Yes—Kubernetes is established in enterprise production, and enterprises are putting databases, AI/ML and other data-heavy workloads on cloud-native platforms. But Kubernetes is an orchestration layer, not a complete storage or data-protection system. Enterprise readiness depends on pairing it with durable storage services, tested recovery, and operations that fit the workloads and environments you actually run.
What the adoption figures do—and do not—show
The Cloud Native Computing Foundation’s 2024 survey, published in 2025, reported that 80% of surveyed organizations ran Kubernetes in production, up from 66% in 2023. Its 2025 annual survey announcement, published in January 2026, reported production use by 82% of container users. These are separate survey results with different stated populations; they indicate broad adoption, not that every enterprise workload or storage design is suitable for Kubernetes.
Data-intensive workloads make storage a practical design concern rather than a niche one. In a 2025 survey sponsored by Portworx by Pure Storage and conducted with Dimensional Research, 98% of respondents said they ran data-heavy workloads in cloud-native environments; 69% reported database workloads and 60% reported AI/ML workloads there. The same vendor-sponsored survey found 31% identified persistent storage as a cloud-native challenge. These figures describe respondents, not all enterprises, and should be read as evidence of both adoption and operational friction.
What Kubernetes does—and what storage must provide
Kubernetes coordinates containerized applications: it schedules workloads and manages their desired state. Enterprise storage supplies the durable data services those applications depend on, including the appropriate access model, capacity and performance, and protection against failure or data loss. A workload can be orchestrated successfully while still being poorly served by its storage design.
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For each stateful application, decide how it consumes data and what must happen when a pod, node, cluster or site fails. The design should account for persistent volumes and their lifecycle, the storage interface and backend, snapshots or backups, replication and recovery, and the operational ownership of those services. A persistent volume alone is not a complete backup or disaster-recovery plan.
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Match the storage architecture to the workload
There is no single storage type that is best for every Kubernetes workload. Compare the workload’s access pattern, performance and scaling needs with the capabilities the platform actually exposes through Kubernetes. Block, file and object storage are not interchangeable merely because a vendor supports all three.
| Workload or data pattern | What to establish before selection |
|---|---|
| Transactional databases | Confirm the database’s required storage semantics, latency and throughput expectations, scaling behavior, and supported backup and recovery procedure. Test those requirements with the intended Kubernetes distribution and storage integration. |
| AI/ML and analytics | Establish how data is accessed and shared, whether the workload needs high-throughput reads or writes, and how datasets are staged, retained and recovered. Evaluate the data path and scale under the workload rather than relying on a general platform claim. |
| Virtual machines | Verify that the platform supports the required VM lifecycle and storage behavior, and assess migration dependencies, availability objectives and operational skills. A VM running on Kubernetes does not by itself make a migration equivalent to the existing virtualization environment. |
| Object-oriented applications | Determine whether the application expects object APIs and how object data is governed, protected and made available across environments. Validate integration and application compatibility rather than assuming a file or block interface is a substitute. |
Use the Container Storage Interface (CSI) as one part of the evaluation: check which storage features the driver exposes, how provisioning and volume lifecycle operations work, and what support boundaries apply to your Kubernetes distribution and storage platform. CSI integration is not proof of a particular performance level, resilience design or recovery outcome. Those need to be verified for the chosen configuration.
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How analysts frame enterprise storage comparisons
Gartner’s September 2025 public abstract for its Magic Quadrant for Enterprise Storage Platforms describes platform-native services and product features for structured and unstructured data workloads. Its evaluation frame includes high-level positioning—ability to execute and completeness of vision—as well as functional dimensions such as performance, scalability, storage efficiency, resilience and durability, ecosystem, public-cloud integration, hybrid operations, cyber-resilience, AI integration, virtualization and containers, databases, object storage and hybrid-cloud storage.
Those dimensions are useful as a checklist, but they do not establish which platform is best for a particular Kubernetes workload. Gartner’s public abstract exposes evaluation areas, not the full scores or scoring detail. Do not infer a vendor ranking or a workload-specific result from the list of criteria alone. Gartner’s enterprise-storage research names Pure Storage, NetApp, Dell Technologies, HPE, IBM, Hitachi Vantara, DDN, Huawei and IEIT Systems; their inclusion is not, by itself, proof of equal Kubernetes capabilities or a recommendation.
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| Evaluation axis | Questions to put to a vendor and verify in your environment |
|---|---|
| Workload fit | Are the database, AI/ML, analytics, VM or object-application requirements supported in the exact Kubernetes and storage configuration? What limitations or prerequisites apply? |
| Persistent-storage architecture | Which block, file or object services are available, how are they integrated, and what performance and scale evidence applies to the workload and configuration under consideration? |
| Data protection | How do snapshots, backup, replication, disaster recovery and cyber-recovery work? Which components are protected, what recovery dependencies exist, and how is restoration tested? |
| Hybrid and multi-cloud operations | Can teams apply policy consistently across environments? What differs by cloud, region or Kubernetes distribution, and how portable are application data and operations in practice? |
| Migration economics | What licensing exposure, application refactoring, skills, parallel-running period and ongoing operational effort will migration require? |
| Ecosystem and support | Which Kubernetes distributions and integrations are supported? Who owns incident response across the application, Kubernetes and storage layers, and what observability and governance tools are available? |
Why VMware modernization changes the storage decision
VMware cost and footprint concerns are contributing to modernization plans. In a 2025 Portworx by Pure Storage and Dimensional Research survey, 95% of respondents said they planned to reduce their VMware footprint, 33% planned to stop using VMware altogether, and 44% said their VMware enterprise-license agreements had increased by at least $1 million. These are survey findings, not a forecast that every organization will leave VMware or achieve savings by moving workloads.
For a Kubernetes-based VM strategy, compare the full migration path rather than license costs alone. Include application and data dependencies, conversion or refactoring work, staff expertise, support arrangements, recovery requirements, and the cost of operating old and new platforms in parallel. Moving a VM or adopting a new control plane is not automatically a like-for-like replacement; validate the applications and operating procedures that matter to your organization.
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A practical decision sequence
- Inventory the workloads. Separate databases, AI/ML and analytics, VMs, and object-oriented applications. Record their data access patterns, performance needs, recovery objectives, and dependencies.
- Set the Kubernetes boundary. Identify which workloads are intended to run on Kubernetes, which remain on existing infrastructure, and whether the goal is migration, new application development, or both.
- Specify storage and protection requirements. For each workload, define the needed storage interface, scale, availability, backup, replication and recovery behavior. Distinguish the application’s requirements from a vendor’s feature labels.
- Check integration and support. Confirm the exact Kubernetes distribution, CSI driver, backend and feature versions involved, plus which vendor supports each layer when there is an incident.
- Test representative failure and recovery scenarios. Validate application behavior, volume operations, restoration and operational handoffs with realistic data and failure conditions before production rollout.
- Compare total migration and operating effort. Include licensing, refactoring, skills, tooling, governance and any period of running two environments—not just the new storage or platform price.
- Expand in stages. Start with a workload whose requirements and recovery path can be demonstrated, then use operational evidence to decide whether the pattern should extend to more critical or different workloads.
What a sound enterprise conclusion looks like
The analyst evidence supports Kubernetes as a mainstream production platform and shows that data-heavy use is common among the surveyed cloud-native organizations. It does not prove that any one enterprise storage vendor wins, that all workloads should move, or that a Kubernetes storage integration will meet a particular service objective without validation. The useful conclusion is narrower: treat persistent storage and protection as core parts of the platform decision, compare vendors against workload-specific requirements, and verify recovery and operations in the configuration you plan to run.
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