Top 5 Use Cases of Object Storage in 2026

Key Takeaways

Data is now bigger and more unpredictable than ever. AI models are consuming petabytes of training data and media platforms are streaming millions of files every second. And guess what? Traditional storage systems cannot keep up. This is why teams are switching to object storage to handle their data. In this blog, we explore the most impactful object storage use cases, why businesses are adopting it and how organisations can use object storage to power data-heavy workloads.

What is Object Storage? 

Object storage is a data storage architecture designed to handle massive volumes of unstructured data. Compared with SSV (Shared Storage Volumes), object storage supports multi-read and multi-write operations. This allows multiple clients to access or update the same object concurrently.

Object storage is typically accessed over HTTP-based APIs and is built to scale across distributed environments. Because of its flexibility and cost efficiency, object storage has become the preferred choice for cloud-native applications, analytics platforms and data-intensive workloads.

Why Choose Object Storage?

Organisations adopt object storage because it solves problems that traditional file and block storage struggle with at scale. See below:

• Scalability: Object storage scales without performance degradation, making it ideal for growing datasets and unpredictable workloads.
• High Durability: Data is replicated across multiple nodes or locations for extremely high durability and fault tolerance.
• Rich Metadata Support: Custom metadata allows advanced search, classification and lifecycle management of stored objects.
• API-Based Access: Simple REST APIs make object storage easy to integrate with cloud-native apps and services.
• Built for Unstructured Data: Perfect for images, videos, logs, backups, AI datasets and other unstructured data types.

Object Storage vs Shared Storage Volumes (SSVs)

Traditional storage systems like Shared Storage Volumes (SSVs) organise data in files and folders. While this works well for structured data and predictable workloads, it can struggle when data grows or needs to be accessed by many clients at once.

While Object storage treats each piece of data as an independent object with its own metadata. This flat structure makes it easy to scale, retrieve and manage massive volumes of unstructured data without worrying about running out of space or slowing down performance.

To give an idea, SSVs are great for structured and file-based workloads. And you can use object storage for unpredictable, data-heavy workloads of modern applications, analytics and AI.

Top 5 Use Cases of Object Storage

Check out the top 5 object storage use cases that you can implement:

1: AI/ML Datasets 

AI/ML datasets are not really static. Data is continuously ingested, updated, labeled and reprocessed as models grow. Traditional file systems struggle with this dynamic growth and often become bottlenecks due to limited scalability and rigid structures. Object storage with its flat namespace and virtually unlimited scale is designed to handle these demands effortlessly.

Each data file is stored as an object with associated metadata. This metadata can include labels, timestamps, data sources or version information. These are critical elements for training, validation and compliance. By decoupling storage from compute, object storage allows AI teams to scale data independently of GPU or CPU resources.

How Object Storage is Used for AI/ML

Object storage integrates seamlessly with popular AI frameworks such as TensorFlow, PyTorch and Apache Spark through S3-compatible APIs. This allows models to stream data directly from storage during training. This eliminates the need for costly data duplication. In distributed training environments, multiple GPU instances can access the same dataset concurrently without performance degradation.

Versioning is another critical advantage. Object storage enables dataset version control see so teams can track changes, reproduce experiments and roll back to earlier data states when required. Lifecycle policies further automate the movement of rarely accessed datasets to lower-cost tiers, optimising storage costs.

2: Backup and Recovery

Backup and recovery involves creating secure copies of data so it can be restored quickly in case of accidental deletion, hardware failure, ransomware attacks or natural disasters. Traditional backup systems often rely on tape or block storage which can be slow to scale, expensive to maintain and difficult to manage. Object storage overcomes these limitations by offering high durability and elastic capacity.

How Object Storage is Used for Backup and Recovery

Organisations use object storage as a centralised backup repository for databases, virtual machines, containers and application data. Backup software can push data directly into object storage using REST or S3-compatible APIs, making integration simple across diverse environments.

Incremental and differential backups work particularly well with object storage. Since objects are independently stored, only changed data needs to be backed up, reducing storage consumption and speeding up backup operations. Metadata attached to each object helps track backup versions, timestamps and retention policies.

For recovery, object storage helps with rapid restores by allowing parallel access to multiple objects. This reduces recovery time objectives (RTOs) compared to traditional tape-based systems. 

3: Media Storage and Delivery

Media files are large, unstructured and growing in volume and resolution. High-definition and 4K/8K video formats significantly increase storage demands, while global audiences expect fast and reliable content delivery. Traditional file systems struggle to scale and distribute media assets efficiently across regions.

How Object Storage is Used for Media Storage and Delivery

Organisations use object storage as a centralised media repository where raw, edited and final assets are stored in a single system. Metadata such as file type, resolution, creation date, language and usage rights can be attached to each object. This makes it easy to organise, search and retrieve content.

For content delivery, object storage integrates seamlessly with content delivery networks (CDNs). Media files are served directly from object storage, reducing latency and improving user experience. This is valuable for video-on-demand platforms, live streaming services and global marketing campaigns.

Object storage also supports versioning for teams to manage multiple versions of media files without overwriting originals. Lifecycle policies can automatically move older or less frequently accessed content to lower-cost storage tiers.

4: Long-Term Archives

Long-term archives include data that is rarely accessed but must be preserved in its original form. This can include financial records, healthcare data, legal documents, logs, research data and historical media files. Traditional archival methods such as tape storage are slow to retrieve from, operationally complex and difficult to scale.

How Object Storage is Used for Long-Term Archiving

Organisations store archived data in object storage using lifecycle management policies. These policies automatically move data from active storage tiers to cold or archive tiers based on access frequency or age. This automation reduces manual intervention and lowers storage costs over time.

Metadata plays a crucial role in long-term archives. Each object can include information such as retention period, compliance tags, ownership and classification. This makes it easier to locate specific data years later without scanning entire datasets.

Object storage also supports encryption, access controls and audit logging, which are essential for meeting regulatory requirements. In multi-region setups, archived data can be replicated across locations to protect against regional failures or disasters.

5: Big Data and Analytics

Big data workloads involve storing and processing vast amounts of structured and unstructured data. This data is often read-heavy, processed in batches or queried by multiple analytics tools simultaneously. Traditional storage systems struggle to handle this scale without becoming expensive or complex to manage.

How Object Storage is Used for Big Data & Analytics

Organisations use object storage as the backbone of their analytics pipelines. Data is ingested from multiple sources and stored as objects, where metadata helps classify and organise datasets by time, source or type.

Object storage enables high-throughput and parallel data access for multiple analytics jobs to run concurrently without data duplication. This improves performance and reduces infrastructure costs. 

Conclusion 

Data volumes continue to grow fast in 2026. Hence, choosing the right storage architecture is critical for performance, scalability and cost control. From powering AI and analytics pipelines to securing backups, media assets and long-term archives, the use cases of object storage span nearly every modern workload. Its ability to scale without limits, support unstructured data and integrate with cloud-native tools makes object storage an important component of today’s data infrastructure.

Manage Your Data with Hyperstack Object Storage

Get reliable, cost-efficient and scalable storage built for modern data-heavy workloads. Hyperstack Object Storage is designed to handle everything from AI/ML datasets and analytics pipelines to backups, archives and media delivery. With high durability, S3-compatible access and seamless scalability, you can store and manage your data without worrying about growth or performance limits.

Start storing your datasets, backups and media files with ease while keeping costs under control.

Try Object Storage Now 

FAQs

Which are common use cases for object storage?

Common object storage use cases include AI and ML datasets, backup and recovery, media storage and delivery, long-term data archiving and big data analytics. These workloads benefit from object storage’s scalability, durability and cost efficiency.

When should you use object storage?

You should use object storage when dealing with large volumes of unstructured data, unpredictable growth or distributed access needs. It is ideal when scalability, durability and API-based access matter more than low-latency file operations.

How is object storage different from file and block storage?

Object storage stores data as objects with metadata and unique IDs, unlike file or block storage. This flat architecture enables better scalability and durability, making object storage suitable for modern cloud-native workloads.

Is object storage suitable for AI and machine learning workloads?

Yes, object storage is widely used for AI/ML workloads. It supports massive datasets, parallel access and integration with popular frameworks, making it ideal for training, testing and retraining machine learning models.

Is object storage cost-effective for long-term data retention?

Yes, object storage is highly cost-effective for long-term retention. Lifecycle policies and tiered pricing allow organisations to store infrequently accessed data at lower costs while maintaining accessibility and compliance.

How does object storage support big data and analytics?

Object storage acts as a scalable data lake for analytics workloads. It enables parallel data access, integrates with analytics engines and allows storage to scale independently from compute resources.