Besides Milvus, there are some other vector databases to choose from. Here are some common vector databases:
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Faiss: Faiss is Facebook's open source vector similarity search library, which provides efficient vector indexing and similarity search algorithms. It supports multiple index structures and search algorithms, and is suitable for storage and retrieval of large-scale vector data.
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Annoy: Annoy is Spotify's open source approximate nearest neighbor search library, designed for fast vector similarity searches. It uses a tree-based indexing method that can efficiently handle large-scale vector data.
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Hnswlib: Hnswlib is a vector index library based on a hierarchical approximate nearest neighbor search algorithm, which provides efficient vector similarity search and approximate nearest neighbor query capabilities.
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NMSLIB: NMSLIB (Non-Metric Space Library) is a non-metric space index library for high-dimensional vector data that supports multiple index structures and search algorithms.
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TensorFlow Similarity: TensorFlow Similarity is a library for high-dimensional vector similarity search developed by the TensorFlow team, based on the TensorFlow framework, and provides some high-performance vector index structures and search algorithms.
Regarding whether there is a free vector database, some open source vector databases such as Milvus, Faiss, Annoy, Hnswlib, and NMSLIB are provided free of charge, and their codes and documents can be obtained in the open source community. These databases are free to use and customizable, suitable for individuals and teams for learning, research and development. However, commercial vector databases may provide more advanced features and technical support, and may require payment. Specifically, you can choose the appropriate vector database according to your needs, and check its license and terms of use to see if you need to pay.