VAST targets trillion-embedding scale

The core point is that VAST is trying to turn AI data storage into the system of record, not just a fast sidecar. A trillion embedding rows means a company wants one place to hold raw files, vector indexes, and SQL tables without copying data across separate warehouse and vector systems. Massive parallelism means thousands of queries or GPU workers hitting the same dataset at once, which favors VAST’s shared all flash architecture over warehouse products built first for business analytics workflows.

• VAST is built so the same cluster can expose data as files, S3 objects, and database tables. That matters for AI teams because training data, embeddings, and analytics usually live in different systems, with cost and delay each time data is copied between them.
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• Pinecone shows the more common pattern in the market. Developers generate embeddings, store them in a dedicated vector database, and query by similarity. That works well for retrieval, but it still leaves the wider warehouse and data lake stack in place. VAST is aiming at the larger consolidation move.
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• Databricks and Snowflake both support vector features today, but those features sit inside broader analytics platforms. VAST’s argument is that AI workloads stress the lower layer first, storage bandwidth, concurrency, and cluster wide data access, so the winner can move up into database and warehouse functions over time.
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This is heading toward a fight over who owns the physical home for AI data. If VAST keeps adding warehouse and query features on top of its storage base, it can pull spending away from separate vector databases, lakehouse tools, and legacy storage arrays, especially in very large enterprise and GPU cloud deployments.