Organizations today stand at a crossroads of transformation. On one side, there’s the legacy of decades of data and workloads built across on-premises environments. On the other, there’s the opportunity to harness AI and analytics in the cloud. The real challenge, and the opportunity, is connecting these worlds without compromise.
That’s where unified, intelligent storage comes in. NetApp recently announced the expansion of Google Cloud NetApp Volumes with enhanced block storage capabilities, building on our ongoing collaboration with Google Cloud. It’s a glimpse into the future of data infrastructure, one that’s built to eliminate silos, simplify operations, and unlock the full potential of enterprise AI.
One Foundation for Every Workload
When we first brought NetApp technology into public clouds, the goal was simple: make it possible for customers to run the same enterprise-grade storage experience anywhere. Today, that vision has expanded to include all major cloud platforms and, with this latest announcement, a full range of workloads, from traditional enterprise applications to cutting-edge AI systems.
Block storage is a key part of that evolution. By adding it to Google Cloud NetApp Volumes, enterprises now have the flexibility to run workloads that were once limited to on-prem environments, virtualized systems, databases, or high-performance computing, directly in the cloud. Just as importantly, they can manage both file and block data through the same service.
This matters because enterprise IT isn’t one-dimensional. Workloads rarely fit into neat categories, and forcing teams to use multiple systems for different protocols creates unnecessary friction. Unified storage gives organizations the power to design around outcomes, not constraints. Whether it’s performance, scalability, or governance, the data foundation remains consistent.
Bringing AI to Where the Data Lives
AI is transforming every industry, but its success depends on access to trusted, high-quality data. Too often, that data is scattered across environments, on-premises, in different clouds, or trapped in specialized systems. Moving it around isn’t only costly; it’s also inefficient.
The solution is to bring AI to the data, not the other way around. With capabilities like FlexCache and SnapMirror, we’ve made it possible to seamlessly extend data from any ONTAP-based environment into Google Cloud. Customers can use the same data set for operational workloads and AI processing without replicating or transferring terabytes of information.
This unified approach is critical for emerging AI use cases. Take Gemini Enterprise, for example. Its integration with Google Cloud NetApp Volumes allows enterprises to use their own context-rich, governed data for retrieval-augmented generation (RAG) and inference. Instead of relying solely on public models trained on generic data, they can generate insights based on the knowledge unique to their business. That’s what turns AI from a research project into a practical advantage.
Built for Choice and Performance
No two organizations are at the same stage of their cloud journey. Some are all-in on hyperscale cloud, while others maintain hybrid environments for reasons of performance, compliance, or cost. What they have in common is a desire for consistency: the ability to get the same storage experience, high performance, resilience, and simplicity, across every environment.
That’s been our focus from the beginning. Our data platform is built to support a mix of workloads, whether it’s electronic design automation (EDA), high-performance computing (HPC), VMware, or SAP. Each of these has unique demands, but they share the same need for low latency, predictable throughput, and enterprise-grade data management. By extending these capabilities to the cloud, we’re ensuring customers don’t have to choose between flexibility and performance.
When customers talk about their priorities, “freedom” comes up again and again. They want the freedom to choose their cloud, their architecture, and their speed of innovation. That’s exactly what a unified, intelligent data infrastructure delivers.
From Modernization to Intelligence
The conversation around cloud has evolved. It was about modernization, how to move faster, scale smarter, and operate more efficiently. Now, it’s about intelligence. The enterprises leading this next phase are the ones that can turn data into insight in real time.
That requires infrastructure that doesn’t just store data but understands it. With automation and observability built in, organizations can focus on outcomes instead of operations. This is especially powerful for AI workloads, where the line between infrastructure and intelligence is disappearing. The same data platform that powers transactional systems can now drive predictive analytics and generative AI.
What’s emerging is an intelligent layer that connects all of this,a data platform that’s as adaptable as the workloads it supports. It’s not tied to a single cloud or architecture. It’s built for a world where data is everywhere and AI is woven into every business process.
The Path Ahead
Cloud transformation isn’t about moving everything overnight. It’s about choice, continuity, and readiness for what’s next. The future of enterprise storage will be defined by how easily organizations can connect their existing data estates to the innovations happening in AI and cloud computing.
The new capabilities in Google Cloud NetApp Volumes (block storage, unified management, hybrid caching) are a step forward in that direction. But more broadly, they represent a shift in how we think about data itself. It’s no longer just something to store. It’s an active, intelligent resource that drives innovation.
