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Unique list of open-source vector databases, libraries, and versatile platforms with vector functionality

Milvus, Qdrant, Faiss, Weaviate, and other databases to work with LLMs and other foundation models

In this short article, you’ll find a list of

  • open-source vector databases

  • embedding and similarity search libraries

  • general-purpose platforms that have vector capabilities

This is a comprehensive list that you won't find anywhere else.

According to a research report on the "Vector Database Market", the sector is expected to grow significantly from $1.5 billion in 2023 to $4.3 billion by 2028, representing a compound annual growth rate (CAGR) of 23.3%.

While traditional databases have been useful for handling simple, structured data like numbers and text, they are not equipped to handle more complex types of data, such as those used in fields like machine learning and deep learning. This is where vector databases come in.

If you're interested in learning more about vector embeddings, the vector database pipeline, vector database alternatives, and how to choose a vector database, we have an informative article available for you to read.

Now, to the list!

Open-Source Vector Databases:

  • Milvus: Designed for embedding similarity search in AI applications, supports unstructured data search.

  • Chroma: Fast solution for Python or JavaScript LLM applications with efficient memory management.

  • Weaviate: AI-native, enables intuitive AI-powered applications and complex searches.

  • Qdrant: Tailored for extended filtering support, offering a production-ready service.

  • Vespa: Ideal for low-latency computation over large datasets, supporting structured, textual, and vector data.

  • LanceDB: Built with persistent storage for simplified retrieval and management of embeddings.

  • Deep Lake: AI Database optimized for deep learning, supporting data and vectors for LLM applications.

Embedding and Similarity Search Libraries/Engines:

  • Marqo: End-to-end engine for text and images, with unified API.

  • Faiss: Focuses on similarity search and clustering of dense vectors, effective for large datasets.

  • Vald: Distributed ANN dense vector search engine, highly scalable.

  • ScaNN (Scalable Nearest Neighbors): Efficient for large-scale vector similarity search.

  • Pgvector: Vector similarity search tool for Postgres, integrates with the Postgres ecosystem.

General-Purpose Search and Database Platforms with Vector Capabilities:

  • Elasticsearch: Distributed, RESTful search engine optimized for speed, relevance, and production-scale workloads.

  • OpenSearch: Combines classical search, analytics, and vector search.

  • Apache Cassandra: NoSQL database supporting vector search, known for scalability and fault tolerance.

  • ClickHouse: Column-oriented database utilizing vectors for efficient data processing.

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