Supports POSIX, HDFS, and S3 protocols for versatile access.
Separates data and metadata for elastic scalability and cost-effectiveness.
Pricing: Free plan available
Best for: Growing teams
Pros & Cons
Pros
Highly scalable for billions of files and petabytes of data
Cost-effective by utilizing object storage with high performance
Versatile protocol support allows broad application compatibility
Cloud-native design supports multi-cloud and hybrid environments
Open-source Community Edition provides flexibility and community support
Cons
Advanced features like higher availability SLAs and enterprise-grade support are in paid versions
Requires understanding of distributed systems and metadata engine choices for optimal setup
Community Edition metadata throughput is lower than Cloud/Enterprise Editions
Key Features
Data-metadata separated architectureSupports various open-source metadata engines (Redis, TiKV, MySQL, PostgreSQL, FoundationDB)High-performance distributed metadata service (Cloud/Enterprise Editions)Elastic and scalable throughput with distributed cachingCompatible with POSIX, HDFS, and S3 protocolsCloud-native for public, hybrid, and multi-cloud scenariosAutomatic data replication across clouds and regionsBuilt-in observability tools for system monitoring
Pricing Plans
Community Edition
Free
Open Source
Elastic, Multi-Cloud
Compatible with POSIX, HDFS, and S3 Protocols
Data-metadata separated architecture
Supports various open-source storage engines (Redis, PostgreSQL, TiKV)
Elastic and scalable throughput
In-house metadata handles millions of requests per second
Cost-effective object storage with distributed caching
Fully supports POSIX, HDFS, and S3 protocols
Caters to public clouds, hybrid setups, and multi-cloud scenarios
Leverages cloud elasticity for automatic data replication
JuiceFS is an open-source distributed file system designed for cloud environments, offering high performance, cost-effectiveness, and multi-protocol compatibility. It separates data and metadata, allowing users to leverage cost-efficient object storage for data while using various open-source databases (like Redis, PostgreSQL, TiKV) or a proprietary distributed metadata service for metadata management. This architecture enables independent scaling of data and metadata components, addressing data hotspots and supporting billions of files.
It is built for developers and organizations seeking a flexible and scalable storage solution for diverse applications, including AI/Machine Learning, Big Data, and Kubernetes data persistence. JuiceFS supports POSIX, HDFS, and S3 protocols, making it suitable for both new application development and seamless migration of existing workloads. It caters to public clouds, hybrid setups, and multi-cloud scenarios, facilitating automatic data replication and resilient architectures. The Community Edition is open-source under the Apache 2.0 license, while Cloud Service and Enterprise Edition offer additional features like higher availability SLAs, advanced metadata engines, and professional support.
JuiceFS is an open-source, high-performance, cloud-native distributed file system that separates data and metadata. It allows users to store data on cost-effective object storage while managing metadata with various databases, providing elastic scalability and compatibility with POSIX, HDFS, and S3 protocols.
How much does JuiceFS cost?
JuiceFS offers a free Community Edition under the Apache 2.0 license. It also provides a Cloud Service with a free trial and an Enterprise Edition, which are paid offerings with advanced features and professional support. Specific pricing for Cloud and Enterprise editions is not detailed on the provided pages but implies a paid model.
Is JuiceFS free?
Yes, JuiceFS has a free Community Edition released under the Apache 2.0 license. There is also a free trial available for the Cloud Service.
Who is JuiceFS for?
JuiceFS is for developers, enterprises, and organizations that require a scalable, high-performance, and cost-effective distributed file system for cloud environments. It is particularly well-suited for use cases in AI/Machine Learning, Big Data, Kubernetes data persistence, and scenarios requiring multi-cloud or hybrid cloud storage solutions.