Everpure says its new data management capabilities help enterprises govern data access and manage AI costs as they move from pilots to production.
Everpure has announced new platform capabilities for enterprise data management and AI. The updates build on its Data Primacy approach, introduced at Pure//Accelerate in June, which puts data at the centre of enterprise architecture. The company says the new capabilities address challenges in scaling AI, including fragmented data context, unpredictable inference costs and complex deployments.
“Enterprise AI is hitting a wall not because the models are lacking, but because data is not ready for real-time, autonomous agents. We are eliminating that friction. By making enterprise data continuously governed, automated, and instantly accessible, we’re giving organizations the foundation to move AI out of the lab and into production with the necessary confidence,” said Prakash Darji, General Manager, Data & Digital Experience at Everpure.
Enterprise Data Management and Security for AI
Everpure has added capabilities to help organizations identify, classify and manage data across its platform, public clouds, SaaS applications and third-party storage. The updates also allow AI agents and administrators to access current data information without requiring custom API integrations.
- MCP integration: Allows AI agents and security tools to query data catalogs using natural language and identify relevant information and its sensitivity classification.
- Simplified deployment: Enables deployment through the existing Pure1 console, without separate management servers.
- File intelligence: Shows who can access file shares and identifies stale data without reading file contents, helping organizations review access risks and identify storage that may no longer be needed.
AI Performance, Storage Efficiency and Cost Control
Everpure has also introduced capabilities designed to improve AI inference performance, increase usable storage capacity and manage AI costs.
- LLM inference with PureKVA: According to Everpure, FlashBlade can use its Key-Value Accelerator (PureKVA) to pre-stage context in GPU memory, delivering up to 20 times faster time to first token (TTFT). The company says this reduces response latency and improves token throughput without moving datasets from their existing storage location.
- DeepReduce compression: Continuously identifies additional data reduction opportunities across FlashBlade systems, including similarities within pre-compressed data. Everpure says this can increase usable capacity without affecting write performance or requiring scheduled compression jobs.
- Token optimization reference architecture: Provides a reference architecture based on open-weight models to help organizations retain greater control over their data and reduce reliance on external AI APIs. Everpure says this can lower overall API token usage.
Everpure says the updates address data management, security, performance and cost challenges associated with running AI workloads in production. The company also links data classification and access visibility to cyber resilience, helping organizations determine which data needs protection and what to prioritize during recovery.
Disclaimer: This article is based on Everpure’s official product announcement and has been edited for length and readability. Product capabilities and performance claims are based on information provided by the company.
