The rapid growth of AI and ML applications, particularly those involving large-scale data analysis, has increased the demand for vector databases that can efficiently store, index, and query vector embeddings. Therefore, vector databases like MyScale, Pinecone, and Qdrant continue to be developed and expanded to meet these requirements.

At the same time, traditional databases continue to improve their vector data storage and retrieval capabilities. For instance, the well-known relational database PostgreSQL and its pgvector extension provide similar functionality, albeit less effectively than a well-optimized vector database.

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