The scenario specifies two key requirements: "rapid inference" and operation "across multiple sites." Rapid inference for real-time fraud detection demands minimal delay, making low-latency storage access paramount. Every millisecond saved in data retrieval contributes to a faster response. The "across multiple sites" requirement means that inference requests can originate from different geographic locations. To ensure consistently low latency for all sites, a geo-distributed storage system is necessary. This architecture places data replicas closer to the points of inference, minimizing network latency that would otherwise occur when fetching data from a single, centralized data center.

High-throughput for sequential writes: Inference workloads are dominated by small, random reads, not large, sequential writes, which are characteristic of model training.

Data immutability and versioning: These are data governance features. While potentially useful, they do not directly address the primary performance requirements of speed and multi-site access.

Cost-effective cold storage tiering: The use case is for rapid, active data retrieval. Cold storage is designed for inexpensive, long-term archival of infrequently accessed data and is too slow.

1. NVIDIA. (2021). NVIDIA AI Enterprise Technical Brief (TB-10849-001v1.1). This brief distinguishes between storage requirements for training (high throughput) and inference (low latency), stating inference workloads "benefit from low-latency access to data." (p. 12).

2. Werner, B., & Chung, E. (2019). Designing and Deploying AI Solutions. Stanford University, CS230 Courseware. Lecture notes on "AI in Practice" often emphasize that production inference systems, especially user-facing ones like fraud detection, must be optimized for P99 latency, requiring low-latency data access and geographically distributed endpoints to serve a global user base effectively.