AI-Native Multi-Modal
Data Intelligence Platform
Flexible Deployment, Expert Support, Mutual Growth
MatrixOne, as a next-generation hyper-converged, heterogeneous, cloud-native database management system, leverages a newly designed unified distributed database engine to flexibly support data management and applications across diverse workloads including OLTP, OLAP, and Streaming. Users can seamlessly deploy and operate it across public clouds, self-built database centers, and edge nodes.

Unify all data types—structured, JSON, text, and vectors—in a single storage layer. Break down data silos.
Compute resources automatically scale in and out within seconds based on business demand, and scale to zero during periods of inactivity.
Zero-copy technology, creating writable data branches in sub-second data sandbox for AI Agent development.
Timestamp-based snapshot capabilities, instantly restoring databases or tables to any point in time.
Powerful distributed engine for ultra-fast queries on massive data, sub-second response for TB-scale analytics.
Built-in vector retrieval and full-text indexing—build RAG and other AI search applications without dedicated components.
Secure, zero-copy data sharing enables seamless data access and highly efficient collaboration across teams and tenants without duplicating data.
Highly compatible with MySQL 8.0 protocol, syntax, and ecosystem—seamless migration for existing applications.
Ensure enterprise data security with Change Data Capture (CDC), log replication for disaster recovery, and point-in-time recovery via snapshot backups.
Compute resources billed by actual SQL consumption—pay only for what you use, achieving optimal cost efficiency.
DataX,Canal,KettleSeaTunnel,FlinkCDC
Spark, Flink
Superset, TableauFineBI,永洪BI
DolphinScheduler
Dify,LangchainMCP协议

Handles mixed OLTP and OLAP workloads through compute-storage separation, read-write isolation, and load partitioning, flexibly managing seamless queries and batch analytics.

Supports zero-cost rapid creation and management of data branches in development, testing, pre-production, AI model training and other scenarios, providing exceptional development flexibility.

Supports text, image, audio, video, and other multi-modal data formats, along with external file management capabilities. Effortlessly handles structured and semi-structured data for unified storage and querying.

Built-in vector indexing and full-text search enable mixed vector-text queries, delivering more precise multi-modal search results to empower various generative AI applications.

Supports UDF and stored procedures to deploy and run machine learning models, as well as connecting to and invoking LLM models, enabling compatibility with developers' existing AI infrastructure.

Supports disaster recovery through logical backup, physical backup, and snapshot backup. Also supports Event-Driven Dual-Engine Architecture based on transaction logs, providing enterprise-grade high availability and disaster recovery capabilities.