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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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qdrant
Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
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Weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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Milvus (16.6k ⭐) → An open-source vector database that can manage trillions of vector datasets and supports multiple vector search indexes and built-in filtering.
Vald (1.2k ⭐) → A highly scalable distributed fast approximate nearest neighbor dense vector search engine. Vald is designed and implemented based on the Cloud-Native architecture. It uses the fastest ANN Algorithm NGT to search neighbors. Vald has automatic vector indexing and index backup, and horizontal scaling which made for searching from billions of feature vector data.
Elasticsearch (63.3k ⭐) → A distributed search and analytics engine that supports various types of data. One of the data types that Elasticsearch supports is vector fields, which store dense vectors of numeric values. In version 7.10, Elasticsearch added support for indexing vectors into a specialized data structure to support fast kNN retrieval through the kNN search API. In version 8.0, Elasticsearch added support for native natural language processing (NLP) with vector fields.
Qdrant (5.8k ⭐) → A vector similarity search engine and vector database. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural-network or semantic-based matching, faceted search, and other applications.
Faiss (20.7k ⭐, Meta) → A library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.
Weaviate (4.8k ⭐) → An open-source vector database that allows you to store data objects and vector embeddings from your favorite ML-models, and scale seamlessly into billions of data objects.
Vespa(4.3k ⭐) → A fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query. Integrated machine-learned model inference allows you to apply AI to make sense of your data in real time.
ScaNN (Scalable Nearest Neighbors, Google Research) → A library for efficient vector similarity search, which finds the k nearest vectors to a query vector, as measured by a similarity metric. Vector similarity search is useful for applications such as image search, natural language processing, recommender systems, and anomaly detection.
pgvector (2.3k ⭐) → An open-source extension for PostgreSQL that allows you to store and query vector embeddings within your database. It is built on top of the Faiss library, which is a popular library for efficient similarity search of dense vectors. pgvector is easy to use and can be installed with a single command.