Faiss or Facebook AI Similarity Search is an open-source library (from Facebook AI). It accomplishes fast similarity search, thus making it suitable for image retrieval, recommendation systems, and anomaly detection. It supports various vector types, indexing and GPU acceleration for large datasets.

Weaviate is also an open-source vector database. It makes you store and query the data objects (along with their vector embeddings). It offers GraphQL, supports semantic search and integrates various ML frameworks.

Pinecone is designed for ML apps. It offers fast and scalable vector search. It leverages Faiss under the hood. It provides use-friendly API for integration with different tools and frameworks.

Milvus is too an open-source similarity search engine and excels in search across different cloud environments. It supports different similarity metrics, indexing and filtering capabilities for large datasets.

Qdrant is cloud-based vector database for vector similarity search. It is user-friendly, does real-time search. It integrates various cloud platforms.

Elastisearch is used for full-text search. It also-supports storing and searching vector data. Its version 7.0 has been introduced. It is for indexing vectors. It enables efficient k-nearest neighbours search.

Apache Spark is distributed computing framework for large scale vector processing and similarity search.

The MLib includes algos such as kMeans and cosine similarity. It enables vector analysis on big data.

Ne04j is graph database which can be extended to vector similarity search capabilities using plugins. ArangoDB is NOSQL document database supporting storage and querying vectors. DolphinDB is a time-series database. It is suitable for tasks involving high-dimensional vectors in generative models.