Building AI Data Foundations: Object Storage Patterns for Scale, Access, and Longevity

AI systems are only as powerful as the data foundations that support them. As applications increasingly rely on vectors, tables, graphs, and long-lived datasets, the object storage developers build against must do more than simply hold data - it must actively support how AI applications coherently access, move, and reuse it over time.

We will examine the architectural patterns and storage interfaces shaping modern AI-ready object storage. We explore how object storage is designed to support emerging AI data types alongside traditional unstructured data, while remaining scalable, durable, and cost efficient at cloud scale. Central to this evolution is tighter integration between data lifecycle management and application workflows, giving developers a clean path to move from ingestion and training to inference, governance, and long term retention.

Developers will leave with practical, implementable approaches to building AI-ready storage systems that deliver scale, efficient access, and long-term data reuse.