PyGrid?
PyGrid is a peer-to-peer network that enables data owners and scientists to collectively train AI models using PySyft, ensuring privacy while leveraging distributed datasets.
Key differentiator
“PyGrid stands out by enabling federated learning in a privacy-preserving manner through its peer-to-peer network architecture, making it ideal for scenarios where data centralization is not feasible or desirable.”
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
Primarily integrates with PySyft, lacks native support for TensorFlow Federated or other popular federated learning libraries
Privacy-preserving methods like differential privacy and secure multi-party computation can introduce significant computational overhead
Fit analysis
Who is it for?
✓ Best for
Teams needing to train models on distributed datasets while maintaining strict privacy controls
Organizations with data sharing restrictions due to regulatory or privacy concerns
✕ Not a fit for
Projects requiring real-time model updates and predictions, as PyGrid focuses on federated learning setup
Applications where centralized data storage is preferred over a decentralized approach
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 PyGrid
Step-by-step setup guide with code examples and common gotchas.