Enterprise AI platform architects, infrastructure leads, and technology executives building or scaling AI infrastructure that must support agentic workloads, analytics, training pipelines, and lakehouse architectures on a single governed foundation across core, edge, and cloud environments.
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Enterprise AI programs that store memory, tables, and objects across separate systems spend more engineering time moving data than building on it. AIStor addresses this with one namespace, one identity model, and one binary spanning agents, analytics engines, training pipelines, and lakehouse architectures from edge to exabyte scale.
Agentic AI requires persistent organizational memory that survives session boundaries. AIStor Memory gives agents a governed, auditable knowledge foundation where decisions, context, and reasoning accumulate across runs and compound across agent fleets rather than resetting with each session.
AIStor delivers 23.5 TiB/s demonstrated throughput, millisecond inference latency, and 40% lower TCO versus proprietary AI storage, on a software-defined architecture that runs on industry-standard hardware with a capacity-based subscription and no per-operation or egress fees.
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