Hitachi Vantara Adds In-Place Lakehouse Queries to VSP One Object

Hitachi Vantara announced on November 11, 2026 that VSP One Object can query supported open-format data directly on the storage platform, with a four-node starting configuration intended to simplify deployment. The update also adds governance features to Hitachi iQ Studio for enterprise AI applications and agents.

The announcement connects object storage, lakehouse analytics and the software used to build and govern AI applications and agents. VSP One provides the data foundation for Hitachi iQ, which combines validated AI infrastructure and software to connect applications and agents with enterprise data, models, tools and workflows.

A Smaller Starting Point for AI Storage

The four-node configuration gives organizations a smaller entry point for AI-ready object storage, while simplified deployment is intended to make the initial setup easier and allow capacity to scale with demand. Hitachi Vantara reports that continued small-object performance improvements support up to 5.5x faster data access for AI, analytics and S3 workloads.

Expanded multisite replication and regional capabilities are designed to keep data available, protected and accessible during disruptions. For AI and analytics workloads spread across sites, those features address a different operational requirement from query speed: continued access to the underlying data when something goes wrong.

Lakehouse Queries Where Data Resides

VSP One Object supports direct queries against Iceberg, Parquet, JSON and CSV data. Hitachi Vantara reports up to 5x faster analytical query performance with 80% fewer CPUs from the new capabilities. Running queries on the object-storage platform reduces the need to copy supported data into separate analytics systems, which can reduce duplicate datasets, data pipelines and the infrastructure needed to maintain them.

An accompanying Hitachi Vantara explanation of in-place analytics identifies two governance problems with exported data. Copies can drift, so an explanation generated from an older or differently filtered extract may not match the data behind a prediction; analyzing the same data in place keeps the explanation and prediction aligned. It also preserves a clearer lineage trail through the tables, columns and transformations that fed a model.

The company presents this approach as relevant to energy and medical workloads. Its examples include joining sensor time series with asset, outage and billing data for grid analysis, and querying imaging metadata and structured reports without duplicating sensitive records into separate analytics environments.

Model and Agent Governance in Hitachi iQ Studio

Hitachi iQ Studio is the enterprise software for building, deploying, managing and governing AI applications and agents. Its new filesystem and NFS knowledge sources expand access to structured and unstructured enterprise data for retrieval-augmented generation (RAG), which supplies enterprise context for AI responses and agents, without requiring organizations to move or restructure all of that data first.

A new Model Gateway governs access to local and external models. The release also adds governed Model Context Protocol connectivity for linking agents with tools, ServiceNow support, and role-based access, observability and lifecycle management across enterprise AI applications and agents. This extends governance beyond the storage layer to model access, tools and agent operations.

Chief product officer Octavian Tanase connected that scope to the company’s production-AI message: “AI strategy is now inseparable from data strategy.”

For enterprises moving AI into production, the consequence is a closer link between infrastructure and governance decisions. Reducing data movement can simplify analytics and preserve lineage, but the usefulness of that approach depends on whether access controls, observability and replication remain consistent as deployments grow from the initial configuration.