Vector Search Databases
A list of databases and data stores specialized for Vector Search Databases.
| No | Name | Stars | Description | Trend | License | Language | Official Site |
|---|---|---|---|---|---|---|---|
| 1 | Elasticsearch + Vector Search | ⭐ 73,370 | Standard text search DB. Vector search support via kNN since v7.x. Can be integrated into existing Elasticsearch infrastructure. | First choice for existing Elastic users adopting vector search. Utilized for log analysis with ELK stack integration. | Elastic License 2.0 | Java | Official |
| 2 | Redis + Vector Search | ⭐ 70,233 | Fast in-memory DB with vector type support via extensions. Demonstrates superior query throughput and latency in benchmarks. | Enables vector search on existing Redis infrastructure. High performance with combination of real-time processing and caching. | BSD-3 | C | Official |
| 3 | FAISS | ⭐ 36,371 | Vector similarity search library developed by Facebook. Supports various indexing methods. Achieves highest raw speed with GPU acceleration. | Valued for specialized use cases requiring maximum algorithm flexibility and control. Adopted as foundation technology for many vector DBs. | MIT | C++, Python | Official |
| 4 | Milvus | ⭐ 36,232 | Open-source vector database for large-scale search. Popular for AI workloads like RAG and image search. Achieves fastest indexing time with high precision. | Highly popular with ~25k GitHub stars. GPU acceleration highly valued, with managed service Zilliz for enterprise. | Apache-2.0 | Go, C++ | Official |
| 5 | DuckDB + Vector | ⭐ 31,277 | OLAP RDBMS for embedded use. Supports vector similarity search. Reached 30k GitHub stars as of June 2025. | Gaining attention for integration of analytical workloads and vector search. Valued for combination of fast local analysis and AI capabilities. | MIT | C++ | Official |
| 6 | CockroachDB + Vector | ⭐ 31,127 | Distributed SQL DB with vector data storage support. PostgreSQL-compatible with pgvector extension support. | Over 20k GitHub stars. Suitable for scalable AI application development with combination of distributed SQL and vector search. | BSL-1.1 | Go | Official |
| 7 | MongoDB Atlas Vector Search | ⭐ 27,365 | NoSQL MongoDB now includes standard vector search API. Enables vector search within existing MongoDB infrastructure. | Provides vector search on most popular managed developer data platform. Growing adoption for RAG application development. | SSPL | C++ | Official |
| 8 | Qdrant | ⭐ 24,978 | OSS vector search DB built in Rust. Features filtering, payload support, and fast distributed operation. Achieves highest RPS and lowest latency in benchmarks. | 2025 performance leader. Best for complex metadata filtering with dynamic query planning and payload indexing. ~9k GitHub stars. | Apache-2.0 | Rust | Official |
| 9 | Typesense | ⭐ 24,009 | OSS alternative to Algolia + Pinecone. Fast search engine with typo tolerance. Developer-friendly design. | Popular as self-hostable vector search solution. Ideal for small to medium projects with simple API and fast performance. | GPL-3.0 | C++ | Official |
| 10 | Valkey | ⭐ 22,372 | OSS specialized for high-dimensional vector search. High performance and distributable. Supported by Google Cloud Memorystore. | Uses advanced data structures and indexing methods. Growing as cloud-native vector search solution. | BSD-3 | C | Official |
| 11 | Chroma | ⭐ 21,359 | OSS vector DB with excellent LLM/generative AI compatibility. Simple API ideal for prototyping. Best for small-scale projects and rapid prototyping. | Rapid growth with developer-friendly design. ~6k GitHub stars. Growing popularity for RAG application development. | Apache-2.0 | Python | Official |
| 12 | PostgreSQL + pgvector | ⭐ 16,802 | RDBMS + OSS extension 'pgvector' enables vector search. Enhanced with HNSW, IVF indexing. Achieves vector search within SQL stack. | ~4k GitHub stars. Popular as easiest way to add vector search to existing PostgreSQL applications. | PostgreSQL | C | Official |
| 13 | Weaviate | ⭐ 14,045 | Feature-rich OSS with graph query and schema definition capabilities. Provides complex data relationships, knowledge graph features, and GraphQL interface. | Top-class with >1M monthly Docker pulls. ~8k GitHub stars. Valued for hybrid filtering and OSS flexibility. | BSD-3 | Go | Official |
| 14 | Apache Cassandra + Vector | ⭐ 9,296 | Supports high-dimensional vector data storage and nearest neighbor search since v5.0. Achieves vector search in distributed NoSQL database. | Addresses vector search demands in large-scale distributed environments. Can add vector capabilities to existing Cassandra clusters. | Apache-2.0 | Java | Official |
| 15 | Deep Lake | ⭐ 8,742 | Easy integration with data version management. Convenient AI dataset management. Data lake optimized for vector embeddings. | Optimized HNSW implementation can query over 35M embeddings in under 1 second. Gaining attention as new approach to AI data management. | Apache-2.0 | Python | Official |
| 16 | LanceDB | ⭐ 7,182 | Lightweight vector DB suitable for local (edge) and embedded use. Serverless architecture using IVF_PQ algorithm. | Gaining attention with edge computing and AI convergence. Achieves fast retrieval by partitioning datasets and compressing vectors. | Apache-2.0 | Rust, Python | Official |
| 17 | Vespa | ⭐ 6,261 | Large-scale distributed search system developed by Yahoo!. Combines vector and structured search. Designed as low-latency computation engine. | Valued for low-latency computation on large datasets. Comprehensive solution integrating vector search with traditional search capabilities. | Apache-2.0 | Java, C++ | Official |
| 18 | sqlite-vec | ⭐ 5,927 | Vector search extension for SQLite. Successor to SQLite-VSS. Lightweight vector search solution that runs anywhere. | Development resources migrated from sqlite-vss. Ideal for vector search in embedded applications and edge devices. | MIT | C | Official |
| 19 | Marqo | ⭐ 4,909 | Gaining attention with cloud-ready simple API design. Abstracts interface at document level rather than pure vector data type implementation. | Rapid growth with developer experience-focused design. Valued as end-to-end vector search solution. | Apache-2.0 | Python | Official |
| 20 | Pinecone | - | Cloud-native managed vector database service. Features scalability and ease of management. Cannot run locally. | Popular for turnkey scale. Enterprise adoption growing with SOC 2/HIPAA compliance. ~400k monthly Docker pulls. | Proprietary | SaaS | Official |