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How to Choose Software-Defined Storage Vendors in 2025

Posted on September 17, 2025

Picking the right storage stack used to mean comparing controller SKUs and RAID levels. Today, most teams shortlist software-defined storage vendors that can run on commodity servers, integrate with Kubernetes, and scale linearly. This guide translates the noisy SDS market into engineering criteria you can defend in a design review. We will define SDS precisely, map the core data services you should demand, outline performance and resilience tests, and give a step-by-step method to build a credible shortlist.

SDS is not just “storage on servers,” it is a control plane that virtualizes capacity and data services independently of hardware, then exposes them through policy and APIs. That separation of concerns is the foundation for portability and automation.

What is Software-Defined Storage?

SDS virtualizes storage resources and data services, then programs them via software policy, not proprietary hardware. The software layer provides provisioning, replication, snapshots, tiering, encryption at rest, compression, deduplication, thin provisioning, quality of service, and telemetry. The hardware becomes a failure-replaceable substrate.

Two implications matter to architects:

Hardware freedom with guardrails: You can source NVMe, SSD, or HDD from multiple vendors. You still validate NICs, CPUs, and NVMe firmware for the vendor’s HCL, then standardize images.
Automation first: SDS exposes APIs and declarative policy. Infrastructure as code for storage is the new default.

Why SDS now, not later

Three trends push SDS from “interesting” to “table stakes”:

NVMe everywhere. NVMe media and fabrics collapsed IO stacks and cut protocol overhead, so SDS can deliver low latency on standard servers.
Kubernetes persistence. CSI matured, PersistentVolumes are stable, and day-2 operations depend on snapshot, clone, expansion, and topology awareness.
Disaggregated and scale-out designs. NVMe-oF lets you expose remote NVMe at microsecond-class latencies, so you can scale storage independently from compute.

For an accessible overview of how NVMe uplift changes platform choices, TechBullion’s piece on storage evolution is a good primer for non-storage stakeholders.

Core Architectures you will encounter

Block, file, object. Many SDS platforms start with distributed blocks, then add NFS or SMB gateways, and S3-compatible objects for backup or analytics lakes.
Scale-out vs scale-up. Prefer scale-out with consistent hashing and per-object placement. It avoids forklift upgrades and gives you incremental failure domains.
HCI vs disaggregated. Hyperconverged collapses compute and storage on the same nodes, which is simple for VMs. Disaggregated storage pools serve multiple compute clusters and scale more predictably for mixed workloads.
Fabrics and protocols. Expect NVMe-oF, iSCSI, NFSv4.1, SMB3 with continuous availability, and S3 API. NVMe-oF over RDMA or TCP is becoming the default for low-latency east-west IO.

Evaluation criteria for software-defined storage vendors

Use this checklist during PoCs and RFPs. It maps to the most common failure modes seen in production.

1) Performance and efficiency

Latency, IOPS, throughput. Measure 99th percentile latency under mixed 70/30 read-write and 4k to 64k blocks, not just peak IOPS.
CPU per IO and storage efficiency. Compare CPU% at target latency, plus effective capacity after compression and dedupe at your data profile.
Media and fabric choices. Validate NVMe namespaces, multipath behavior, and congestion control on RDMA or TCP transports. Use NVMe-oF references to align test design.

2) Data protection and cyber resilience

Snapshots and clones. Look for redirect-on-write with instant clones and negligible metadata amplification.
Local and remote replication. Async and sync replication with consistency groups, plus snapshot shipping to an object tier for ransomware-resilient copies.
Erasure coding and rebuild math. Prefer math that reduces blast radius at petabyte scale. Rebuild time matters more than raw parity level.

3) Kubernetes and virtualization fit

CSI features. Dynamic provisioning, volume expansion, snapshots, topology awareness, raw block support, and per-PVC QoS. Validate against upstream docs and your distro’s supported matrix.
VM stacks. Native vSphere integration, Proxmox or KVM drivers, and Windows Failover Clustering considerations.

4) Operations and automation

APIs and IaC. Terraform providers, Ansible modules, and event hooks for autoscaling.
Observability. Prometheus exporters, detailed placement maps, and SLO-grade alerts.
Upgrades and failure domains. Rolling upgrades with strict version skew guarantees and safe drain procedures.

5) Economics

TCO components. License model per node, per TiB raw, or per TiB usable, plus support tiers. Include NICs, NVMe endurance, power, and cooling.
Scale economics. Small cluster overhead vs large cluster discounts. Watch minimum node counts and feature gates that unlock at higher tiers.

Quick capability map by workload

VM farms and VDI. Favor platforms with write-cache designs that shield random small writes, instant clones, and CBT-friendly snapshots.
Databases. Demand strict sync replication options, fast failover, and consistent 99p latency.
Analytics and AI. Blend NVMe tiers for hot data, object for cold stages, and parallel read performance.
Backup and archive. S3-compatible object, versioning, and immutability.
Edge and ROBO. Small node counts, lightweight quorum, and bandwidth-efficient replication.

If you want a broad market view while you shortlist, this curated guide to SDS vendors is a useful comparative read during vendor research.

How SDS integrates with Kubernetes, in practice

Your cluster requests storage through PersistentVolumeClaims that map to StorageClasses, which encode parameters for your CSI driver. Features to insist on during tests:

Snapshots and restore. Native CSI snapshot API, tested on your distro.
Topology and zoning. Awareness of zones and racks for failure containment.
Quota and QoS. Per-PVC throughput and IOPS caps to avoid noisy neighbors.

NVMe-oF, briefly, and why it matters

NVMe-oF extends NVMe semantics over networks, using RDMA or TCP. For SDS, that means remote drives can behave like local ones with low overhead, so you can separate compute and storage without paying a big latency tax. The NVMe-oF specification from NVM Express is the canonical reference for transport details and binding behaviors.

How to build a defensible SDS shortlist

Define workload profiles. IO mix, working set size, replication RPO, and growth curve.
Fix a target architecture. HCI or disaggregated, block or file or object, fabrics and protocols.
Select three vendors. One established enterprise option, one open-source or community-driven platform, and one challenger with clear differentiation.
Write an acceptance test plan. Latency SLOs, failure injection, snapshot and restore, cluster upgrade, and observability.
Run a time-boxed PoC. Require scripted deploys, deterministic results, and repeatable runs.
Score with a weighted rubric. Performance 30, resilience 25, Kubernetes 20, operations 15, cost 10.
Document trade-offs. Call out limits like maximum namespace count, rebuild domains, or feature gates.

Reference architectures to sanity-check

Virtualization first. Three to five SDS nodes, dual 25/100 GbE, NVMe cache tier plus QLC capacity, VM storage via vSphere or Proxmox integration.
Kubernetes first. Independent SDS cluster served over NVMe-oF or iSCSI, CSI driver with StorageClasses per tier, snapshots integrated with the platform’s backup operator. Kubernetes documentation on PVs is the north star for the control plane interface.
Hybrid cloud. Local SDS for hot data, object tier in cloud for backups and cold data. Pay attention to egress math and encryption key handling.

Risk register: what trips teams in year one

Ignoring failure math. Rebuild time and data placement matter more than raw parity.
Under-scoped networks. Storage of east-west traffic needs proper buffers and QoS.
Unverified CSI features. Snapshots, expansion, and topology must be tested on your exact Kubernetes version.
Upgrade path surprises. Demand rolling upgrades with clear pre-flight checks and version skew policies.

Frequently Answer Questions:

What problems does SDS actually solve?

It unifies block, file, and object under one control plane, delivers policy-driven self-service, and provides snapshots, replication, and immutability on commodity hardware for predictable latency and faster recoveries.

Should I choose hyperconverged or disaggregated?

Pick hyperconverged for VM-heavy estates that scale in small, uniform steps. Choose disaggregated when multiple clusters and mixed workloads need independent storage scaling.

Which benchmarks matter most?

Measure 99th percentile latency under your real IO mix and block sizes, include failure injection and rebuilds, and keep hardware, firmware, and tooling identical across vendors.

What Kubernetes features are mandatory?

CSI snapshots and restore, dynamic provisioning, volume expansion, topology awareness, raw block where needed, and per-PVC QoS, all validated on your exact distro and version.

How should we model SDS costs?

Include licenses, support, NICs, NVMe endurance, power and cooling, spares, and minimum node overhead. Compare effective capacity using your data reduction ratios, not vendor claims.

Conclusion:

Treat SDS selection like you treat a database or a message bus. Define workload SLOs, insist on measurable performance at the 99th percentile, validate CSI features on your exact Kubernetes version, and test failure and upgrade paths. Use NVMe and NVMe-oF to separate compute from storage without paying a latency penalty, then automate everything through APIs and IaC. If you follow the evaluation criteria and PoC method in this guide, you will end up with software-defined storage vendors that match your IO profile, resilience needs, and operating model, and you will have the evidence to prove it in your design review.