AI Agents Are Outgrowing the Converged Data Platform

AI agents create data workloads that require governed, low-latency access to information across operational databases, lakes, warehouses, cloud environments, and applications, rather than data centralized in a single converged platform. Starburst argues that copying data into such platforms can add security, governance, cost, and flexibility challenges, while federated query architectures such as Trino allow organizations to query data where it resides and independently select suitable compute. Building on Presto and Trino’s principles of federation and storage-compute separation, Starburst has introduced Torin, a GPU-native, Trino-based SQL engine for Starburst Enterprise that uses NVIDIA cuDF and can execute supported operations on NVIDIA GPUs while retaining CPU execution for others within the same query. According to Starburst’s internal benchmark-derived testing, Torin improved performance for certain analytical scan-and-join workloads, though results vary and agent performance also depends on model inference and deployment conditions. The company maintains that successful AI data architectures will combine consistent governance, distributed data access, and the ability to choose CPU or GPU compute without moving data or rewriting SQL.

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