Everpure Redesigns AI Storage: Fewer Silos, More Governance, Data Ready for Agents

Corporate storage was born to store data, protect it, and serve it to applications. With artificial intelligence, this is no longer enough.

Models and agents need to understand what data exists, where it is located, what it means, how it is connected, and what security constraints must be adhered to. This is where Everpure (formerly known as Pure Storage) aims to raise the bar: no longer just arrays, capacity, and performance, but a data platform capable of making information usable by AI without multiplying copies, pipelines, and governance tools.

The innovations announced at Accelerate, Everpure's annual event, should be read in this direction. The company emphasizes the centrality of data and expands its Enterprise Data Cloud with three components: Universal Data Intelligence, Unified Data Plane, and Intelligent Control Plane. Three different but connected levels. The first is designed to discover and understand data. The second is to store and distribute it on a common base, from the data center to the cloud. The third automates the management of the entire infrastructure.

The most interesting move concerns Data Intelligence, a technology that comes from the acquisition of 1touch.io. Everpure is not just acquiring a product, but expertise in data governance, automatic classification, and semantic analysis. These skills are increasingly important because many AI initiatives do not stall due to a lack of GPUs or models, but due to a more banal and difficult-to-solve problem: business data is scattered, poorly described, duplicated, and often lacks reliable context.

Data Intelligence: Understanding Data Before Using It

Data Intelligence operates without agents installed on the client's systems and creates a map of relationships between business data. It does not simply indicate where files, tables, or databases are located. It classifies them, interprets their business significance, identifies sensitive data, reconstructs relationships and dependencies, and generates a semantic graph usable by applications, analytics tools, and AI agents.

In a traditional architecture, data remains tied to the application that generated it: CRM, ERP, financial systems, document repositories, SaaS environments, mainframes, public clouds, third-party storage. Each system has its own view, rules, and language. AI, on the other hand, works better when it can access not only the information but also the context in which it is generated and used.

Data Intelligence is born precisely for this reason. It discovers structured and unstructured data, catalogues it, links it to business definitions, and associates security and governance policies directly at the informational level. This way, an agent receives not just a block of text or a table, but richer information: what that data represents, where it comes from, which other data it is connected to, who can use it, and under what conditions.

A benefit that also extends to costs: if the model receives more pertinent and better-contextualized information, the amount of data to send to the prompt or context window decreases. Less noise, fewer tokens, less unnecessary processing. For companies starting to experiment with agents on real processes, this can make the difference between an interesting pilot project and a sustainable production application.

Data Stream Brings Unstructured Data to AI

The second innovation is Data Stream, developed with NVIDIA. Here, Everpure tackles a very concrete problem: converting unstructured data into usable material for search, analysis, and natural language query systems. Many AI projects require complex ingestion, cleaning, vectorization, and data transfer pipelines to the computing environment. It is a costly, slow, and often inefficient process. If the pipeline is not well-designed, the GPUs wait idly or are used for tasks that should not burden the main computing engine.

With Data Stream, Everpure shifts part of this workload to the data infrastructure. Managing and transferring the information flow is delegated to storage, allowing GPUs to focus on the tasks for which they were purchased: training, inference, and accelerating AI workloads. The stated goal is to dramatically reduce data preparation times while maintaining access controls at the flow level and a more flexible separation between storage and compute.

Collaboration with NVIDIA fits into a broader strategy. Everpure refers to the NVIDIA AI Data Platform and is also working on next-generation solutions based on NVIDIA STX, Vera, and BlueField-4 STX storage processors. The direction is to bring acceleration, security, and intelligent data services closer to where the data resides.

Unified Data Plane: A Common Base for Cold Data, Critical Workloads, and AI

Everpure also expands the Unified Data Plane, the common base designed to manage very different workloads without creating new infrastructural silos. The logic is one of consolidation. Customers no longer want separate platforms for databases, files, analytics, virtualization, backup, ransomware recovery, cold data, and AI workloads. They want less fragmentation, fewer tools to manage, fewer contracts, and less operational complexity.

In this scenario, the FlashArray//XL 190 with Purity Turbo is introduced, aimed at mission-critical environments that require higher performance margins. The objective is to consolidate demanding workloads onto a single architecture, increasing the capacity to absorb spikes and meet stricter SLAs.

On the opposite front, Everpure continues to work on managing large capacities and less hot data, an increasingly important area with AI. Not all data needs to be served at maximum speed, but more data must remain accessible, governed, and ready for analysis. The distinction between archive and operational data becomes less sharp: even data that has been stagnant for some time can become useful for training, enriching, or contextualizing an AI system.

The same logic guides Everpure Cloud Azure Native VMs. The solution brings Purity into Azure as native storage for virtual machines. The advantage is decoupling storage and compute, offering shared storage that supports multiple VMs and allows for features like data reduction. For companies, this means being able to move or extend workloads in Azure with greater flexibility, without always accepting the rigid relationship between capacity, performance, and single virtual instance.

Evergreen//One Overdrive: Extra Performance Without Changing Plans

Another innovation concerns Evergreen//One Overdrive, an extension of Everpure's service offering. The idea is to give customers the ability to absorb temporary spikes without permanently switching to a higher plan.

Overdrive allows for a temporary increase in on-premise performance of up to 25% above the baseline. It can serve during periods of unusual traffic, promotional campaigns, accounting closures, Black Friday, seasonal peaks, or new analytics processing. Once the spike is over, the environment returns to the usual configuration.

This is a function that meets a concrete need, especially in markets like Southern Europe, where the service model is also appreciated for predictable costs. The customer knows the price over the duration of the contract, can count on faster delivery times, and does not have to over-provision the infrastructure for events that last only a few days.

The company aims to bring some of the cloud's elasticity into the on-premise environment. It is not a replacement for the cloud, but a response to a widespread reality: many companies want flexibility but do not want to move everything outside the data center.

The Control Plane Becomes Smarter

Above the data plane comes the Intelligent Control Plane, an evolution of the logic already seen with Fusion and Pure1. Here, Everpure works on fleet management: observing all systems, analyzing behavior and performance, suggesting interventions, and automating operations.

New features include workload rebalancing and mobility, compliance monitoring, cyber anomaly detection, and agent-based workflows based on MCP. In practice, the platform can analyze history and telemetry to suggest when to move a workload, when to scale up or down, when to correct an out-of-policy configuration, or when to investigate suspicious behavior.

The advantage lies in reducing manual work. In distributed environments with dozens or hundreds of systems, the problem is not just seeing an alert. It is understanding which workload is impacted, how severe the problem is, which policy is involved, and which action is advisable. Everpure aims to transform this management from reactive to predictive, with more centralized and automated control of the infrastructure.

From Storage to Data Asset

Everpure's innovations show how the storage market is changing. Capacity, latency, and reliability remain fundamental but are no longer sufficient to differentiate a platform. AI forces providers to up their game: they need to know where the data is, understand its meaning, protect, prepare, and transport it in the most efficient way to models and agents.

The multinational builds its response around a precise idea: data should not remain a prisoner of the application that generated it. It must become a shared, governed, and accessible resource, without creating yet another copy or yet another silo. It is an ambitious promise, as it tackles one of the toughest problems in corporate IT: restoring order to years of application and infrastructural layering.