Pinecone
Managed vector database for AI applications
Open-Source Projects
Discover top open-source software, updated regularly with real-world adoption signals.
Discover top open-source software, updated regularly with real-world adoption signals.
Distributed multi-modal vector database with MySQL compatibility
Open-source distributed vector database combining real-time strong consistency, relational semantics, and vector search with MySQL protocol compatibility and horizontal scalability.
DingoDB is a distributed multi-modal vector database that unifies relational and vector semantics into a single platform. Designed for enterprise-grade applications, it delivers real-time strong consistency, horizontal scalability, and elastic scaling capabilities while maintaining MySQL protocol compatibility.
DingoDB targets developers and organizations building AI-powered applications that require both structured data management and vector search capabilities. Teams seeking to avoid complex multi-database architectures will benefit from its unified SQL interface that handles traditional queries and vector operations seamlessly.
The platform supports scalar-vector hybrid retrieval, enabling sophisticated queries that combine traditional database filters with semantic search. Built-in high availability eliminates external dependencies, reducing deployment complexity. Automatic data sharding with dynamic splitting and merging adapts to workload changes without manual intervention. Real-time index optimization runs transparently in the background, ensuring consistent query performance. For large-scale deployments, cold-hot tiered storage minimizes memory footprint while maintaining search responsiveness.
DingoDB provides flexible access through SQL, SDK, and API interfaces, treating both tables and vectors as first-class data models. Licensed under Apache 2.0, it offers comprehensive multi-language support for diverse development environments.
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No. DingoDB provides built-in high availability configurations without requiring external components like ZooKeeper or etcd, reducing deployment complexity and operational overhead.
Yes. DingoDB offers MySQL protocol compatibility, allowing you to connect using standard MySQL clients, drivers, and tools while accessing both relational and vector capabilities.
DingoDB automatically splits and merges data shards based on configurable size thresholds, enabling elastic horizontal scaling without manual intervention as your dataset grows.
DingoDB supports various vector index types and can dynamically switch between memory-based and disk-based indexes depending on data scale, optimizing for both performance and resource consumption.
Yes. DingoDB supports distributed transaction processing that spans both scalar and vector data, ensuring consistency across hybrid queries and updates.
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