Data architects, data engineers, and platform leads evaluating Iceberg lakehouse deployments on-premises or in hybrid environments, particularly those looking to reduce catalog infrastructure complexity or unify structured and unstructured data for AI workloads.
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AIStor Tables stores all Iceberg catalog metadata as objects inside AIStor with no external database required, and supports views alongside full Iceberg REST catalog compliance.
At approximately five petabytes of hot data, on-premises AIStor storage cost falls clearly below public cloud storage cost even before compute is factored in, and the gap widens significantly once compute costs are included.
Storing S3 object paths (or vectors pointing to object paths) inside Iceberg table columns alongside structured fields enables a single SQL query to return both structured data and direct references to unstructured files, giving AI agents and human analysts unified discovery across an entire enterprise data estate.
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