AI-Native Multi-Modal

Data Intelligence Platform

Flexible Deployment, Expert Support, Mutual Growth

Hyper-Converged Database Architecture

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.

MatrixOne Architecture

Storage

All Modality Data

Unify all data types—structured, JSON, text, and vectors—in a single storage layer. Break down data silos.

Serverless

Extreme Elasticity

Compute resources automatically scale in and out within seconds based on business demand, and scale to zero during periods of inactivity.

Sub-Second

Data Branching

Zero-copy technology, creating writable data branches in sub-second data sandbox for AI Agent development.

Any Data

Instant Rollback

Timestamp-based snapshot capabilities, instantly restoring databases or tables to any point in time.

TB-Scale

Real-time Analytics

Powerful distributed engine for ultra-fast queries on massive data, sub-second response for TB-scale analytics.

AI-Oriented

Search Engine

Built-in vector retrieval and full-text indexing—build RAG and other AI search applications without dedicated components.

Simple Data

Sharing & Collaboration

Secure, zero-copy data sharing enables seamless data access and highly efficient collaboration across teams and tenants without duplicating data.

MySQL

Compatible

Highly compatible with MySQL 8.0 protocol, syntax, and ecosystem—seamless migration for existing applications.

Enterprise-Grade

High Availability

Ensure enterprise data security with Change Data Capture (CDC), log replication for disaster recovery, and point-in-time recovery via snapshot backups.

Usage-Based

Billing

Compute resources billed by actual SQL consumption—pay only for what you use, achieving optimal cost efficiency.

Ecosystem Compatibility

ETL Tools

DataX,Canal,KettleSeaTunnel,FlinkCDC

Compute Engines

Spark, Flink

BI Tools

Superset, TableauFineBI,永洪BI

Data Scheduling

DolphinScheduler

AI Frameworks

Dify,LangchainMCP协议

HTAP Hybrid Workload

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HTAP Hybrid Workload

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

Git for data

git-for-data

Git for data

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.

Multi-Modal Data Management

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Multi-Modal Data Management

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.

Vector and Text Hybrid Searchl

vector

Vector and Text Hybrid Searchl

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.

Large Model IntegrationàAI Ecosystem Integration

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Large Model IntegrationàAI Ecosystem Integration

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.

Enterprise-Grade Disaster Recovery and High Availability

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Enterprise-Grade Disaster Recovery and High Availability

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.

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Expert technical support and deep business collaboration
Custom solutiondesign with ongoing optimization
Expert support services with rapid incident response