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NucliaDB is a commercial vector database that enables semantic and vector search across unstructured data, supporting advanced AI and ML-powered applications.
Meilisearch offers vector search capabilities as part of its search engine, enabling hybrid and semantic search for AI applications.
Vectara is a commercial vector database and search platform that enables semantic and hybrid AI-powered search using vector embeddings.
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.
Denser Retriever is a vector-based retrieval system designed for efficient similarity search and information access in AI and ML workloads.
AstraDB (also known as Astra DB by DataStax) is a cloud-native vector database built on Apache Cassandra, supporting real-time AI applications with scalable vector search. It is designed for large-scale deployments and features a user-friendly Data API, robust vector capabilities, and automation for AI-powered applications.
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.
NucliaDB is an open-source AI-powered search database designed for Retrieval-Augmented Generation (RAG) and advanced semantic/vector search across unstructured data. It supports hybrid search capabilities and is tailored for modern AI and ML-powered applications.