Sub-millisecond read and write latencies remain stable under high-volume, mixed workloads, giving real-time inference pipelines the predictable performance they require

Scales from terabytes to petabytes across tiered storage options without proportional growth in server footprint or operational costs

Five-nines availability with self-healing, self-managing clusters that rebalance dynamically and accommodate most changes with no downtime

Native connectors for Spark, Kafka, Pulsar, and Presto/Trino enable fast feature ingestion, engineering, and sharing across your entire ML toolchain

Deployed by Sony Interactive Entertainment, Quantcast, and others to power mission-critical ML applications including personalization and fraud detection