Elasticsearch Was Great, But Vector Databases Are the Future

The article discusses how semantic search is becoming more popular as AI technology advances, with embedding models and vector databases playing a central role in this shift. Semantic search surpasses keyword matching by representing data as vector embeddings, providing a more nuanced understanding of search intent and transforming applications ranging from retrieval-augmented generation (RAG) to multimodal search. Many organizations are adopting a hybrid search approach, combining the strengths of both semantic and full-text search methods to balance flexible relevance with predictable exact keyword matching. Vector databases like Milvus are poised to surpass Elasticsearch as the unified solution for hybrid search due to their superior performance, scalability, and efficiency in integrating dense vector search with optimized sparse vector techniques.