Organizations building on-premises or hybrid data lakehouses face a critical storage decision, and the wrong choice compounds over time. This technical evaluation assesses storage platforms across three architectural groups: legacy scale-up (NAS/SAN platforms like Dell PowerScale and NetApp AFF that added S3 through gateway layers, bounded by controller capacity and protocol translation overhead); scale-out with hard limits (platforms like Dell PowerMax, Pure Storage FlashBlade, and NetApp StorageGRID that scale modularly but hit vendor-published maximums requiring disruptive migration); and modern distributed multi-protocol platforms (VAST Data, Ceph, Cloudera Ozone) that avoid scaling ceilings but introduce protocol contention and operational complexity. The guide explains four key organizational signals that indicate storage platform choice will significantly impact lakehouse success: data growth measured in multiples, multiple concurrent workload types, GPU efficiency requirements, and past storage-related project friction. It then details MinIO AIStor's S3-native advantage — native protocol without translation layers, linear throughput scaling, small-file performance optimized for AI, distributed metadata, and AIStor Tables that eliminate the need for a separate Iceberg REST catalog and metadata transactional database, collapsing the traditional 4-layer stack to 2 layers.
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Legacy scale-up and scale-out storage platforms impose architectural constraints — S3 gateway overhead, controller-bound performance, and vendor-published scaling ceilings — that become acute and costly as lakehouse and AI workload demands grow.
Multi-protocol platforms avoid hard scaling limits but introduce protocol contention that degrades S3 performance unpredictably, requiring operational complexity that erodes the consolidation value proposition.
AIStor Tables collapses the traditional 4-layer Iceberg stack (query engine, REST catalog, PostgreSQL metastore, object storage) to 2 layers by embedding the Iceberg REST catalog API directly into the storage platform.
Technical architects, data platform engineers, and infrastructure leads evaluating storage platforms for on-premises or hybrid data lakehouse deployments where GPU efficiency, AI workload support, and scalability without architectural ceilings are requirements.
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