Could Disaggregated Infrastructure Redefine What Software-Defined Storage Can Do?

Based on recent industry analysis from Kings Research, global software-defined storage market revenue reached USD 6,063.7 million in 2025. Projections show a climb to USD 6,797.3 million in 2026, eventually reaching a valuation of USD 16,876.9 million by 2033. This represents a robust 13.87% compound annual growth rate over the 2026 to 2033 forecast period, highlighting a massive shift in how organizations architect their data center modernization strategies.

Historically, administrators deployed drives directly inside server chassis. Today, data demands force architects to separate hardware resources physically. By moving drives outside the server, administrators unlock entirely fresh capabilities. The central question remains: What happens when software-defined storage manages resources completely separated from the compute servers?

What Is Software-Defined Storage?

Software-defined storage abstracts the physical data capacity from the underlying hardware, allowing an intelligent control layer to provision and govern resources dynamically. Instead of relying on proprietary appliances built with tightly coupled controllers and disk arrays, organizations deploy this software layer across commodity servers. This software approach creates logical volumes from disparate physical disks, providing administrators with a unified dashboard to allocate capacity instantly.

The software manages data protection, compression, and automated tiering exclusively through algorithms, bypassing hardcoded hardware restrictions. By decoupling the control plane from the physical media, software-defined storage allows IT teams to build highly scalable environments capable of adapting quickly to changing business requirements. The architecture gives administrators granular control over their entire data ecosystem from a single interface, eliminating the siloed management required by traditional hardware arrays.

Why Is Storage Moving Outside the Server?

Enterprise workloads, particularly AI training and heavy data analytics, demand massive datasets. Processing these datasets requires specialized accelerators like GPUs, which scale at entirely different rates than disk capacity. Traditional infrastructure forces architects to purchase servers containing both processing power and drive bays. When a cluster exhausts its drive capacity, administrators must buy a fresh server, acquiring unnecessary CPUs and memory just to gain extra drive slots.

Keeping massive amounts of drives physically attached to individual servers limits scalability. Power consumption, cooling requirements, and physical space within a standard rack unit create rigid boundaries. High-performance accelerators generate extreme heat, leaving minimal thermal budget for densely packed NVMe drives within the same chassis.

The U.S. Energy Information Administration (EIA) released its Annual Energy Outlook 2026, revealing that data center servers alone accounted for an estimated 7% of total commercial sector electricity consumption in 2025. Furthermore, the California Energy Commission reported in early 2026 that data centers consumed roughly 1,000 megawatts, representing 2% of California's peak electricity demand. These massive energy footprints force facility managers to find far more efficient layouts.

Separating capacity from processing hardware offers a clear solution. Disaggregated infrastructure physically removes drives from the compute server, placing them in dedicated enclosures connected via high-speed networks. The U.S. Department of Energy utilizes similar concepts in exascale computing facilities. Their supercomputers utilize massive standalone file systems operating entirely separate from compute clusters to maximize utilization. This architectural separation ensures that facilities scale capacities exactly as workloads require, completely avoiding wasted watts on idle components.

What Changes When Software-Defined Storage Meets Disaggregated Infrastructure?

Disaggregated software-defined storage separates physical drives from compute hardware, while a software layer manages these pooled resources. This architecture allows capacity and processing power to scale independently, utilizing high-speed protocols like NVMe-oF to connect applications with remote data seamlessly across the data center fabric.

Software-defined storage relies on a control layer to manage hardware resources dynamically. It abstracts physical drives, creating logical volumes that administrators can provision easily. Conversely, disaggregated infrastructure represents a physical hardware architecture that separates processors from drives.

These two concepts complement each other perfectly. Disaggregation provides the flexible physical hardware layout, while software-defined storage provides the intelligence to orchestrate it. When combined, the architecture allows administrators to pool remote drives and allocate them dynamically to any server across the network.

How Does NVMe-oF Make Disaggregated Storage Practical?

Historically, accessing remote drives introduced severe performance penalties. Legacy protocols like iSCSI added massive overhead, creating latency that rendered remote drives useless for high-performance applications. Non-Volatile Memory Express over Fabrics (NVMe-oF) fundamentally alters this dynamic.

NVMe-oF translates the highly efficient PCIe commands used by local NVMe SSDs into network packets. By leveraging Remote Direct Memory Access (RDMA) over Ethernet or InfiniBand, NVMe-oF allows a remote server to read and write to a shared drive cluster with nearly identical latency to a local drive.

Recent industry developments showcase this capability clearly. In May 2026, Western Digital announced the expansion of its Open Composable Compatibility Lab alongside fresh OpenFlex Data24 EBOF disaggregated solutions engineered specifically for NVMe-oF workloads. These platforms deliver bare-metal NVMe performance across Ethernet fabrics, allowing enterprises to scale seamlessly.

NVMe-oF is a foundational pillar for a new composable architecture, ensuring organizations can pool resources while maintaining ultra-low latency I/O. This protocol allows the orchestration layer to dynamically attach remote NVMe namespaces to bare-metal servers or virtual machines instantly. 

Why Does the Network Become Part of the Storage Architecture?

Once drives leave the server chassis, the network fabric dictates overall system performance. Network bandwidth, switch congestion, and routing paths become integral to the I/O pipeline. Administrators must design lossless Ethernet networks utilizing advanced protocols like RoCE v2 to handle constant, heavy data flows. In this model, the top-of-rack switch acts functionally identical to a traditional server motherboard's PCIe bus.

Can Independent Scaling Solve the Storage-Compute Imbalance?

Independent scaling represents the most compelling economic argument for this architectural shift. Traditional hyperconverged infrastructure requires purchasing servers containing fixed ratios of CPU, RAM, and disk space.

Consider a worked example:

A media rendering cluster currently utilizes 100% of its disk capacity, but only 40% of its CPU power. In a conventional architecture, the administrator must purchase a completely new hyperconverged server. They pay for expensive CPUs and RAM they rarely need, simply to access the drive bays in the new server. In a disaggregated architecture, the administrator provisions a new NVMe enclosure containing exclusively drives. They connect it to the fabric, and the software layer instantly expands the available capacity pool. The facility completely bypasses the purchase of unnecessary processing power.

This resource pooling methodology maximizes cloud infrastructure efficiency. By serving multiple applications from a single, massive pool of high-speed drives, organizations drive utilization rates significantly higher. VAST Data's architecture documentation similarly describes separating compute resources from persistent data, allowing compute and storage capacity to scale entirely independently based on specific application needs.

Why Could Disaggregation Matter More for AI Than Conventional Enterprise Workloads?

AI infrastructure requires compute and storage to scale at different rates. Disaggregated architectures allow capacity and compute resources to expand independently while providing shared access to massive datasets. This reduces duplicate copies and helps infrastructure operators allocate resources exactly according to workload requirements.

Modern AI server infrastructure runs entirely on data pipelines. Training Large Language Models requires feeding continuous streams of text or image data into expensive GPU clusters. If the GPU finishes processing its batch and waits for the next dataset to load, the organization wastes massive amounts of money on idle compute time.

Disaggregated software-defined storage provides a massive, high-throughput data lake accessible simultaneously by hundreds of GPU nodes. When the cluster saves a model checkpoint (a massive write operation), the fabric absorbs the I/O burst while preserving speed for adjacent training tasks. 

Could Disaggregated Storage Reduce Repeated Dataset Copies?

In traditional server-attached models, researchers must copy massive datasets to the local drives of every single GPU server to ensure fast access. This practice wastes petabytes of space and creates version control complications. A shared, disaggregated pool allows every GPU node to access the same centralized dataset concurrently at PCIe speeds, virtually eliminating redundant data copying and ensuring researchers always train on the most current datasets.

Where Do Kubernetes and Containerized Applications Fit Into the Disaggregated Model?

Containerized applications operate entirely independent of persistent state. When a pod dies, the data dies with it. Kubernetes requires external mechanisms to provide persistent volumes.

Disaggregated architectures integrate perfectly with Kubernetes via the Container Storage Interface (CSI). When a developer deploys a database pod requiring 500GB of high-speed block storage, Kubernetes communicates with the control plane. The software dynamically carves out a volume from the remote NVMe pool and attaches it to the specific worker node hosting the pod.

A 2025 Lightbits and AMD reference architecture demonstrates a disaggregated environment designed specifically for Kubernetes, delivering millions of IOPS for AI and database workloads. Furthermore, USENIX research describes a Kubernetes-integrated architecture where physical drives exist separately from compute, and logical volumes are provisioned from a shared pool over NVMe-oF.

Does Disaggregated Storage Make Persistent Volumes More Flexible?

Absolutely. If a Kubernetes worker node fails, the pod instantly respawns on a healthy node. Because the drives reside on the network fabric rather than inside the failed server, the software layer simply detaches the volume from the dead node and reattaches it to the new one in milliseconds. This mobility makes stateful workloads highly resilient, allowing continuous CI/CD pipeline operations without interruption.

What Happens to Storage Management When Resources Are Pooled?

Centralizing hardware into a massive shared pool shifts administrative focus from hardware maintenance to policy management. Instead of configuring RAID arrays on individual servers, administrators define Quality of Service (QoS) policies within the software layer.

The software automatically dictates data placement based on workload priority. If a critical AI inference engine shares the pool with a batch-processing application, the software layer guarantees a minimum bandwidth threshold to the AI workload, throttling the batch job if network congestion occurs. This multi-tenant capability ensures predictable performance across diverse applications sharing the same physical hardware.

Can Disaggregated Software Defined Storage Reduce Infrastructure Waste?

Overprovisioning plagues modern data centers. Administrators routinely buy drives they rarely fill just to ensure they possess enough headroom for unexpected spikes.

Data published by the U.S. Environmental Protection Agency (EPA) highlights that data centers account for roughly 2% of total U.S. electricity consumption. A massive portion of this energy powers underutilized servers spinning idle disks. Disaggregation attacks this waste directly.

By centralizing capacity, the architecture aggregates the headroom required by multiple applications into a single, highly utilized pool. Furthermore, hardware refresh cycles become far more sustainable. Administrators upgrade compute nodes when faster CPUs are released, leaving the drive enclosures completely intact. Later, they upgrade the enclosures to denser SSDs while avoiding modifications to the compute nodes. This decoupling drastically reduces electronic waste and extends the lifespan of expensive hardware components.

IBM states that disaggregated systems involve high upfront costs, including specialized networking requirements and separate compute enclosures. Organizations must present the economics as workload-dependent, carefully evaluating whether the architectural efficiency offsets the initial infrastructure investments.

What New Bottlenecks Does Disaggregation Introduce?

Moving drives across the network fabric introduces substantial engineering challenges. Network dependency stands as the most prominent vulnerability. A misconfigured switch or a severed fiber link instantly severs an application from its data, expanding the failure domain across the entire rack.

Latency spikes represent another severe threat. While NVMe-oF theoretically matches local PCIe speeds, network congestion quickly degrades performance. If an application suddenly dumps a massive dataset onto the fabric, it causes packet drops that slow down every other application sharing those switches. A USENIX study specifically examines traffic-control mechanisms for NVMe-oF environments, demonstrating that network architecture and I/O traffic management remain paramount design considerations.

Organizations must invest heavily in high-end Ethernet switches supporting Priority Flow Control and Explicit Congestion Notification. Managing these lossless networks requires highly specialized engineering skills, increasing the operational complexity for IT departments migrating from simple direct-attached hardware.

Is Software Defined Storage Evolving Into a Broader Data Infrastructure Layer?

The convergence of these advancements pushes the industry toward intelligent modern cloud infrastructure. The SNIA Storage.AI initiative points toward a future featuring GPU-aware data pipelines and accelerator-initiated I/O.

Instead of routing data through the host CPU, emerging architectures utilize Data Processing Units (DPUs) and SmartNICs to pull data directly from the disaggregated pool straight into high-bandwidth memory for AI infrastructure on the GPU. A 2026 arXiv research project explores disaggregated memory architectures for AI inference workloads and terabyte-scale context states, indicating that resource separation extends far beyond conventional capacity.

The software layer evolves from a simple volume manager into an intelligent traffic controller orchestrating data movement across complex hardware hierarchies. Industry players continue pushing these boundaries. In June 2025, WEKA and Nebius partnered specifically to deliver cloud-native, high-performance data infrastructure optimized for large-scale AI and machine learning workloads, proving the massive demand for specialized architectural layouts.

What Does the Software-Defined Storage Landscape Reveal About This Architectural Shift?

Analyzing the software-defined storage sector validates this technical evolution. Kings Research data indicates that the on-premises deployment type held a massive 38.23% share in 2025. This statistic proves that organizations are aggressively modernizing their physical data centers rather than relying exclusively on public cloud providers.

Object storage architecture exhibits a remarkable 17.34% CAGR from 2026 to 2033, perfectly aligning with the explosive growth of unstructured AI training datasets. Furthermore, the Asia Pacific region demonstrates rapid modernization with a projected 17.13% CAGR between 2026 and 2033, while North America maintained a dominant 37.23% regional share in 2025.

These figures demonstrate a global, cross-industry mandate to adopt flexible, software-driven infrastructure capable of handling next-generation workloads. In April 2026, DDN received CRN recognition for its software-defined storage category leadership with the Infinia platform, highlighting the commercial demand for AI-focused data infrastructure platforms.

What Would a Disaggregated SDS Architecture Look Like in Practice?

Visualizing this architecture requires understanding the distinct layers separating applications from physical media.

Applications / AI Workloads / Kubernetes Workers

(Compute layer requiring high performance)

↓

SDS Control + Orchestration Layer

(The intelligence defining volumes, QoS, and routing)

↓

NVMe-oF / High-Speed Network Fabric

(Lossless Ethernet or InfiniBand switches acting as the backplane)

↓

Shared NVMe Storage Pool

(Dense enclosures housing petabytes of NVMe media)

In this model, multiple CPU and GPU compute nodes sit on top, connected via massive fiber cables to the network fabric. The storage enclosures sit at the bottom, also connected to the fabric. The software control plane sits in the middle, directing traffic and ensuring each compute node receives exactly the I/O performance it requires from the shared pool below.

Architectural Decision Matrix

Conclusion: Could Disaggregation Expand the Role of Software Defined Storage?

Disaggregation fundamentally alters the trajectory of these software layers. Moving drives outside the server elevates the software from a basic abstraction tool into a comprehensive orchestration engine for the entire data center fabric.

Traditional hardware architectures retain their place for predictable, small-scale deployments. However, as AI models grow exponentially and Kubernetes dominates application deployment, flexibility becomes paramount. Organizations require infrastructure that scales processors and capacity entirely independently. While network complexity requires careful engineering, the operational benefits of shared datasets, reduced hardware waste, and dynamic volume provisioning present a compelling path forward. The software layer ultimately provides the intelligence necessary to make physical disaggregation practical, efficient, and highly performant.

Explore the software-defined storage market research detailing regional growth and architectural adoption trends. Request access to the full industry report to review comprehensive data.

FAQ

What is disaggregated software-defined storage?

It represents an architecture separating physical drives from compute hardware, utilizing a software layer to manage these pooled resources. This allows capacity and processing power to scale independently across high-speed fabrics.

How does disaggregated storage differ from software-defined storage?

Software-defined storage refers to the intelligence abstracting hardware, while disaggregation refers to the physical separation of compute processors from drive enclosures across a network.

What role does NVMe-oF play in disaggregated storage?

NVMe-oF provides the high-speed network protocol required to transport I/O commands between compute nodes and remote drives with latency matching local PCIe connections.

Is disaggregated storage suitable for AI workloads?

Yes, the architecture excels at providing massive GPU clusters with shared, simultaneous access to immense training datasets, avoiding unnecessary data duplication.

Does disaggregated storage reduce infrastructure costs?

By enabling independent scaling and pooling resources to boost utilization rates, organizations drastically reduce the purchase of unnecessary, overprovisioned hardware.

What are the main challenges of disaggregated storage?

Network dependency introduces potential latency bottlenecks and requires highly specialized engineering skills to manage complex, lossless network traffic efficiently.