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Vearch is a distributed vector search engine designed for AI-native applications, enabling scalable and efficient similarity search across large datasets.
Havenask is an open-source distributed search engine with support for vector search, designed for large-scale AI and search applications.
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.
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.
Marqo is an open-source neural search engine that leverages vector representations to enable semantic search over textual data. It abstracts vector database complexity and provides a high-level interface for building advanced search applications.
A library by Google Research for efficient vector similarity search, suitable for large-scale nearest neighbor applications in AI.
Vearch is a cloud-native distributed vector database designed for efficient similarity search of embedding vectors in AI applications.
Vearch is open-source and free to use under the Apache-2.0 license. No paid plans are mentioned.