Everpure (PSTG) Pure//Accelerate 2024 summary

Strategic vision and platform evolution

• Emphasized a shift from hardware-centric to software-defined storage, enabling unified cloud data management and supporting block, file, and object storage under a single operating environment with consistent APIs.

• Fusion was highlighted as a major innovation, virtualizing storage and enabling effortless, policy-driven fleet management, automation, and first-of-its-kind storage automation.

• Backward compatibility for Fusion was announced, with staged rollouts starting in September and continuing into next year.

• The company’s Evergreen architecture ensures products never become obsolete, supporting continuous upgrades, lower total cost of ownership, and non-disruptive updates.

• The platform is extensible from AI to backup, commercial to hyperscaler, and supports hybrid cloud optimization and cyber resiliency.

Storage as a service and customer experience

• Storage as a service is positioned as a flexible, outcome-based model, moving risk and operational complexity from customers to the vendor.

• Evergreen//One provides SLAs for performance, capacity, efficiency, resiliency, and cyber recovery, with the vendor paying for power and rack space used.

• The service supports traditional and AI workloads, with a new AI-specific tier guaranteeing throughput for GPU utilization and cold storage rates for data.

• Customers benefit from continuous SaaS-like improvements, rapid deployment, global scalability, and regular feature updates.

• Evergreen//One is now available in 30 countries, with enhanced SLAs and add-ons for resilience and cyber recovery.

AI and enterprise modernization opportunities

• Identified three AI-driven opportunities: storage for AI training, inference, and enterprise data modernization.

• AI is accelerating infrastructure upgrades as enterprises seek to unify and leverage proprietary data for inference and RAG use cases.

• The platform delivers sustained, balanced performance and unified management for every phase of AI, from data ingest to inference.

• Customer examples include large GPU cloud wins, Fortune 100 retailers, and global investment firms modernizing for AI workloads.

• AI adoption in enterprises is still early, with increasing focus on use cases and KPIs.