Valkey is an open-source in-memory key-value data store that supports vector search operations, making it useful for AI and machine learning vector database workloads. It is also a specialized open-source vector database designed for efficient management and retrieval of high-dimensional vector data, offering advanced APIs and optimized storage for AI workloads.

A distributed vector database designed for scalable and efficient vector similarity search. It is purpose-built for handling large-scale vector data and search workloads.

ClickHouse is an open-source column-oriented database that supports vectorized computation and now offers vector search features. Its architecture enables efficient real-time analytics and vector operations, making it a relevant choice for vector database use cases.

Cottontail DB is an open-source vector database for storing and searching high-dimensional data, with features geared towards research and production environments.

Deep Lake is a vector database designed as a data lake for AI, capable of storing and managing vector embeddings, text, images, and videos. It utilizes a tensor format for efficient querying and integration with AI algorithms, making it suitable for similarity search and machine learning workflows. It is open-source and tailored for handling unstructured and multimodal data, with seamless integration with frameworks like PyTorch and TensorFlow.

Elasticsearch is a distributed search engine supporting various data types, including vectors, and provides scalable vector search capabilities, making it a popular choice for modern AI-powered applications. It can be extended with the k-NN plugin to provide scalable vector search using HNSW and Lucene, enabling hybrid semantic and keyword search capabilities.