What is persistent storage for containers?

Essential to cloud-native application development, containers are lightweight, portable units of software that package an application and its dependencies, making them simple to deploy across modern IT infrastructure.

Containers are inherently ephemeral. They are intended to be temporary, launching and shutting down as needed. While this flexibility makes them highly flexible and scalable, any container data generated is lost when the container stops running. Persistent storage solves this issue by keeping data available independently of any individual container.

Without persistent storage, critical systems would fail. For instance, a bank’s transaction database running in containers would lose customer account balances during routine updates or an e-commerce platform would lose shopping carts with each restart.

As organizations continue shifting toward cloud-native and microservice architectures, containers have become central to app deployment and management, making persistent storage for containers essential for running stateful applications at scale. According to a recent report from Strategic Market Research, the global application container market was valued at approximately USD 2.1 billion in 2024. It is projected to reach USD 6.9 billion by 2030, growing at a compound annual growth rate (CAGR) of 21.1%.¹

In enterprise environments, persistent storage comes in the form of file, block and object storage, each appropriate to different workloads. Organizations typically deliver these storage solutions through a combination of hardware systems and software-defined storage (SDS) platforms designed to support hybrid cloud and distributed cloud environments.

Containerization consists of packaging software code with only the operating system (OS) libraries and dependencies—typically Linux-based—required to run it. This process creates a single lightweight unit, such as a container, that can run consistently across any infrastructure.

As organizations shifted from virtual machines (VMs) to containers, the need to manage containerized workloads at scale grew. Docker, introduced in 2013, made containers widely accessible by offering developers a standardized way to build and share them. But orchestrating hundreds or thousands of containers across hybrid multicloud environments requires a way to handle complexity. Hence, Kubernetes was developed to automate the deployment, scaling and management of containerized applications.

Created by Google in 2014, Kubernetes is an open source platform maintained by the Cloud Native Computing Foundation (CNCF). Major cloud providers such as AWS, Microsoft Azure, Google Cloud and IBM Cloud® support the platform.

Kubernetes runs containers in pods, which are deployed across nodes in a Kubernetes cluster. It manages configuration and communication between components through application programming interfaces (APIs), supporting automated orchestration across diverse systems. Today, Kubernetes is the de facto standard for container orchestration.

In relation to data storage, an important aspect of how Kubernetes works is understanding the distinction between stateless and stateful applications. Stateless applications (for example, web servers handling API requests) handle each request independently. As a result, they do not retain data between sessions. In contrast, stateful applications (for example, databases) do retain data and depend on information from previous interactions to function properly.

Moreover, containers and pods in Kubernetes are ephemeral, able to be stopped, restarted or rescheduled at any time. For stateless applications, this behavior is not an issue. However, in stateful applications, when a container stops, any data stored inside it is lost. Here’s where persistent storage plays an essential role in containerized settings by separating data from the container lifecycle.

In addition to traditional applications moving to containers, data-intensive workloads like databases, artificial intelligence (AI) and machine learning (ML) are increasingly cloud-based. These workloads require persistent storage to ensure that data survives container termination, maintains state within distributed systems and provides the high-throughput, low-latency performance that model training demands.

Persistent storage for containers is built on a set of components that work together to separate data from the containers. In Kubernetes, administrators configure the storage infrastructure, while developers and applications access it through simple requests.

These components include:

• Volumes and bind mounts
• PersistentVolume (PV)
• PersistentVolumeClaim (PVC)
• Storage classes
• Container storage interface (CSI)

There are two main ways to attach storage to containers: bind mounts and named volumes (for example, Docker volumes).

• Bind mounts connect a specific file or directory from the host machine directly into a container.

• Volumes gain flexibility because Kubernetes manages them across different storage systems.

A volume is a storage location accessible to containers in a pod. Unlike ephemeral storage inside a container, which disappears when the container stops, a volume persists for the life of the pod. This means that if a container fails and restarts within the same pod, the data in the volume remains available.

Volumes can connect to different types of storage devices, including local disks, network-attached storage through protocols such as Network File System (NFS) or cloud-based storage services.

A PersistentVolume provides storage within the Kubernetes cluster and is created either manually or automatically.

The key difference between a regular volume and a PersistentVolume is lifespan. A PersistentVolume exists independently of any pod. This setup means that the storage persists even if the pod that accesses it is deleted or moved to another machine.

PersistentVolumes have their own lifecycle separate from the pods that use them. Administrators can configure them with specific storage capacity, read/write access permissions (for example, ReadWriteOnce for single-pod access or ReadWriteMany for shared access).

A PersistentVolumeClaim is a storage request made by an application or user. Instead of connecting directly to a PersistentVolume, a pod uses a PersistentVolumeClaim as an intermediary layer. The claim specifies the required storage capacity and the required access mode. Kubernetes then matches it to an available PersistentVolume. This separation means that developers can request storage without having to understand the underlying storage infrastructure.

When a claim is connected to a PersistentVolume, the pod can read and write data just as it would with any file system. If the pod is moved or restarted, it can still access the same claim and the same persistent data.

In enterprise environments, manually creating storage volumes for each application becomes complex and unmanageable. Kubernetes solves this challenge through StorageClasses, which define different types of storage (for example, high-performance solid-state drives) and use a provisioner to automatically create data volumes on demand.

When an application requests storage and references a StorageClass, Kubernetes provisions the appropriate volume without needing manual setup. This feature simplifies overall storage management.

The Container Storage Interface (CSI) is a standardized vendor-neutral API that enables Kubernetes to interact with various storage systems.

CSI allows storage providers’ platforms (for example, IBM Storage Fusion, NetApp) to develop and update their own plug-ins independently. These plug-ins manage the complete storage lifecycle: creating, attaching, provisioning and removing volumes as needed.

Persistent storage for containers enables organizations to run stateful applications in containerized settings, delivering the following benefits:

• Data durability and resilience: Data written to each persistent volume survives container failures, restarts and rescheduling, preventing data loss and ensuring stateful applications remain resilient even as the underlying container infrastructure shifts.

• Simplified operations: Dynamic provisioning and automated storage management reduce manual workload. Platform teams define storage policies once, allowing applications to consume storage as a self-service resource within their namespace.

• High performance and scalability: Persistent storage for containers delivers the throughput, low latency and scalability required for data-intensive workloads, such as AI/ML training and real-time analytics.

• Flexibility and portability: Kubernetes persistent volumes and CSI drivers abstract storage, allowing organizations to run applications across on-premises infrastructure, private cloud and public cloud environments, supporting hybrid cloud strategies.

• Security and compliance: Persistent volumes backed by enterprise storage systems provide data protection features, including encryption, replication and backup capabilities needed to meet compliance and regulatory requirements.

• Cost efficiency: Dynamic provisioning scales storage up or down based on demand, while automated data tiering moves infrequently used data to cost-effective storage tiers, helping organizations optimize costs.

• Shared access: Persistent storage for containers enables multiple pods to simultaneously read and write the same data, supporting collaborative workflows without duplicating storage resources.

Organizations can access persistent storage for containers through a range of tools and solutions:

• Container orchestration platforms
• Enterprise storage solutions
• Public cloud providers 

Container orchestration platforms (for example, Red Hat OpenShift) provide integrated persistent storage management with built-in support for CSI drivers and dynamic storage provisioning.

These platforms simplify deployment and operations for organizations running containerized workloads at scale.

Enterprise storage platforms (for example, IBM Storage Fusion) deliver container-native storage solutions with advanced data services, including snapshots, cloning, replication and disaster recovery.

These platforms integrate directly with Kubernetes through CSI drivers, providing security, compliance capabilities and shared access controls for stateful applications.

Public cloud providers, including AWS, Microsoft Azure, Google Cloud and IBM Cloud, offer managed Kubernetes services with native persistent storage options, such as Amazon Elastic Block Store (EBS) and IBM Cloud Block Storage.

Persistent storage for containers supports the following business use cases:

• Databases and data management
• AI workloads
• DevOps and CI/CD
• Backup and disaster recovery (BDR)