Object versus file versus block storage: What’s the difference?

Each storage system serves different use cases, with distinctive strengths and limitations depending on the application and workload requirements. 

Knowing the differences between these options helps enterprises choose tailored, scalable storage solutions that match today’s needs and fuel long-term growth.

Object-based storage systems divide data into separate, self-contained units stored in a flat environment, with all objects at the same level. There are no folders or subdirectories like those used with file storage.

Moreover, object storage does not store all data together in a single file. Objects also contain metadata, which is information about the file that helps with processing and usability. Users can set the value for fixed-key metadata with object storage, or they can create both the key and value for custom metadata associated with an object.

Instead of using a file name and path to access an object, each object has a unique number. Unlike with file storage, you must use an application programming interface (API) to access and manage objects. Objects can be stored locally on computer hard disks and public cloud servers.

This API-based approach has made object storage ideal for cloud environments, where it has largely replaced traditional tape storage systems due to its scalability, accessibility and management advantages.

While tape storage was historically important for backup and archival purposes, cloud-based object storage now provides more flexible and accessible solutions for long-term data retention. Providers of leading object storage solutions include AWS's Amazon S3, Google Cloud Storage, Microsoft Azure Blob Storage and IBM Cloud® Object Storage.

Check out the following video for a deeper dive into object storage.

• Handles large amounts of unstructured data: Object storage systems efficiently store and manage large volumes of unstructured data, which has become critical for workloads involving artificial intelligence (AI), machine learning (ML) and big data analytics. Today, unstructured data accounts for 90% of all enterprise-generated data.¹ Check out "Structured versus unstructured data: What’s the difference?" for more.
• Affordable consumption model: Object storage provides cost efficiency. Instead of paying in advance for a set amount of storage space, as is familiar with file storage, you purchase only the object storage you need on an on-demand basis.
• Unlimited scalability and high durability: With a consumption-based model, you can add as much extra storage as needed—even petabytes or more—ensuring a scalable solution. At the same time, built-in redundancy and data protection mechanisms provide high durability, safeguarding your data over the long term.
• Rich metadata capabilities: Metadata is stored with the objects so users can quickly gain value from data and more easily retrieve the object they need.
• Advanced searchability: Object storage enables users to search for metadata, object contents and other properties.

• Limited file locking: Object storage lacks traditional file-locking mechanisms, though modern services offer object-level retention and versioning controls that work differently from file system locks.
• Higher latency compared to other storage types: Object storage traditionally has higher latency than block storage for transactional workloads, though modern implementations have improved performance for many use cases.
• Single file portion modification limitations: Once an object is created, you cannot modify it in place. Instead, you create a new version, though modern services offer versioning capabilities to manage updates effectively.

• IoT data management: The ability to quickly scale and easily retrieve data makes object storage a good choice for the rapidly increasing amounts of IoT data being gathered and managed at the edge.
• Email archiving and compliance: Organizations use object storage for long-term email retention, compliance archiving and backup of cloud-based email systems like Microsoft 365 and Google Workspace.
• Backup/disaster recovery: Object storage is often used for backup/disaster recovery, where performance demands are lower.
• Video monitoring: Object storage provides a cost-effective solution for extensive video storage and long-term footage retention.
• AI/ML training data: With built-in scalability and metadata capabilities, object storage is well suited for storing large datasets used in machine learning model training and AI applications.
• Content delivery networks (CDNs): Object storage provides the foundation for content delivery networks (CDNs), which deliver web content, images and media files globally with high availability and performance.
• Real-time analytics: Organizations use object storage to store streaming data and logs that feed into analytics platforms for business intelligence and operational insights.

File storage is a method of storing and organizing data in a hierarchical structure through files and folders (or directories). The application saves data in a single file and determines the file extension type based on the file format (.jpg, .docx, .txt).

When you save a document on a corporate network or your computer’s hard disk, you are using file storage.

Files can also be stored on a network-attached storage (NAS) device. These devices are designed for file storage, making them a faster option than general network servers. Other examples of file storage devices include cloud-based file storage systems, network drives, computer hard disks and flash drives.

The hierarchical structure, with its folders and subfolders, makes it easier to find and manage files. To access a file, the user selects or enters the path for the file, which includes the subdirectories and file name. Most users manage file storage through a simple file system, such as a file manager.

Examples of data typically saved through file storage include presentations, reports, spreadsheets, graphics, photos and other documents. File storage is familiar to most users, allowing them to set data access rights and limits. However, managing large numbers of files and hardware costs can become a challenge.

The following video provides a closer look at both file and block storage.

• Simple navigation: With a small-to-moderate number of files, users can easily locate and access target files through familiar folder structures.

• Intuitive for most users: As the most common storage type for end users, people with basic computer skills can easily navigate file storage with a minimal learning curve.

• Direct user control: Using a simple interface, end users can independently create, move and delete their files without requiring administrative support.

• Granular access controls: Users and administrators can set files to be writable, read-only or locked with password protection for secure file sharing.

• Difficult to scale management: File management becomes increasingly complicated as folder structures grow, leading to slower searches and reduced productivity across the organization.

• Poor fit for unstructured data: While it’s possible to save unstructured data like text, mobile activity, social media posts and IoT sensor data in file storage, it’s not ideal for storing large volumes.

• High costs at scale for on-premises: Storage expansion requires purchasing extra hardware devices when capacity limits are reached, though cloud-based file storage services have primarily addressed scalability limitations.

• Document collaboration: File storage enables teams to work together on shared documents through centralized access. Modern cloud-based file storage systems like Google Drive, Microsoft OneDrive and Dropbox include built-in version control and real-time collaboration features that automatically manage simultaneous edits and preserve document history.

• Backup and recovery: Cloud backup and external backup devices typically use file storage for creating copies of the latest versions of files.

• Archiving: Because of the ability to set permissions at a file level for sensitive data and the simplicity of management, many organizations use file storage for archiving documents for compliance or historical reasons.

Block storage involves splitting data into fixed blocks and then storing it separately with distinctive identifiers. The blocks can be stored in different environments; for example, you might save one block in Windows and the rest in Linux. When a user retrieves a block, the storage system reassembles the blocks into a single unit.

Block storage is the default storage for both hard disk drives and frequently updated data. You can store blocks on storage area networks (SANs), network-attached storage systems (NASs), solid-state drives (SSDs) or in cloud storage environments.

Block storage systems have been a mainstay in the tech industry for decades. While organizations increasingly adopt object storage for large-scale unstructured data and file storage for collaboration needs, block storage remains essential for high-performance applications requiring consistent, low-latency access. The choice between storage types depends on specific workload requirements rather than one approach replacing another.

• Speed: When all blocks are stored locally or close together, block storage has high performance with low latency for data retrieval, making it a common choice for business-critical data.

• Reliability: Because blocks are stored in self-contained units, block storage has a low fail rate and supports data replication for enhanced reliability.

• Easy to modify: Changing a block does not require creating a new block; instead, you can create a new version.

• Lack of metadata: Block storage does not contain metadata, making it less usable for unstructured data storage.

• Limited searchability: Large volumes of block data quickly become unmanageable because of limited search capabilities.

• High cost: Purchasing extra block storage is expensive and often cost-prohibitive at a high scale.

• Databases: Because block storage has high performance and supports seamless update operations, many organizations use it to store data in transactional databases.
• Enterprise applications: Business-critical applications like ERP, CRM and financial systems rely on block storage for consistent performance and data integrity.
• Virtual machine file system (VMFS) volumes: Organizations often use block storage to deploy VMFS storage volumes in virtualized environments. For example, when deploying virtual machines (VMs) across an enterprise, block storage allows you to create and format a volume specifically for VMFS, which is VMware’s high-performance clustered file system. This capability enables multiple physical server nodes (such as ESXi hosts) to access the same VMFS volume concurrently, allowing them to run and manage multiple VMs. Moreover, block storage supports the installation of operating systems (OS) on virtual disks stored in the VMFS volume. Once deployed, VMs can share files and services through the native file-sharing capabilities of their OSes.
• High-performance applications: Mission-critical applications requiring consistent, low-latency storage performance rely on block storage for optimal user experience (UX).
• Container persistent volumes: Modern containerized applications use block storage to maintain data persistence across container lifecycles and within automated container orchestration platforms like Kubernetes.

When determining which type of storage to use for different types of data, consider these factors:

• Cost: Because the costs involved with block and file storage are higher, many organizations choose object storage for high volumes of data. The pay-as-you-consume model of object storage offers significant savings for large-scale data storage. This advantage contrasts with the upfront hardware investments required for block and file storage.
• Management ease: The metadata and searchability make object storage a top choice for high volumes of data. File storage, with its hierarchical organization system, is more appropriate for lower volumes of data.
• Volume: Organizations with high volumes of data often choose object or block storage.
• Retrievability: Data is relatively retrievable from all three types of storage, though file and object storage are typically easier to access.
• Handling of metadata: Object storage typically serves information with extensive metadata better than file storage, which contains only basic metadata.
• Data protection: Stored data must be safeguarded against data breaches and cybersecurity threats. Effective data security measures for storage include through automation for special permissions, encryption, data masking and redacting sensitive files.
• Storage use cases: Each type of storage is most effective for different use cases and workflows. By understanding their specific storage needs, organizations can select the type that fits most of their storage use cases.

The correct storage mix depends on your specific operational needs and data characteristics. Evaluate your data volume, performance requirements, budget constraints and growth projections when planning your storage architecture.

Strategic storage selection guidelines:

1. Object storage functions best for unstructured data, generative AI workloads and AI/ML applications requiring massive scalability at low cost.
2. File storage provides a familiar, hierarchical organization ideal for collaboration, document management and smaller-scale operations.
3. Block storage delivers high performance for databases, virtual machines and applications requiring consistent, low-latency access.

Most organizations implement hybrid approaches, combining storage types strategically. For example, a company might have the following usage:

• Object storage for data lakes and content delivery.

• Block storage for their transactional databases and virtual machines.

• File storage for employee collaboration and document management systems.

Data storage continues to experience tremendous growth. A Fortune Business Insights study projects the global data storage market size to grow from USD 255.29 billion in 2025 to USD 774.00 billion by 2032. This growth reflects a compound annual growth rate (CAGR) of 17.2% during the forecast period.²

The boundaries between storage types are shifting. File and object storage are converging, with unified software platforms that combine hierarchical organization with scalable metadata capabilities. Meanwhile, object storage is gaining intelligence through AI integration, automatically classifying and tiering data based on usage patterns.

Looking ahead, adopting the right mix of storage approaches is expected to be key to building storage architectures that scale efficiently with evolving workloads and data complexity.