Top 5 Open Source Vector Search Engines: A Comprehensive Comparison Guide for 2025

Vector search has become a crucial component in many AI applications, enabling efficient similarity-based queries that traditional databases can't handle. The challenge lies in finding the nearest neighbors" to a vector in a massive dataset, which is where vector search engines come in. The open-source community has developed multiple high-quality options, but choosing the right one for a specific use case, technical expertise, and team requirements can be challenging. Vector search involves converting data into vectors that capture semantic meaning, and measuring the distance between vectors to find similar items. Traditional search engines use inverted indices and exact keyword matching, whereas vector search understands semantic similarity rather than requiring exact term matches. Key performance metrics for vector search engines include query speed, recall, index build time, memory usage, and scalability. Vector search has various applications, including product recommendation systems, AI agents, semantic search, and image and multimedia similarity search. Milvus is a popular open-source vector database that uses a cloud-native architecture, supports multiple index types, and offers hybrid search capabilities. Milvus has a straightforward migration path from other vector databases, and its managed service, Zilliz Cloud, eliminates operational overhead for customers. Faiss is another popular vector search library that was developed by Facebook AI Research and is optimized for both CPU and GPU execution.