VAST provides integrated catalog and SQL

This is the clearest line between selling fast storage and selling the system where AI data actually gets organized and used. VAST is not just serving bytes to GPUs. It also keeps a live catalog of what those files are, exposes that metadata through SQL, and lets teams search, filter, and process data in place. That means fewer separate tools for cataloging, analytics, and data movement than a WEKA deployment usually requires.

• VAST’s database layer is concrete, not conceptual. Its Catalog indexes metadata across files, objects, and directories, and that catalog is queryable through SQL, Spark, and Trino. In practice, a team can ask for a subset of training data based on tags, dates, or labels without first copying everything into a separate warehouse.
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• WEKA is positioned much more as a high performance data path. Its own materials emphasize software defined file and object storage, GPU utilization, KV cache offload, and multicloud deployment. When customers want vector databases, metadata stores, or SQL analytics around that storage, WEKA commonly sits beside those systems rather than replacing them.
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• That changes how money and adoption can scale. VAST can land as storage, then expand into catalog, query, and data processing budgets on the same footprint. The company frames this as moving from a storage market into a broader data warehousing, analytics, and serverless infrastructure market, which is why the platform pitch matters strategically.
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Going forward, AI infrastructure vendors will keep converging around a full data stack. If WEKA remains the best pure performance layer, it will stay strong in GPU heavy deployments. If VAST keeps proving that storage, metadata, SQL, and processing belong together, it can win the larger control point in enterprise AI infrastructure.