
Pinecone
Managed vector database for AI applications
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.
When teams consider DingoDB, these hosted platforms usually appear on the same shortlist.
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
E-commerce Product Search
Combine semantic similarity search on product descriptions with structured filters for price, category, and inventory using unified SQL queries
Customer Support Knowledge Base
Enable hybrid retrieval across support tickets and documentation, filtering by metadata while ranking results by semantic relevance
Real-Time Recommendation Engine
Deliver personalized recommendations by joining user profiles with vector embeddings of content, maintaining strong consistency across distributed nodes
Multi-Tenant SaaS Analytics
Scale automatically across tenants with elastic sharding while providing each customer isolated semantic search over their structured datasets
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.
Project at a glance
ActiveLast synced 4 days ago