Running a vector database on Kubernetes requires careful configuration to ensure optimal performance, scalability, and security. This involves leveraging StatefulSets for reliable deployment, configuring persistent storage for performance, and managing resource allocation effectively. Autoscaling, monitoring, and security measures are essential to maintain system reliability, while backups and disaster recovery plans safeguard against data loss. Fine-tuning database parameters is crucial to optimize query speed, memory usage, and indexing efficiency. By applying these best practices, organizations can ensure reliable and high-performance vector search applications in a Kubernetes environment.