Cleora
Rust-powered graph embedding engine for deterministic entity representation learning.
Pricing
About Cleora
Cleora is an open-source, CPU-only graph embedding engine designed for efficient and scalable computation of entity embeddings from large heterogeneous relational data. It uses deterministic sparse Markov propagation with interleaved whitening, eliminating the need for GPUs, negative sampling, or gradient approximation. The tool computes the exact distribution of all possible walks in a single sparse matrix operation, delivering production-grade embeddings from a single CPU core. Created by the Synerise team and released under the MIT license, Cleora is built with Rust for performance and provides a Python API for accessibility. It significantly outperforms comparable solutions—reportedly 197x faster than DeepWalk and 4x-8x faster than PyTorch-BigGraph. The tool works without parameters and requires no tuning, making it accessible for practitioners. A potential limitation is that it is specifically optimized for graph-based embedding tasks and may not be suitable for use cases requiring different data representations.
At a glance
- Company
- Synerise (est. 2020)
- Platforms
- Web, Python API
- API
- Available
- Last verified
- June 2026
Who It's For
- •Machine learning engineers and data scientists
- •Organizations processing large-scale graph data
- •Researchers in entity representation learning
- •Teams seeking computationally efficient embedding solutions without GPU infrastructure
How It Works
- 1Computes exact distribution of every possible walk in large graphs via single sparse matrix power operation
- 2Uses deterministic sparse Markov propagation with interleaved whitening algorithm
- 3Operates entirely on CPU without requiring GPU acceleration or negative sampling
- 4Generates entity embeddings in n-dimensional spaces from heterogeneous relational data
How to Use Cleora
- 1Install via pip: pip install pycleora
- 2Use Python API to load graph data
- 3Configure embedding parameters (parameter-free/minimal configuration)
- 4Generate embeddings for entities in the graph
- 5Access results through Python interface or export for downstream applications
Key Features
- •CPU-only computation with no GPU requirement
- •Parameter-free algorithm requiring minimal configuration
- •Deterministic sparse Markov propagation with interleaved whitening
- •Rust-based core with Python API for ease of use
- •Blazing fast performance on single CPU core
- •Production-grade embeddings without negative sampling or gradient approximation
Use Cases
- •Large-scale graph embedding generation for recommendation systems
- •Entity representation learning from heterogeneous relational data
- •Biomedical applications requiring efficient embedding computation
- •Machine learning pipelines requiring fast, deterministic embeddings
Pros & Cons
Advantages
- •Exceptional performance speed: 197x faster than DeepWalk and 4x-8x faster than PyTorch-BigGraph
- •No GPU required—CPU-only operation reduces infrastructure costs and complexity
- •Parameter-free design eliminates tuning overhead and makes it accessible for practitioners
- •MIT license allows commercial use with full open-source transparency
Disadvantages
- •Limited to graph-based embedding tasks; not suitable for other data representation needs
- •Relatively new and smaller ecosystem compared to established embedding frameworks
- •Requires some graph data preprocessing and understanding of graph structures for effective use
Alternatives
- No good alternatives.
- DeepWalk
- PyTorch-BigGraph
- Node2Vec
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Frequently Asked Questions
What is Cleora?
Cleora is an open-source, CPU-only graph embedding engine designed for efficient and scalable computation of entity embeddings from large heterogeneous relational data. It uses deterministic sparse Markov propagation with interleaved whitening, eliminating the need for GPUs, negative sampling, or gradient approximation.
How much does Cleora cost?
Cleora is free to use.
Is Cleora free?
Yes, Cleora offers a free plan you can start with.
What are the best Cleora alternatives?
Popular Cleora alternatives include No good alternatives., DeepWalk, PyTorch-BigGraph.
What is Cleora used for?
Cleora is commonly used for Large-scale graph embedding generation for recommendation systems, Entity representation learning from heterogeneous relational data, Biomedical applications requiring efficient embedding computation.
Does Cleora have an API?
Yes, Cleora offers an API for developers.
What platforms does Cleora support?
Cleora is available on Web, Python API.
Information Accuracy
Please note: While we regularly update all tool information including descriptions, features, pricing, and other details, this information may change over time as tools evolve and update their offerings. For the most current and accurate information, we recommend visiting the official website directly. Our goal is to provide you with comprehensive and up-to-date information to help you make informed decisions.