Oracle 23ai introduces native support for vector data types, enabling AI and similarity search workloads directly within the database. This session will provide essential guidance on how to properly size your database and storage infrastructure in light of these advancements.

We’ll explore the storage implications of vector data, including typical size footprints per vector dimension, how vector data differs from traditional scalar types in terms of compression and I/O, and the significant storage overhead introduced by vector indexes. The session also includes insights into sizing considerations for mixed workloads, including traditional OLTP and AI workloads coexisting in a single database instance.

Attendees will leave with a practical framework for estimating growth due to vector data and indexes, understanding the impact on modern storage platforms, performance trade-offs, and planning capacity to ensure scalable, cost-effective deployments of Oracle 23ai.