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Apache Arrow is a cross-language development platform for in-memory data that is commonly used to facilitate efficient integration between vector databases and machine learning frameworks. It provides a standardized format for data exchange that is useful for storing and querying high-dimensional vectors in AI applications.

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.

ChromaDB (also known as Chroma or chroma-core) is an open-source vector database focused on LLM applications, emphasizing simplicity and in-memory HNSW-based dense vector search. It is suited for prototyping, metadata filtering, and offers a user-friendly interface for building and testing vector search applications, though it currently lacks hybrid and distributed features.

Redis, while primarily an in-memory data store, offers vector search capabilities through its RediSearch and RedisAI modules, enabling vector similarity searches and deep learning model management for existing Redis users. With the RediSearch module, Redis extends its functionality to support native vector search, indexing, and hybrid queries, making it suitable for real-time AI and semantic search applications.