Everpure is expanding its enterprise storage platform into AI data infrastructure with new capabilities for governed data discovery, Model Context Protocol access, accelerated inference and automated storage efficiency, targeting the gap between AI models and production enterprise data.
Enterprise AI infrastructure is increasingly becoming a data-management problem. As organizations move from pilots to autonomous agents, models need reliable access to enterprise information, while IT teams need to control what those systems can see, how much inference costs and where workloads execute.
Everpure, formerly known as Pure Storage, is addressing that layer with a set of new platform capabilities designed to make enterprise data more accessible to AI systems without turning every deployment into a custom integration project.
The company announced the updates on September 30 as part of its broader Data Primacy strategy, introduced at Pure//Accelerate in June. The concept puts enterprise data rather than applications at the center of IT architecture, with governance and context attached closer to the data itself. Everpure’s earlier Data Intelligence launch established capabilities for discovering, classifying and contextualizing information across its own platform, public clouds, SaaS applications and third-party storage.
One of the most notable additions is native Model Context Protocol (MCP) integration. Everpure says AI agents and security tools can use natural-language queries to access live data catalogs and determine what information exists and how sensitive it is.
MCP, originally introduced by Anthropic as an open standard for connecting AI applications with external data sources and tools, is becoming an important integration layer for agentic AI. Instead of building a separate connection for every application and data source, MCP provides a standardized interface between AI systems and enterprise information.
For enterprise AI, that distinction matters. An agent can be highly capable at reasoning but still produce unreliable results if it cannot identify the right source data or determine whether that information is appropriate to use. Everpure’s approach is to expose data context and sensitivity information alongside the underlying information.
The company is also adding a privacy-oriented file intelligence capability that identifies who can access file shares and how stale those files are without reading their contents. That can help organizations identify excessive permissions and obsolete data before making repositories available to AI agents.
Deployment is another target. Everpure says the new capabilities can be configured through its existing Pure1 management console rather than requiring separate management servers or extensive professional-services work.
The company is simultaneously pushing compute performance closer to the storage layer. Its PureKVA Key-Value Accelerator, running on FlashBlade, is designed to pre-stage context into GPU memory and deliver up to 20 times faster Time to First Token, according to Everpure. The architecture is intended to support multi-tenant inference without relocating datasets from their system of record.
That is significant because inference increasingly depends on moving context efficiently between storage and accelerators. As AI applications generate more retrieval and inference traffic, repeatedly copying datasets can introduce latency, operational complexity and additional infrastructure costs.
Gartner estimates worldwide AI spending will reach $2.7 trillion in 2026, up 49.5% from 2025, with AI infrastructure representing the largest component of that spending. The analyst firm also forecasts AI-optimized infrastructure-as-a-service spending to reach $42 billion in 2026.
Storage therefore sits inside a much larger infrastructure investment cycle. The challenge is shifting from simply providing enough capacity to ensuring that data can support high-frequency inference and agentic workloads without creating an uncontrolled cost or governance problem.
Everpure is addressing the capacity side with DeepReduce, which continuously searches for similarities across storage blocks, including data that traditional deduplication may not identify. The company says the technology is designed to expand usable capacity automatically without affecting write performance or requiring scheduled optimization. Its earlier technical material describes DeepReduce as a similarity-based approach intended for AI, analytics and backup workloads.
The final component is an intelligent token optimization reference architecture based on open-weight models. The objective is to give enterprises greater control over where their data is processed while reducing dependence on external model APIs and making inference costs more predictable.
Taken together, the additions show Everpure moving beyond conventional storage management toward a broader AI data platform strategy. Data discovery provides visibility, classification supplies governance context, MCP provides an agent access layer, PureKVA connects storage with inference performance, and DeepReduce targets the economics of growing datasets.
That architecture reflects a broader change in enterprise AI. Models are becoming easier to access, but production systems still have to solve the harder operational questions: Which data can an agent use? Where does that data live? How quickly can it reach an accelerator? What does each inference cost? And how can administrators maintain control as workloads multiply?
Everpure’s answer is to make those capabilities part of the data infrastructure itself rather than leaving them to a collection of separate AI tools.
The new capabilities are scheduled to become available in October. Their significance will ultimately depend on how well they perform across production workloads, but the strategy is clear: enterprise storage is increasingly being redesigned as an active layer for AI context, governance, inference and cost management rather than simply a place to keep data.
Market Landscape
AI infrastructure spending is expanding rapidly, but enterprise AI deployment increasingly depends on the data layer. Gartner forecasts $2.7 trillion in worldwide AI spending for 2026, representing 49.5% year-over-year growth, while its research identifies AI infrastructure as the largest spending segment.
At the storage level, IDC reported that worldwide external OEM enterprise storage spending reached $9.9 billion in Q1 2026, up 22.9% year over year, with AI-driven storage demand contributing to the expansion.
The competitive landscape includes storage providers, hyperscalers and cloud platforms increasingly building AI-aware data services. The technology focus is moving toward data discovery, governance, retrieval performance, inference optimization, resilience and cost efficiency.
Top Insights
- Everpure is repositioning enterprise storage as an active AI infrastructure layer spanning data discovery, governance, inference performance and storage efficiency.
- Native MCP integration gives AI agents a standardized mechanism for accessing enterprise data context instead of relying entirely on custom integrations.
- PureKVA targets inference latency by moving context closer to GPUs while keeping enterprise datasets in their existing systems of record.
- DeepReduce addresses storage economics by identifying similarities traditional deduplication can miss, including patterns within modern AI and unstructured workloads.
- The strategy reflects a broader shift toward AI-ready data architectures as enterprises deploy agents that require governed, real-time access to business information.
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