AI-Powered Vector Databases: Query Optimization Techniques and Real-Time Performance Engineering

This paper has presented a comprehensive investigation of query optimization strategies for AI-powered vector databases, demonstrating that systematic performance engineering can yield substantial latency reductions without sacrificing retrieval quality. Our experimental evaluation across Milvus, Qdrant, and Weaviate established that optimized parameter configurations reduce p50 query latency by 42-63% relative to default settings while maintaining recall above 0.93. The proposed four-stage optimization framework provides practitioners with a structured methodology for achieving production-grade vector retrieval performance. As AI applications continue to demand increasingly responsive semantic search capabilities, the optimization techniques and analytical frameworks presented in this paper will play a central role in enabling reliable, high-throughput vector database deployments at scale.

[1] M. Aumüller, E. Bernhardsson, and A. Faithfull, "ANN-Benchmarks: A benchmarking tool for approximate nearest neighbor algorithms," Information Systems, vol. 87, pp. 1–13, 2020. [2] Y. A. Malkov and D. A. Yashunin, "Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 4, pp. 824–836, 2020. [3] E. Aguerrebere et al., "Locally-adaptive quantization for streaming vector search," Proceedings of the ACM on Management of Data, vol. 1, no. 2, pp. 1–25, 2023. [4] C. Wang et al., "Milvus: A purpose-built vector data management system," Proceedings of ACM SIGMOD, pp. 2614–2627, 2021. [5] J. Johnson, M. Douze, and H. Jégou, "Billion-scale similarity search with GPUs," IEEE Transactions on Big Data, vol. 7, no. 3, pp. 535–547, 2019. [6] H. Jégou, M. Douze, and C. Schmid, "Product quantization for nearest neighbor search," IEEE TPAMI, vol. 33, no. 1, pp. 117–128, 2011. [7] J. Pan et al., "Survey of vector database management systems," The VLDB Journal, vol. 33, pp. 1591–1615, 2024. [8] N. Reimers and I. Gurevych, "Sentence-BERT: Sentence embeddings using siamese BERT-networks," Proceedings of EMNLP, pp. 3982–3992, 2019. [9] L. Boytsov and B. Naidan, "Engineering efficient and effective non-metric space library," Proceedings of SISAP, pp. 280–293, 2013. [10] T. Ge, K. He, Q. Ke, and J. Sun, "Optimized product quantization," IEEE TPAMI, vol. 36, no. 4, pp. 744–755, 2014.