What is edge storage?

In this approach, data resides in distributed edge locations, including regional data centers, onsite servers and other IT infrastructure positioned close to users and data sources. Processing happens locally, reducing the need for data to make constant roundtrips to a central facility. The result is faster response times, lower bandwidth consumption and greater resilience.

For some industries, local processing is crucial. In healthcare, for example, wearable devices can track patients’ vital signs and alert care providers to changes that require immediate attention. In the energy sector, sensors on a power grid can detect equipment issues and flag failures before an outage occurs.

Businesses increasingly rely on enterprise edge storage systems to manage large volumes of data at scale, with an emphasis on high performance, scalability and reliability. As artificial intelligence (AI) adoption grows and more devices connect to networks, edge storage has become an important part of how organizations handle and act on data in real time.

Progress in high-speed connectivity, especially 5G, combined with advances in cloud computing, AI and machine learning (ML) has changed the storage infrastructure landscape significantly, increasing the role of edge storage.

In a study from Straits Research, the global edge computing market was valued at USD 38.32 billion in 2024 and is projected to grow from USD 55.44 billion in 2025 to USD 1,065.63 billion by 2033, at a compound annual growth rate (CAGR) of 44.7%.¹

Driving much of this growth is data volume. According to IDC, global data generated is set to reach 393.9 ZB by 2028, fueled by generative AI (gen AI) and other technologies.² More and more of that data will originate and remain at the edge, rather than traveling to cloud or on-premises data centers.

Reasons for keeping data at the edge range from data gravity to data sovereignty. Data gravity refers to the tendency of data to attract applications and services that are costly to move, while data sovereignty describes regulations that require data to stay within specific geographic boundaries.

Security is another factor. According to the IBM Cost of a Data Breach Report 2025, breaches involving data stored across multiple environments cost USD 5.05 million on average and took 276 days to identify and contain. Storing data locally at the edge, rather than across multiple environments, gives organizations fewer places to monitor and protect.

Industries like finance that depend on real-time data processing cannot afford the delays that come with routing data to a distant cloud. Edge storage addresses this challenge by processing and storing data onsite, at the point where it is collected.

AI storage needs are accelerating the need for edge storage further. As AI inference moves from centralized data centers to distributed devices and facilities, the demand for fast local storage grows with it. This drives greater use of high-performance storage media, such as NVMe SSDs, to handle AI workloads without relying on cloud connectivity.

Edge storage has evolved over the decades, driven by advances in networking, computing and a shift toward processing data closer to where it is generated.

The roots go back to the late 1990s, when content delivery networks (CDNs) were built to serve web pages and video from servers placed close to users rather than from a single distant origin. Shorter distance meant faster delivery. As those networks matured and began hosting applications alongside content, what edge infrastructure could handle grew considerably.

By the early 2000s, commercial edge computing services were handling practical, everyday tasks, such as online shopping carts and news feeds that needed to refresh in real time.

Since then, edge settings have grown in scale and complexity as IT infrastructure has spread across more locations and device types. Today’s edge architecture includes edge servers, edge gateways, high-speed networking and local storage media—collecting, processing and routing data close to where it originates.

Modern edge deployments increasingly incorporate containers and Kubernetes, which help organizations manage workloads consistently across edge and central cloud settings, whether they span a shipping network, energy grid or a retail chain.

As organizations manage more data across more locations, keeping storage close to the source has become a practical part of building and managing distributed IT infrastructure.

Edge, cloud and on-premises environments each play an important role in modern data storage and IT infrastructure. Increasingly, organizations use a combination of these technologies.

• Cloud storage is a cloud computing service model in which data and files are stored offsite by a third-party provider and accessed through the internet or a dedicated private network connection. Cloud storage can be public, private or hybrid, and it works well for long-term retention, large-scale analytics and data that needs to be accessible from anywhere.
• On-premises storage keeps data within an organization’s own facilities on hardware that it owns and manages. It offers maximum control and predictable performance for workloads with strict security or compliance requirements, such as those in government or the ones that run better without routing data across a network.
• Edge storage sits closest to where data is generated and used. Rather than replacing cloud or on-premises storage, it complements them. Edge storage handles real-time processing and local data retrieval, sending only what requires long-term storage or deeper analysis back to the cloud or central data center.

Most large organizations now use all three, structuring their data infrastructure around which storage solution best meets their business needs.

Edge storage follows a distributed computing model. Rather than keeping all data in one central location, it stores and processes data across multiple physical nodes. Edge nodes cache data locally, pulling from an origin server when needed and serving requests from nearby users or devices without routing traffic back to a distant data center.

Here’s a breakdown of several key edge storage components:

• Edge servers: Edge servers store and process data locally where it originates. Built to run in distributed locations, often without dedicated IT staff onsite, they need to be compact and resilient. Clusters of edge servers can handle higher data volumes or provide backup at a single site if one goes down.
• NVMe and SSD storage media: PCI-based SSDs that use non-volatile memory express (NVMe) handle data-intensive tasks like video processing and high-performance computing (HPC) workloads at speeds far faster than traditional hard disks. High-capacity SSD media support use cases like video surveillance at edge sites, where footage needs to be stored and accessed locally without a constant connection to a central system.
• Object storage: Object storage handles large volumes of unstructured data, such as video files and sensor images, and scales without the overhead of a traditional file storage system. Many organizations use it at the edge to hold data locally before sending it to cloud or on-premises systems for longer-term retention.
• AI inference hardware: Graphics processing units (GPUs) and neural processing units (NPUs) run AI models directly on edge devices, drawing on locally stored data rather than sending it to a central system. A security camera can analyze footage onsite or a factory sensor can detect an anomaly without a round trip to the cloud.
• Data management and tiering software: The software layer controls what data stays at the edge, what moves to central storage and what the system deletes. Enterprise edge storage solutions (for example, IBM FlashSystem, AWS Outposts, Dell EMC) automate storage management and monitoring workflows across distributed deployments, applying rules organizations define in advance. In addition, open application programming interfaces (APIs) allow applications to access and manage data across all edge locations.
• Network and gateway infrastructure: Reliable connectivity and edge gateways control how quickly data moves between edge nodes and central systems. They also allow data to securely cross network boundaries. Telecom companies provide 5G coverage, which increases what edge deployments can handle and supports a higher amount of data and lower latency across mobile and distributed locations.
• Central data center or cloud: A central data center or cloud stores the primary copy of data and picks up what edge nodes forward after local processing. It handles long-term retention and analytics that run better centrally.

Edge storage offers a range of benefits for organizations managing data across distributed locations:

• Low latency: Storing and processing data close to the source speeds response times and improves user experiences, which is crucial for time-sensitive apps, such as those in autonomous vehicles and real-time video analytics.
• Lower bandwidth costs: Edge storage reduces how much data crosses the network by handling processing locally. Organizations send only what needs to reach the cloud, which reduces cloud pricing and cuts egress and data storage costs.
• Resilience and offline operations: Edge nodes run independently, so a central outage or dropped connection doesn’t interfere with operations. In fully disconnected or air-gapped settings, such as remote industrial or military sites, edge storage can function without any link to a central system.
• Storage flexibility: Network attached storage (NAS), storage area network (SAN) and object storage all work at the edge and scale to meet changing storage needs, from a single remote site to hundreds of locations. Organizations can pick the storage type that fits the workload, such as object storage for unstructured data or SAN for high-performance applications.
• AI at the edge: Edge AI models on distributed devices need fast local storage to run. Without it, AI inference waits on a cloud connection. Edge storage also handles data tiering and lifecycle management, keeping the right data onsite and moving older or lower-priority data to cheaper central storage automatically.
• Security: Keeping data local means it stays off shared infrastructure and doesn’t travel across public or third-party networks, which reduces exposure. Organizations in regulated industries also find it easier to enforce access policies consistently when data doesn’t leave the facility.
• Data sovereignty: Many industries operate under data sovereignty regulations that restrict where data can go. Edge storage gives organizations direct control over where data lives and who can access it, without routing it through the cloud.

Many industries generate high volumes of data in locations where sending everything to a central facility isn’t fast enough, practical or cost-effective. Edge storage keeps that data where it is needed most.

Edge storage introduces management challenges that don’t exist in a centralized data center. Hardware runs in remote or sometimes unstaffed locations and security is harder to enforce consistently. Here are a few practices that can help:

• Define data lifecycle policies upfront: Decide what data stays at the edge, what moves to central storage and how long each type is retained. Without clear policies, organizations often find they don’t have enough storage capacity at edge sites or are holding data longer than necessary.
• Centralize monitoring and updates: Remote sites often lack dedicated IT staff. Managing software updates, storage performance and security policies from a central platform keeps distributed deployments manageable as the number of edge locations grows.
• Build in redundancy: Edge storage hardware often runs in locations without climate control or physical security. Deploying clusters rather than single servers at critical sites means that one failure doesn’t take down local storage.
• Apply consistent security policies across all locations: Each edge node is a potential entry point into the network. Access controls and security policies need to cover every site to protect data wherever it is stored, not just central systems.