In our opinion, Gartner’s Hype Cycle™ for Storage Technologies, 2025 (subscription required) offers critical insights into which storage innovations are poised to transform enterprise IT strategies. According to Gartner®: “this research examines the influence of emerging storage technologies on infrastructure outcomes, as well as their adoption rates and maturity levels. With these insights, I&O leaders can develop strategies to deliver innovative and future-proof storage platforms that meet business requirements.”
This year, DDN is named a Sample Vendor in two of the most forward-looking categories we believe:
- Storage Platforms for Generative AI
- Open-Source Storage Software
To us, these recognitions are more than accolades — they underscore DDN’s role as a foundational force in the future of enterprise AI and data platforms.
But First: Why AI Initiatives Are Stalling—And What’s Really Missing
For many teams racing to build production AI systems, the signs of strain are all too familiar:
- GPUs sitting idle while waiting for data
- Pipelines breaking across environments
- Massive unstructured datasets too slow to be usable
- Cold archives that GenAI needs – but can’t access in time
From the outside, these look like GPU scaling problems. In reality, they’re data layer failures.
Storage – specifically, smart, AI-optimized storage – isn’t just a performance layer. It’s the foundation that determines whether your AI systems can actually scale, learn, and respond in real time.
And in most organizations, it hasn’t caught up.
What the New AI Data Layer Needs to Do
The storage architectures of the past weren’t built for what AI demands now: Fast, frequent, intelligent access to vast volumes of data across distributed environments – at the exact moment it’s needed.
In our opinion, that’s why Gartner’s Hype Cycle for Storage Technologies, 2025 recognizes Storage Platforms for Generative AI and Open-Source Storage Platforms as innovation triggers. And it’s why we believe the AI Data Layer is now the most important missing piece of modern infrastructure.
To work at scale, it must:
- Understand how data moves across training, tuning, and inference
- Adapt automatically to changes in workload, priority, and location
- Serve up cold or archived data as if it were hot – especially for RAG and LLMs
- Span edge, core, and cloud with consistent performance and intelligence
This is not what traditional storage delivers. It’s what intelligent, self-optimizing platforms like DDN’s do.
Storage Platforms for GenAI: Real-Time AI, Real Business Outcomes
Generative AI (GenAI) workloads – including retrieval-augmented generation (RAG), fine-tuning, inference, and large model training – are reshaping enterprise compute needs. These applications demand not just powerful GPUs, but storage systems capable of feeding them massive volumes of data with minimal latency.
Gartner defines Storage Platforms for GenAI as solutions optimized for performance and data management to support AI workflows efficiently – without requiring separate infrastructure for GenAI vs. traditional enterprise workloads.
We believe, DDN’s recognition as a Sample Vendor in this space validates its position as the AI data platform of choice. With solutions like DDN Infinia and EXAScaler®, enterprises benefit from:
- Blistering performance at scale to saturate GPU pipelines
- Concurrent workload execution – training, inference, analytics, and ETL
- Native support for RAG and vector-aware architectures, reducing stale responses and data duplication
The business impact is real: customers using DDN have cut fraud detection times by 70%, tripled AI throughput, and slashed model training costs through higher GPU utilization and zero-copy data access. Simply put, DDN enables AI infrastructure that is faster, leaner, and smarter.
Open-Source Storage Software: Agility Meets Performance
Open-source storage software is gaining momentum as enterprises look for greater flexibility, cost efficiency, and integration into DevOps workflows. We conclude that Gartner highlights this category for its potential to drive innovation in multicloud, containerized, and performance-intensive environments.
DDN’s leadership in this space is built on decades of HPC and AI expertise, most notably through its active role in the Lustre community. As a top contributor and maintainer of Lustre – the world’s most performant parallel file system – DDN enables:
- Open, programmable access to extreme-scale storage
- Optimizations for AI and HPC workflows with proven performance in production
- Flexibility to run across bare metal, containers, hybrid, and cloud environments
Organizations building modern data platforms – from sovereign AI initiatives to national labs and private enterprises – rely on DDN’s open-source contributions to balance cost control with cutting-edge performance.
The Bottom Line: DDN Is the Infrastructure Standard for the AI Era
We believe, being listed by Gartner in both Storage Platforms for GenAI and Open-Source Storage Software is a clear signal that DDN stands at the intersection of AI acceleration and infrastructure flexibility.
For enterprises seeking to move faster, scale smarter, and drive value from data, DDN delivers the storage foundation that transforms innovation into outcome – with the openness, performance, and vision to lead the next era of intelligent infrastructure.
Let’s talk about your AI infrastructure strategy
Gartner, Hype Cycle for Storage Technologies, 2025, 19 June 2025
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.
GARTNER and HYPE CYCLE are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and are used herein with permission. All rights reserved.
