Vector search engines have become increasingly important in AI applications, and there are several open-source options available. The most popular ones include Milvus, Faiss, Annoy, Weaviate, and Qdrant. Each has its strengths and limitations, and choosing the right one requires careful consideration of specific needs and constraints, such as scale requirements, query patterns, update frequency, integration complexity, and future-proofing. Some engines excel at pure vector search, while others offer additional features like filtering, knowledge graph integration, or read-optimized workloads. It's essential to benchmark with real-world workloads and evaluate the performance of each engine against specific use cases before making a decision. Ultimately, the best choice depends on the project's requirements, team expertise, and operational overhead.