Your company is planning to expand its AI capabilities significantly over the next five years.

Future-proofing AI storage requires balancing the massive throughput needs of training with the vast capacity needs of data lakes. Ahybrid cloud model(Option C) is the most adaptable strategy because it allows organizations to maintain high-performance, low-latency "hot" data on-premises (near the DGX clusters) while leveraging the elastic, near-infinite scalability of cloud storage for "cold" archival data or sudden bursts in compute demand. This model supports data tiering, where active datasets live on NVMe-based local parallel file systems to saturate GPU memory, and older datasets migrate to lower-cost cloud object storage. While an all-flash array (Option A) is fast, it can become cost-prohibitive for multi-petabyte AI data lakes without a cloud-integrated tiering strategy. The hybrid approach provides the architectural flexibility to adopt new technologies over a five-year horizon without being locked into rigid on-premises hardware upgrade cycles.