TiDB is built for agentic workloads that grow unpredictably, with ACID guarantees and native support for transactions, analytics, and vector search. No data silos. No noisy neighbors. No infrastructure ceiling.

The Fastest Distributed Database for Transactional, Analytical, and AI Workloads.

Simple, Elastic-quality search for Postgres

A cloud-native database based on PostgreSQL developed by Alibaba Cloud.

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AI-native HTAP database with Git-for-Data and built-in vector search, serving as the data and memory backbone for intelligent agents and applications.

PolarDB-X is a cloud native distributed SQL Database designed for high concurrency, massive storage, complex querying scenarios.

Tonbo is an embedded database for serverless and edge runtimes.

BaikalDB, A Distributed HTAP Database.

StoneDB is an Open-Source MySQL HTAP and MySQL-Native DataBase for OLTP, Real-Time Analytics, a counterpart of MySQLHeatWave. (https://stonedb.io)

Unified MySQL, Postgres & FlightSQL Server, Powered by DuckDB.

The Next-Gen Database for AI—an infrastructure designed for data and AI. As the MySQL of the AI era.

PolarDB-X is a cloud native distributed SQL Database designed for high concurrency, massive storage, complex querying scenarios.

Low-Latency Transaction Scheduling via Userspace Interrupts: Why Wait or Yield When You Can Preempt? (SIGMOD 2025 Best Paper Award)

Real-time streaming ingestion (CDC) for HTAP in PostgreSQL

In-memory, columnar, arrow-based database.

A PostgreSQL extension providing two high-performance Table Access Methods designed for analytical and HTAP workloads — with **significant storage savings and no meaningful query performance penalty** compared to standard heap tables.

a serverless db service based on TiDB on the cloud, implementing dynamically scale the compute and storage nodes based on business load changes.

In a nutshell, EinsteinDB is a persistent indexing scheme based off of LSH-KVX that exploits the distinct merits of hash index and B+-Tree index to support range scan and avoids long NVM writes for maintaining consistency; thus improving on LSH’s performance guarantees for skewed data and adopts ordered-write consistency to ensure crash consiste…