With the rise of AI-powered applications, enterprises are increasingly leveraging Vector Databases to enable efficient similarity search, recommendation systems, and GenAI workloads. However, choosing and deploying a Vector DB at an enterprise scale requires careful consideration of performance, security, compliance, and integration with existing data infrastructure.
In this session, we will explore key factors in selecting the right Vector DB for enterprise needs, comparing managed and self-hosted options, evaluating trade-offs in scalability and performance, and ensuring governance and compliance. We will also discuss deployment strategies, including cloud-native, hybrid, and on-premises approaches, as well as best practices for monitoring, optimizing, and securing large-scale vector data.
Whether you are evaluating Vector DBs for the first time or looking to optimize existing deployments, this talk will provide actionable insights to help enterprises unlock the full potential of vector search at scale