What is object storage?

Modern digital communications data is largely unstructured, meaning that it does not conform to (nor can be easily organized into) a traditional relational database with rows and columns. It includes email, videos, photos, web pages, audio files, sensor data and other types of media and web content (textual or nontextual).

All of this content streams continuously from social media, search engines, mobile phones and smart devices. For instance, streaming services like Netflix use object storage to store and deliver their vast libraries of movies and shows to users worldwide, allowing instant access from any device, anywhere.

With object storage, you can store and manage data volumes ranging from terabytes (TBs) to petabytes (PBs) and beyond—including exabyte-scale deployments that power today's largest cloud platforms and data-intensive applications.

Today, enterprises are faced with ongoing challenges related to storing and managing massive volumes of data efficiently and cost-effectively. Object storage provides a robust solution for modern data storage needs as it delivers virtually unlimited scalability compared to traditional file- or block-based storage.

A DataIntelo study estimates the global object storage market at about USD 6.8 billion in 2023. The study also projects it to grow to nearly USD 25 billion by 2032, with a compound annual growth rate (CAGR) of 15.7%.¹ This growth reflects the rising need to handle unstructured data, increased cloud adoption and the growing reliance on big data analytics.

Object storage has evolved significantly since its introduction in the early 2000s. Key milestones include Amazon's launch of S3 in 2006, which established the de facto standard for cloud object storage application programming interfaces (APIs). It is then followed by the emergence of open source solutions like OpenStack Swift in 2010 and the rise of hybrid cloud deployments in the mid-2010s.

Initially developed for web-scale applications, modern object storage has become integral to cloud computing and containerized environments. Today's implementations support advanced features like intelligent data tiering, versioning capabilities and integration with Kubernetes and other platforms that automate container orchestration. Recent innovations include AI-driven data management, where machine learning (ML) algorithms help optimize storage costs and performance, and edge object-storage capabilities that bring data closer to where it's consumed.

Around the same time object storage was gaining traction in cloud-native environments, many organizations began rethinking their reliance on traditional storage architectures.

Historically, enterprises used expensive storage area networks (SANs) to manage to grow volumes of data, often requiring major capital investments in hardware and IT infrastructure. As data demands surged, this approach became increasingly difficult to sustain. Cloud storage services offered a more flexible alternative, allowing organizations to scale capacity up or down as needed.

Rather than maintaining large, in-house storage networks, businesses could now access storage as a service (STaaS)—reducing costs while gaining speed and scalability. All major public cloud service providers, including Amazon Web Services (AWS), Google Cloud, IBM Cloud®, Microsoft Azure, offer object storage capabilities. This shift has evolved further into hybrid multicloud approaches, where organizations strategically combine on-premises storage with multiple cloud providers to optimize performance, cost and compliance requirements.

Cloud storage encompasses various architectures, including file, block and object storage. Each offers different approaches to data management and accessibility. Modern organizations use different storage architectures depending on their specific needs and types of data.

While structured data and transactional workloads often rely on traditional file and block storage, the proliferation of unstructured digital content has made object storage essential for today's data landscape. Understanding these three storage methods helps you choose the right approach for your requirements.

Here’s a breakdown of object versus file versus block storage.

File storage organizes and stores data inside a folder. Files are named, tagged with metadata (typically the file name, file type and when it was created and last updated), and organized in folders under a hierarchy of directories and subdirectories.

You can think of file storage in the same way you store physical paper files in a filing cabinet. There are multiple drawers (directories) and labeled file folders inside each drawer (subdirectories).

To locate a particular file folder in your file cabinet, you pull out the proper drawer and view the folder labels. In the same way, to access the data in a file storage system, your computer system requires only the path (directories and subdirectories) in which to find it.

A hierarchical storage system like this works well with relatively small, easily organized amounts of data. However, as the number of files grows, the search and retrieval process can become cumbersome and time-consuming.

Block storage offers an alternative to file-based storage—one with improved efficiency and performance. Block storage breaks a file into equally sized chunks of data and stores these data blocks separately, under a unique address. You don't need a file-folder structure. Instead, you can store the collection of blocks anywhere in the system for maximum efficiency.

To access a file, a server operating system uses the unique address to pull the blocks back together, assembling them into the file. You gain efficiency as the system does not need to navigate through directories and file hierarchies to access the data blocks. Block storage works well for critical business applications, transactional databases and virtual machines that require low-latency, granular or more detailed access to data and consistent high performance.

Instead of breaking files into blocks or organizing them in hierarchical folders, object storage treats each piece of data as a discrete, addressable unit. Unlike file systems that rely on directory structures or block storage that fragments data, object storage maintains complete data integrity within each storage unit.

Object storage offers cost-effective, massively scalable storage for unstructured data that exceeds the practical limits of block and file solutions. It's ideal for archiving static data, such as compliance records, media libraries and backup data that doesn't require frequent modification.

Objects are discrete units of data stored in a structurally flat data environment typical of object storage systems. Unlike traditional file systems, there are no true folders, directories or complex hierarchies—though folder-like structures can be simulated by using naming conventions.

Each object is a self-contained unit that includes the data itself, associated metadata (descriptive information about the object), and a unique identifier, often called an object key. This unique identifier distinguishes the object within the storage system and might resemble a file path, but it does not represent an actual directory structure.

Repository information enables an application to locate and access the object. You can aggregate object storage devices into larger storage pools and distribute these storage pools across locations. This feature allows for unlimited scale and improved data resiliency and disaster recovery.

Object storage removes the complexity and scalability challenges of a hierarchical file system. Objects can be stored locally in on-premises data centers, on cloud servers or in hybrid and multicloud environments, with accessibility from anywhere in the world. Modern deployments often use container orchestration and distributed infrastructure to manage the underlying systems that power object storage.

Objects—each consisting of data, metadata and a unique identifier—are accessed in an object storage system through APIs. The native API for object storage is typically an HTTP-based RESTful API (also known as a RESTful web service). Most providers also offer software development kits (SDKs) that simplify interaction with these APIs across various programming languages.

These APIs use the object’s unique identifier (or key) to retrieve the object and can also allow querying its metadata. Because the APIs are internet-based, objects can be accessed from anywhere, on any device with network connectivity.

RESTful APIs use HTTP commands like "PUT" or "POST" to upload an object, "GET" to retrieve an object, and "DELETE" to remove it. (HTTP stands for "Hypertext Transfer Protocol" and is the set of rules for transferring text, graphic images, sound, video and other multimedia files on the internet.)

You can store any number of static files on an object storage instance to be called by an API. More RESTful API standards are emerging that go beyond creating, retrieving, updating and deleting objects. These standards allow applications to manage the object storage, its containers, accounts, multitenancy, security, billing and more.

For example, suppose that you want to store all the books in a large library system on a single platform. You need to store the contents of the books (data), but also the associated information like the author, publication date, publisher, subject, copyrights and other details. You might store all this data and metadata in a relational database, organized in folders under a hierarchy of directories and subdirectories.

But with millions of books, the search and retrieval process becomes cumbersome and time-consuming. An object storage system functions well because the data is static or fixed. In this example, the contents of the book are not going to change.

The objects are stored as "packages" in a flat structure and easily located and retrieved with a single API call. Further, as the number of books continues to grow, you can aggregate storage devices into larger storage pools and distribute these storage pools for unlimited scale.

You can use simple API calls to upload and retrieve files in an object storage system, but an application also needs the object's metadata to locate the proper object in storage. Here is where an object storage database comes into play. This database provides a directory of sorts that uses the object's metadata to locate the appropriate data files in a distributed storage system.

Each object storage group has an object storage database that contains two tables:

• Object directory table

• Object storage table

The object directory table contains descriptive information about each object (the metadata). This directory tracks all objects in the storage hierarchy by recording the collection name identifier, the object name and other pertinent information. For example, in common object storage methodologies, the object directory table includes three main indexes:

• The object creation time stamp

• The collection name identifier (name ID) and object creation time stamp

• The object name and collection name identifier

The object storage table contains the data content or the file itself (the objects). The data (fixed digital content such as video and image files or large libraries of documents) sits in the object store. Meanwhile, the metadata (contextual information about the data, including the name ID) resides in a database or object directory table.

When an application "posts" a file, it creates the metadata and stores it in the object directory table within the object storage database, along with "putting" the file to the object storage table. To retrieve the file later, the application queries the object directory or database for the metadata and uses that descriptive, identifying information to locate or "get" the data.

Open source technologies offer flexibility and control over data management and storage options, either as alternatives to, or integrated alongside proprietary solutions from cloud service providers and other vendors.

With open source tools and access to open APIs, you can customize the code to suit your organization's specific requirements while maintaining compatibility with existing proprietary systems. This approach offers the freedom to use existing hardware you might own or mix hardware from different vendors, while benefiting from the broader developer community's contributions.

All major open source object storage solutions adhere to Amazon's Simple Storage Service (Amazon S3) object storage protocol. It was first introduced in 2006, and has since become the de facto standard for cloud storage APIs.

Popular open source solutions include Ceph®, MinIO and OpenStack Swift. While these solutions offer different features, policy options and methodologies, each serves the same goal—enabling large-scale storage of unstructured digital data with S3-compatible RESTful APIs.

Many also offer their own APIs as alternatives to S3. OpenStack Swift, for example, not only supports Amazon's S3 API but also offers its own Swift API with unique capabilities. Ceph Object Storage is S3-compatible but also supports a large subset of the OpenStack Swift API, providing flexibility in how applications interact with the storage system.

• Scalability: Unlimited scale is perhaps the most significant advantage of object-based data storage. Objects, or discrete units of data (in any quantity), are stored in a structurally flat data environment within a storage device, such as a server. You can simply add more devices or servers in parallel to an object storage cluster for extra processing and to support the higher throughputs required by large files like videos or images.

• Reduced complexity: Object storage removes the complexity that comes with a hierarchical file system with folders and directories. There is less potential for performance delay and more efficiency when retrieving data since there are no folders, directories or complex hierarchies to navigate. This capability improves performance, particularly when managing large quantities of data.

• High availability and durability: Object storage systems can be configured to replicate data across multiple nodes or clusters. If a disk or node fails, the system can continue operating without data loss due to this redundancy. Data replication can occur within the same data center or across geographically distributed locations, ensuring both high availability and off-site disaster recovery.

• Searchability: Each object is a self-contained repository that includes metadata or descriptive information associated with it. This metadata enhances searchability by making it easier to locate and retrieve objects based on specific attributes or custom tags. Aside from supporting data lifecycle management and data protection strategies, the metadata can be customized to add context—enabling advanced search, filtering and analytics for business insights surrounding market trends and more.

• Cost efficiency: Object storage service providers typically offer pay-as-you-go pricing that eliminates upfront capital investment. Costs are based on actual usage—storage volume, data retrieval, bandwidth and API requests. Pricing is tiered or volume-based, with different storage classes and storage tiers, designed to lower costs for infrequently accessed data or large volumes. Many object storage solutions can run on standard, vendor-neutral hardware, reducing the need for new or proprietary infrastructure. This flexibility allows organizations to repurpose existing servers and scale affordably.

• Security: Object storage provides comprehensive security features (for example, encryption both at rest and in transit) and robust access controls through IAM policies. Many solutions also offer multifactor authentication, data loss prevention (DLP) capabilities and integration with enterprise security tools for centralized monitoring and threat detection.

• Cloud compatibility: Object storage is closely linked with cloud or hosted environments that deliver multitenant storage as a service. This allows many companies or departments within a company to share the same storage repository, with each having access to a separate portion of the storage space. This shared storage approach inherently optimizes scale and costs. You can reduce your organization's onsite IT infrastructure by using low-cost cloud storage while keeping your data accessible when needed. Your enterprise, for example, can use a cloud-based object storage solution to collect and store large amounts of unstructured Internet of Things (IoT) and mobile data for your smart device apps.