Kubernetes Storage: Disaggregated or Hyper-converged?

Modern cloud-native environments demand more from storage than ever before. As Kubernetes becomes the dominant platform for deploying applications at scale, teams are confronted with a critical architectural choice: should storage be disaggregated from compute, or should it be hyper-converged? What are the pros and cons of both approaches?

This decision goes far beyond hardware layout. It influences how systems scale, how data is accessed, and how resilient your platform is under pressure. The wrong storage model can limit performance, increase costs, and add operational complexity. The right one can unlock massive flexibility, efficiency, and developer velocity.

At simplyblock, we believe the answer doesn’t have to be one or the other. That’s because our software-defined storage platform is uniquely designed to support disaggregated and hyper-converged architectures simultaneously, even within the same Kubernetes cluster.

Let’s walk through what these two storage models really mean in the Kubernetes world, explore their trade-offs, and show why flexible, protocol-aware storage is the way forward.

What Is Hyper-converged Storage in Kubernetes?

Bringing Storage and Compute Together

In a hyper-converged storage setup, storage services run directly on the same physical or virtual nodes as compute workloads. In Kubernetes, this often means that the same nodes running your pods are also hosting storage daemons or volume backends.

This approach is increasingly common in smaller clusters, edge deployments, and cost- or performance-conscious environments. It’s simple to set up. There’s no need for a separate storage layer or network path between compute and storage. Storage reads and writes can happen locally or over short intra-node connections, minimizing latency and potentially maximizing throughput.

Hyper-converged designs also align well with commodity hardware. By utilizing local NVMe or SSDs within each node, teams can build performant clusters without specialized SANs or expensive appliances. However, hyper-convergence has limitations.

As your cluster scales, the relationship between compute and storage becomes tightly coupled. If you need more storage capacity, you also need to add more compute nodes—even if you don’t need more compute power. The inverse is also true. Resource utilization can become inefficient, especially when workloads vary in demand.

What Is Disaggregated Storage in Kubernetes?

Separating Compute and Storage for Flexibility

Disaggregated storage separates the storage layer from compute entirely. Storage nodes run independently of the compute cluster, often connected over a network using protocols like NVMe/TCP or NVMe/RoCE. Kubernetes pods mount volumes from this remote storage layer via persistent volume claims (PVCs), backed by a CSI driver.

This model is popular in larger, performance-sensitive clusters or cloud-native platforms that need high availability and fault tolerance across zones or regions. It’s also the foundation for scalable storage backends in many hyperscaler architectures. Cloud storage offerings such as Amazon EBS or Google Persistent Disk are classic examples of disaggregated, networked storage.

The biggest advantage of disaggregation is independent scaling. You can add more storage without touching your compute nodes. Likewise, compute nodes can scale up or down freely based on workload demand, without worrying about running out of disk space or bandwidth.

Disaggregated storage is also a better fit for multi-tenant environments, where storage needs to be shared across services, isolated by namespace, or tiered by performance.

But disaggregation comes with its own challenges.

Performance is heavily dependent on your network fabric and storage protocol. Poorly tuned disaggregated setups can suffer from network bottlenecks or added latency. That’s why the choice of protocol—like NVMe/TCP vs NVMe/RoCE—matters deeply in these designs.

Choosing Between Hyper-converged and Disaggregated Storage

It’s Not a Binary Choice—It’s a Strategic One

Many engineering teams ask: which model is better? Hyper-converged or disaggregated?

The truth is: they both solve different problems, and the best environments combine them intelligently.

Hyper-converged storage shines in environments where performance is critical and hardware is constrained—like edge clusters, dev/test setups, or high-density NVMe nodes. There’s minimal network overhead, and operations can be simpler, especially in smaller teams or single-zone environments.

Disaggregated storage, on the other hand, gives larger and more dynamic platforms the ability to scale storage and compute independently. It supports long-running services, large datasets, and diverse performance needs, especially when integrated with advanced storage protocols and SDS platforms like simplyblock.

At simplyblock, we embrace this hybrid reality. Our platform allows you to run storage services co-located with compute (hyper-converged) or on separate nodes (disaggregated). Even more, we support hybrid deployments, where parts of the system are hyper-converged for speed and cost-efficiency, while others are disaggregated for scale and specialization.

Example: Mixed Hyper-converged and Disaggregated Storage in One Kubernetes Cluster

Let’s consider a growing technology company operating a centralized Kubernetes platform that serves two very different lines of business. On one side, they run internal services such as CI/CD pipelines, dashboards, billing systems, and API backends. These services are relatively lightweight, scale horizontally, and don’t have extreme storage performance demands. For these workloads, the team uses a hyper-converged model. Each Kubernetes worker node is equipped with a fast NVMe SSD and is part of the distributed simplyblock cluster. Storage volumes are provisioned directly from these local devices when possible—using local node affinity—reducing latency and avoiding any network hops.

At the same time, the company also supports an AI-driven product team developing machine learning models that process vast amounts of training data. These models run on GPU-enabled nodes in a different availability zone. To avoid wasting expensive GPU cycles on IO operations—or requiring each GPU node to carry massive local storage—the team uses a disaggregated storage layer. Here, simplyblock storage nodes are deployed separately, backed by high-density QLC SSDs. These storage nodes serve NVMe/RoCE volumes to the AI workloads with minimal latency and high throughput, thanks to RDMA-enabled network infrastructure already present in their data center.

Both storage models—hyper-converged for developer efficiency and disaggregated for high-throughput compute—run under the same simplyblock deployment. Storage policies defined in Kubernetes determine where volumes are provisioned. Developers request standard volumes that land on local disks, while GPU jobs use a “high-performance” storage class that routes traffic to the disaggregated pool via NVMe/RoCE.

This hybrid setup offers the best of both worlds: cost-efficiency for everyday workloads and high performance for data-intensive jobs—all without managing separate storage systems or duplicating operational overhead. It’s all managed under a unified storage control plane, with full observability, quality of service enforcement, and workload-based optimization handled by Simplyblock.

Why Simplyblock Supports Both Models Seamlessly

Our vision at Simplyblock is to remove architectural friction. That’s why our software-defined storage platform, with its unique MAUS architecture, was built from the ground up to support:

• Hyper-converged deployments, where storage runs side-by-side with workloads.
  
• Disaggregated architectures, where storage and compute are scaled and managed independently.
  
• Hybrid deployments, combining the best of both worlds.

With built-in support for NVMe/TCP and NVMe/RoCE, simplyblock ensures that disaggregated volumes deliver high throughput and low latency, even across data center fabrics. Meanwhile, hyper-converged setups benefit from local performance without giving up on manageability or observability.

We don’t tie you to a single model—or a single hardware vendor. Whether you run bare-metal nodes, virtual machines, or edge devices, Simplyblock adapts to your strategy, not the other way around.

Storage Architecture That Grows With You

The debate between disaggregated and hyper-converged storage in Kubernetes misses a bigger point: your architecture should adapt to your workloads, not the other way around.

With simplyblock, you can build clusters that optimize for both speed and scale. You can deploy hyper-converged nodes for local performance and disaggregated volumes for scalable, multi-tenant storage—all orchestrated through a single software-defined platform.

This flexibility is what modern Kubernetes platforms need. It’s how we help infrastructure teams break free from rigid storage models, unlock performance where it matters, and invest only where it makes sense.

Ready to evolve your Kubernetes storage? Let’s talk.

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