What is Cloud Native Storage?

Modern applications demand storage that's as dynamic as the workloads they support. Cloud native storage delivers exactly that—software-defined, container-ready infrastructure that scales automatically, integrates seamlessly with Kubernetes, and provides persistent data access across distributed environments. Unlike traditional storage tied to physical hardware, cloud native storage abstracts resources through software, enabling flexibility, automation, and resilience for microservices architectures.

What is cloud native storage (CNS)?

Cloud native storage is software defined storage designed specifically for containers, microservices, and dynamic workloads. It abstracts physical hardware through software, exposes storage to Kubernetes via the Container Storage Interface (CSI), and provides persistent volumes that follow application lifecycles. CNS emphasizes automation, portability, and elasticity—so storage grows, shrinks, and heals in step with the platform and the application.

How cloud native storage works

• Abstraction — Storage is decoupled from hardware through software-defined layers, pooling resources across nodes and clusters.

• Kubernetes integration — CSI drivers enable dynamic provisioning, volume expansion (for operational elasticity), attach/detach, snapshots, lifecycle management for Persistent Volume Claims (PVCs, pod level storage requests), mapping them to Persistent Volumes (PVs, cluster storage resources).

• Policy-driven operations — StorageClasses define performance, resilience, and placement policies so developers request what they need while the platform decides how to fulfill it.

• Resilience — Replication, self-healing, and policy-based placement protect data across nodes/clusters.

• Portability — Data and policies follow workloads across on-prem, hybrid, and multicloud environments.

Key characteristics of cloud native storage 

• Consistency — Distributed systems use replication and consensus to keep data in sync across nodes; applications can rely on predictable read/write semantics.

• Durability — Redundancy across nodes and zones protects against hardware and software failures, ensuring data is not lost.

• Availability — Self-healing, failover, and topology-aware placement minimize downtime and maintain access during upgrades or failures.

• Scalability — Horizontally scale capacity and performance by adding nodes without service disruption; automation handles balancing and placement.

• Performance — Caching, tiering (flash/disk/object), and smart placement optimize I/O throughput and latency for diverse workloads. Machine learning pipelines require high-performance storage for training datasets and elastic scaling for GPU clusters. CNS supports these dynamic workloads seamlessly.

• Deployment — Native Kubernetes integration via CSI, StorageClasses, and other Kubernetes primitives to enable hands-off lifecycle management and GitOps-friendly workflows.

Types of cloud native storage

• Object storage — Highly scalable and designed for unstructured data, such as backups, media, archives, and AI datasets. 

• Block storage — Low latency volumes for databases/transactional apps. 

• Shared file storage — RWX access for apps that need concurrent reads/writes. Development teams use CNS for shared RWX file systems to store build artifacts and logs across multiple pods in CI/CD pipelines.

• Ephemeral storage — Fast, pod scoped scratch/caching; unlike persistent storage, it is tied directly to the lifecycle of the Pod, meaning data is lost if the Pod is deleted or fails.

Core components of a cloud native storage architecture

Cloud native database management

Databases in cloud native environments benefit from containerized deployment, rapid provisioning, and elastic scaling—critical for agility and resilience. Yet managing stateful data across distributed microservices introduces complexity around replication, failover, and performance consistency. Manual patching, backups, and tuning consume time and introduce risk.

Nutanix Database Service (NDB) streamlines operations with automated provisioning, patching, backup/recovery, and performance optimization across hybrid and multicloud environments. By centralizing governance and policies, NDB helps teams maintain compliance while reducing routine toil.

Cloud native file system (CNFS)

A CNFS is a distributed, scalable file system optimized for microservices and container workloads. It enables persistent, shared (RWX) access to files across pods and clusters—supporting content apps, analytics pipelines, and CI/CD artifacts—with seamless scaling and high availability.

Cloud native data protection

Protection in cloud native environments centers on continuous backup, immutable snapshots, replication, and policy-driven recovery across clouds using application-specific configuration. Applications and their state should move as a single portable entity for true cross-cloud mobility. Versioning defends against accidental changes; automation reduces human error and improves Recovery Time Objective (RTO)/ Recovery Point Objective (RPO). A robust strategy includes offsite backups, periodic DR testing, and ransomware-ready controls (immutability, encryption, role-based access).

Benefits of cloud native storage

Cloud native storage transforms how organizations manage data in containerized environments:

• Scalability and elasticity — Cloud native storage scales automatically with workloads, allowing capacity to expand or contract without manual intervention. This elasticity ensures applications can handle traffic spikes or resource changes efficiently. 

Example: Large e-commerce sites experience traffic spikes during seasonal sales. Cloud native storage enables automatic scaling of storage resources to handle sudden increases in transactions and product catalog updates without downtime.

• Performance and reliability — It delivers consistent performance across distributed environments, even as workloads shift between nodes or clusters. Built-in mechanisms like intelligent data placement and caching optimize I/O operations for high throughput and low latency. Studies show that cloud native storage deployments achieve 31% faster query performance and 27% reduced latency, delivering measurable performance gains for stateful applications. 

Example: Banks and fintech companies run stateful applications like payment gateways and fraud detection systems that require low-latency, high-throughput storage. CNS ensures consistent performance across distributed clusters, even during failover events.

• Portability — Cloud native storage enables workloads to move freely across private, public, and hybrid clouds without losing access to persistent data. This portability supports multicloud strategies and simplifies disaster recovery planning.

Example: Organizations modernizing legacy applications often run development workloads in public clouds for elasticity while keeping production on-premises for control and security. CNS portability ensures development databases, configuration stores, and persistent application state remain in sync whether the workload runs in AWS, Azure, or the private datacenter. This accelerates modernization without sacrificing consistency and stability.

• Cost efficiency — Automation and resource optimization reduce the need for overprovisioning, lowering infrastructure costs. Dynamic provisioning ensures storage resources are allocated only when needed, improving overall utilization. According to the IDC Business Value of Nutanix Cloud Platform report, organizations report 41% lower infrastructure costs through efficient resource utilization, with $1.47M per year in savings on compute and storage resources typical for enterprise deployments. Additionally, 88% faster new storage deployment accelerates time-to-value and reduces operational overhead.

Example: SaaS providers often overprovision storage to avoid downtime during peak usage. With cloud native storage, dynamic provisioning and automated tiering ensure resources are allocated only when needed—reducing idle capacity and lowering infrastructure costs.

• Data resilience — With built-in replication and fault tolerance, cloud native storage protects data against hardware failures and node outages. These features ensure high availability and durability for hybrid and distributed deployments.

Example: Hospitals and research institutions need secure, resilient storage for patient records and imaging data. CNS provides replication, encryption, and compliance features to meet HIPAA and GDPR requirements.

Common mistakes to avoid

• Treating all containers as ephemeral — Stateful applications require persistent volumes and careful data management.

• Mismatching storage type to data format — Ignoring data characteristics when selecting storage types (object, block, file) results in performance degradation and cost inefficiency.

• Ignoring storage performance classes — Mismatching I/O requirements to tiers (e.g., databases on cold storage) leads to latency and instability.

• Over-provisioning expensive storage tiers — Storing infrequently accessed data on high-performance tiers inflates costs. Implement tiering policies to match cost to access patterns.

• Single-cloud storage dependency — Tight coupling creates lock-in and limits portability and resilience.

• Overlooking access modes — Confusion between ReadWriteOnce (RWO) and ReadWriteMany (RWX) causes scheduling conflicts and availability issues.

• Neglecting backup and DR — Replication is not a complete DR strategy; backups, immutability, and failover orchestration are also required.

Storage planning considerations (checklist)

• Capacity planning & growth modeling — Baseline requirements, future projections, buffer capacity

• Performance baseline & monitoring — Benchmarks, SLAs, load testing approaches

• Protocol support & compatibility — Required protocols (iSCSI, NFS, S3, CSI), cross-environment consistency, performance characteristics

• Cost management & optimization — TCO analysis, tiering strategies, right-sizing

• Multi-tenancy & isolation — Tenant boundaries, quotas, noisy neighbor prevention

• Integration & compatibility — Application requirements, migration paths, backup validation

• Security & compliance — Encryption, RBAC, data sovereignty considerations

• Operational readiness — Documentation, training, support contracts

Quick decision guide

• Access mode — Does the workload need ReadWriteOnce (RWO) or ReadWriteMany (RWX)? 

• Performance class — Match I/O profile (IOPS, throughput, latency) to storage tier (NVMe / SSD / HDD / Object).

• Consistency requirements — Define read/write semantics and replication factors per workload.

• Data protection level — Include snapshots, backups, replication, immutability, and tested recovery objectives (RPO/RTO).

• Security & compliance — Ensure encryption, tenant isolation, audit trails, and data locality controls.

• Cost targets — Optimize with dynamic provisioning, auto-tiering, and lifecycle policies.

Cloud native storage with Nutanix

Nutanix delivers unified, scalable, and secure storage for cloud native workloads through Nutanix Cloud Platform, built on the proven foundation of Nutanix AOS, a mature, enterprise-grade software-defined storage platform with automatic self-healing, snapshots, replication, and adaptive performance optimization. With Cloud Native AOS, this same battle-tested storage technology now runs natively as containers on platforms like Amazon EKS, integrating seamlessly with Kubernetes via the Container Storage Interface (CSI) without requiring a hypervisor. Storage automatically scales and heals in step with applications across hybrid environments.

To further support stateful applications, Nutanix Database Service (NDB) automates database provisioning, patching, and backups. Combined with Nutanix's core object, block, and file storage capabilities, these solutions empower teams to manage persistent data effortlessly, handle the massive datasets required for AI pipelines, and build resilient, automated architectures.

AI and cloud native storage

As enterprises adopt AI and machine learning workloads, the need for scalable, secure, and high-performance storage becomes critical. Traditional infrastructure often struggles to support the massive datasets and GPU-driven compute requirements of AI pipelines. Cloud native storage addresses these challenges by providing elastic scalability, policy-driven data placement, and integration with advanced hardware accelerators.