Hitachi Vantara pushes AI analytics into storage with VSP One update

Hitachi Vantara has added AI analytics tools to its VSP One data platform, including native queries on object storage that the company said deliver up to five times faster analytical query performance with 80% fewer CPUs.

The update, announced on 12 November 2026, centres on enhancements to VSP One Object and the Hitachi iQ portfolio. It targets object storage, data lakehouse analytics and governed access to enterprise data for AI use. The release aims at companies moving AI projects beyond pilots into broader operational deployment.

The launch arrives against a documented gap between AI adoption and AI at scale. Hitachi Vantara cited McKinsey's 2026 State of AI survey, which found that nearly nine in 10 respondents said their organisations regularly use AI in at least one business function, while only 44% said AI was scaling across the enterprise. IDC research, also cited by the company, found that 89% of organisations reported some level of data-quality problem.

Those numbers frame the commercial stakes. Storage vendors now compete less on raw capacity and more on whether they can prepare, govern and analyse data at the point where it is stored.

What changes in VSP One Object?

VSP One Object now starts with a four-node configuration, down from larger previous entry points. Hitachi Vantara designed the change to lower the entry barrier for organisations building storage for AI and analytics workloads.

The release also includes simpler deployment and performance improvements for small-object workloads. Hitachi Vantara said VSP One Object can provide up to 5.5 times faster access to data for AI, analytics and S3 workloads. Expanded multisite replication and regional features aim to improve resilience and availability.

The three changes combine into a single proposition: a smaller starting footprint, faster access and stronger replication.

Why run analytics where the data sits?

The key technical shift in the release is the addition of analytics functions directly on VSP One Object. The platform can query supported open-format data — including Iceberg, Parquet, JSON and CSV — where it resides, rather than requiring it to be moved into a separate analytics environment.

Hitachi Vantara said this approach supports up to five times faster analytical query performance with 80% fewer CPUs. Reducing data movement also lets organisations limit duplicate datasets, data pipelines and the infrastructure needed to manage them.

The design reflects a broader industry effort to cut the cost and complexity of preparing enterprise data for AI systems. Many organisations have struggled to connect large volumes of operational and unstructured data to AI models without creating new silos or governance issues. Querying data in place attacks that problem at the storage layer instead of adding another pipeline.

What does Hitachi iQ Studio add?

VSP One underpins Hitachi iQ, the company's AI infrastructure and software offering. In this release, Hitachi iQ Studio expands access to both structured and unstructured data for retrieval-augmented generation, or RAG, including filesystem and NFS knowledge sources.

The update lets organisations use more enterprise data in AI applications and agents without first moving or restructuring all of it. Other additions include:

• A Model Gateway for governed access to local and external models
• Governed Model Context Protocol connectivity
• ServiceNow support
• Role-based access controls
• Observability and lifecycle management for AI applications and agents

Together, these features aim to strengthen oversight of model use, data access and operational management in enterprise AI environments.

What did Hitachi Vantara say?

Octavian Tanase, Chief Product Officer at Hitachi Vantara, said the company sees data management and AI deployment as increasingly linked.

"AI strategy is now inseparable from data strategy. As AI becomes part of more business-critical workloads, the infrastructure underneath it has to make it easier to put enterprise data to work," said Tanase. "With VSP One, we're giving customers a practical way to build the data foundation for AI, bring analytics closer to their data and scale with confidence as demand grows. That creates a stronger path from experimentation to production without sacrificing the resilience and control enterprises depend on."

The statement positions VSP One as the substrate beneath Hitachi iQ rather than a standalone array line — a repositioning that mirrors moves across the storage industry.

Why does this release matter beyond one vendor?

The release shows storage suppliers repositioning infrastructure around AI requirements rather than traditional capacity and performance measures alone. Vendors increasingly combine data storage, analytics and governance tools into a unified stack for enterprises concerned about data locality, oversight and cost.

Hitachi Vantara is also pairing its infrastructure and software with wider industry expertise from parent Hitachi. It linked the approach to work under the Hitachi AI Factory banner, which focuses on moving AI systems from experimentation into production environments tied to business operations.

For customers, the significance of the release rests less on any single feature than on whether the platform can reduce the friction involved in preparing, governing and analysing data for AI workloads. The additions to object storage, native analytics and governed model access suggest Hitachi Vantara is addressing that bottleneck directly, at the point where enterprise data is stored and managed.

The test ahead is execution. With McKinsey reporting only 44% of organisations scaling AI enterprise-wide and IDC finding data-quality problems at 89% of organisations, vendors that shorten the path from stored data to working AI systems hold a measurable commercial advantage. Hitachi Vantara has now built its argument around that gap.