Generative AI has officially crossed over from buzzword to boardroom priority. Every executive conversation, every product roadmap discussion, and every customer innovation strategy now includes some variant of the question: “How do we operationalize GenAI at scale?”
At DDN, this is the world we live in every day. And Gartner’s recent report—“A CTO’s Guide to the Generative AI Technology Landscape” (March 2025, license required) is we believe one of the most grounded and useful explorations of this rapidly shifting terrain I’ve read. In our opinion, it doesn’t just catalog trends, it maps them. It gives technology leaders a way to cut through the noise and make smart decisions in a landscape that’s both exciting and chaotic.
According to Gartner: “the generative AI (GenAI) technology landscape, which consists of infrastructure, models, engineering tools and applications, is rapidly evolving, creating a new ecosystem of vendors and products that technology innovation leaders find difficult to navigate.” But what stands out most and aligns closely with what we’re seeing at DDN is this:
If your infrastructure isn’t AI-native, your GenAI ambitions are going to hit real-world bottlenecks, fast.
Let’s unpack what that means, and why organizations need to rethink how they build their data architecture if they want to thrive in the GenAI era.
GenAI Isn’t Just a Model Problem. It’s a Data Infrastructure Problem.
Too many enterprises still think of GenAI through a single-lens focus on models. Should we use GPT-4 or Llama 2? Fine-tune or prompt-engineer? Go open source or pay for API access? These are valid questions, but they miss a fundamental point: Your model is only as powerful as the infrastructure feeding it.
According to Gartner, “genAI models and applications require significant investments in specialized training infrastructure due to their massive size, which increases the amount of infrastructure required for initial training, retraining and inferencing. Infrastructure for GenAI models can be classified into three major subcategories — computing, networking and storage.” Model training is only one side of the equation. Inference, analytics, data governance, and real-time responsiveness are equally important, and they all depend on how well your infrastructure handles data movement, latency, and scale.
At DDN, we’ve seen firsthand what happens when traditional storage systems are forced into GenAI workloads: they collapse under the pressure. GenAI models, especially when used in real-time applications like Retrieval-Augmented Generation (RAG), require sub-millisecond access to billions of metadata-rich objects, with full governance and multitenancy. That’s not what legacy file systems were built for. DDN gives enterprises an end-to-end foundation for GenAI success.
Data Bottlenecks Are the Silent Killer of GenAI Initiatives
Gartner rightfully calls out that “the enormous demand for custom infrastructure to train the GenAI models has created a huge mismatch between supply and demand (particularly for GPUs) .” But here’s a layer deeper: even if you have the GPUs, they’re useless if your data can’t feed them fast enough.
This is the bottleneck most teams don’t see coming. Organizations invest in compute and model development, only to be derailed by:
- Slow metadata search when trying to contextually augment prompts
- Inefficient checkpointing and recovery during training
- Latency spikes when serving inference in real-time
- Unmanageable governance and siloed datasets across hybrid environments
We call this the infrastructure gap. And it’s why NVIDIA, xAI, and top financial and research institutions rely on DDN. They know that an AI data platform must go far beyond speed, it must be intelligent, flexible, secure, and built to scale with data, not just hardware.
From ModelOps to DataOps: The Rise of the Intelligent Data Layer
Gartner states: “new engineering tools that help enterprises operationalize their GenAI use cases such as vector databases; API orchestration tools and AI trust; and risk and security management tools are all experiencing early interest.” But again, the unsung hero in all of this is the data layer that makes these tools usable in enterprise environments.
For instance:
- A vector database is only as fast as the underlying object store it retrieves embeddings from.
- A RAG pipeline is only as effective as its ability to access and manage real-time metadata.
- Governance frameworks are only meaningful if your storage architecture actually supports secure multitenancy, auditability, and tagging.
This is where DDN stands apart. It doesn’t just store data. It makes data intelligent, indexable, searchable and well-orchestrated. It brings governance, performance, and metadata to the heart of the AI pipeline. It turns infrastructure into a competitive advantage.
AI Will Be Everywhere, But Only the Right Infrastructure Will Keep Up
“By 2026, more than 80% of enterprises will have used GenAI APIs, models and/or deployed GenAI-enabled applications in production environments, which is a significant increase from less than 5% today.” According to Gartner, “By 2026, more than 70% of independent software vendors (ISVs) will have embedded GenAI capabilities in their enterprise applications, which is a major increase from less than 1% today.” That’s an exponential leap from where we are today—and it means infrastructure teams must act now.
The GenAI leaders of tomorrow are the companies investing today in intelligent, scalable, and AI-optimized infrastructure. This doesn’t mean every enterprise needs to build their own foundation models. But it does mean every enterprise needs:
- Flexible data platforms that span on-prem and cloud
- High-throughput, low-latency storage that scales with usage
- Architectures that can power both training and inference with equal precision
- Secure, trusted, and proven data infrastructure that ensures peace of mind and increased efficiency
DDN’s Data Intelligence Platform is purpose-built for this new world. Our customers are using it to cut training times, enable instant inference, reduce infrastructure sprawl, and accelerate time to insight.
Closing Thoughts: Infrastructure as a Competitive Edge
Generative AI is not a trend, it’s a transformation. Innovation without infrastructure is just potential, not performance.
If your organization is serious about building the next generation of AI-driven applications, the foundation starts with data. Not just where it lives, but how it moves, scales, secures, and informs. At DDN, we’re helping the most ambitious teams in the world bridge the gap between innovation and execution. Because in the era of GenAI, the smartest infrastructure wins. Visit our website to learn more.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.
