If you're looking for a data storage system that can handle enormous AI workloads and scale linearly, WEKA is a great option. This data platform is built for cloud and on-premises environments, with high performance and sustainability for next-gen workloads like AI. It can handle 10 exabytes of data with linear scaling for compute and storage, and it offers 10-100x better performance than traditional data infrastructure.
Another option is DDN, which offers optimized data storage and management solutions for AI and HPC workloads. Its storage appliances are optimized for efficiency and cloud integration, with features like 10X more efficient AI infrastructure and 30x more IOPS. That makes it a good option for companies that want to speed up their AI and HPC workloads with big data sets.
For cloud-native workloads, MinIO is an interesting option. It's high-performance object storage for AI and machine learning, and it's highly scalable and Kubernetes-native. MinIO offers active-active, multi-site replication, industry-standard encryption and features for information lifecycle management, so it's good for large-scale AI/ML workloads and data lakes.
You could also look at SingleStore, a real-time data platform that can handle petabyte-scale data sets with millisecond query performance. It combines transactional and analytical data in a single engine and can ingest high-throughput streaming data from multiple sources. That makes it a good option for generative AI and real-time analytics.