Best Object Storage Products: Top 5 Hardware Solutions in 2026

What Is Object Storage?

Object storage manages and stores data as objects, rather than files in a hierarchical file system or blocks within sectors. Each object includes the data itself, metadata, and a unique identifier, making it suitable for handling unstructured data such as images, videos, backups, and large datasets. Unlike traditional storage, object storage is highly scalable, supports distributed environments, and provides efficient access to large volumes of information through HTTP-based APIs.

This design separates it from file and block storage both in architecture and use cases. Object storage platforms are valued for durability, flexibility, and compatibility with cloud-native applications. They allow for limitless storage growth, simplified data management, and accessibility from anywhere via standard protocols. Demand continues to climb: industry analyses value the global object storage market in the single-digit billions of dollars, with forecasts projecting sustained double-digit annual growth through the early 2030s as unstructured data volumes surge.

In this article:

• Why Do Organizations Deploy Object Storage Products On-Premises?
• Common Use Cases of Object Storage Products
• Object Storage as the Data Layer for AI
• Notable Object Storage Products
• How to Choose the Right Object Storage Product

As unstructured data volumes climb and AI adoption accelerates, object storage has moved from a back-office archive to a strategic foundation for modern data infrastructure. Newer platforms emphasize S3-compatible access, high-throughput performance for AI pipelines, and hybrid deployment across on-premises and cloud environments.

Why Do Organizations Deploy Object Storage Products On-Premises?

Organizations deploy object storage on-premises to meet specific regulatory, performance, and integration needs that public cloud storage may not fully address. Increasingly, these deployments are hybrid by design, keeping hot and sensitive data on-premises while tiering warm and cold data to S3-compatible cloud archives for cost efficiency.

• Data sovereignty and compliance are major drivers. Enterprises in regulated industries—such as healthcare, finance, or government—must store data within specific geographic boundaries or comply with strict security standards. On-premises object storage allows full control over data residency and facilitates adherence to these regulations.
• Performance optimization is another factor. Applications that demand low-latency access to large datasets—like AI/ML pipelines, media workflows, or analytics—benefit from local deployment. Keeping storage close to compute infrastructure reduces network bottlenecks and improves throughput.
• Cost predictability also influences the decision. While cloud storage has flexible pricing, egress fees and variable API charges can become unpredictable. On-prem solutions offer fixed costs, making budgeting easier for large-scale or high-frequency workloads.
• Integration with existing systems is often simpler on-premises. Organizations can leverage current infrastructure—such as networking, identity management, or monitoring tools—without the need for reconfiguration or cloud-specific adaptation.
• Long-term storage control also matters. With on-prem object storage, organizations can design custom retention policies, avoid vendor lock-in, and ensure long-term accessibility regardless of changes in public cloud services or pricing models.

Common Use Cases of Object Storage Products

Object storage systems are optimized for managing large volumes of unstructured data, making them suitable for a wide range of modern IT and business scenarios. The rapid expansion of AI workloads has accelerated this shift, as training and inference pipelines generate and consume unstructured data at petabyte and exabyte scale. Here are common use cases where object storage offers clear advantages:

• Backup and archival: Long-term storage for backups, compliance data, and cold archives with high durability and low cost.
• AI Workflows: Most AI tools are written to support the S3 protocol, and the majority of enterprise data is now in the S3 format, making the S3 API a logical fit for AI deployments.
• Media storage and delivery: Hosting and distributing images, videos, and audio content, often integrated with CDNs for global access.
• Big data and analytics: Storing unstructured data in data lakes for processing with analytics tools and machine learning platforms.
• Cloud-native application storage: Serving static content, logs, and configuration files for containerized and serverless applications.
• Disaster recovery: Replicating data across locations to support business continuity and rapid recovery during outages.

IoT and sensor data storage: Ingesting and retaining time-series data from devices and sensors for analysis and real-time processing.

Object Storage as the Data Layer for AI

Object storage has become the default data layer for large-scale AI, because it decouples compute from storage and lets teams grow GPU clusters and capacity independently while sharing a single, S3-compatible repository. Its flat namespace and metadata model handle the mix of images, video, logs, model checkpoints, and training corpora that AI pipelines depend on, avoiding the metadata bottlenecks that stall traditional file systems.

A major recent advance is direct GPU-to-object data movement. Using RDMA over the standard S3 API, new integrations stream object data straight into GPU memory while bypassing the host CPU, preserving the universal S3 interface that AI tools already rely on. This keeps GPUs compute-bound rather than waiting on data, reducing CPU overhead in the data path and improving throughput for both training and inference at scale.

The practical takeaway is that object storage is no longer just a cost-effective archive tier. For AI-ready deployments, evaluate platforms on high-throughput parallel access, native S3 support, and emerging RDMA-accelerated data paths alongside traditional durability and scale.

Notable Object Storage Products

1. Cloudian

Cloudian HyperStore Object Storage Software is an enterprise-grade, software-defined storage platform designed for managing massive volumes of unstructured data at scale. Built for AI-ready performance and secure, hybrid-cloud integration, HyperStore empowers organizations to maintain full data sovereignty while delivering high throughput and bulletproof ransomware protection.

With seamless S3 API compatibility and unified file-object storage, it supports capacity-intensive workflows—like AI/ML, data analytics, and backup/archival—while offering flexible deployment options on commodity hardware or Cloudian appliances.

Key features include:

• Massive scale: Modular architecture enables non-disruptive, limitless expansion across one or multiple sites, managed as a unified system.
• AI-ready performance: High-throughput, low-latency performance with direct S3 access, NVIDIA GPUDirect support, and flash-optimized configurations.
• S3 compatibility: Delivers the industry’s highest S3 API compatibility, fully supporting AWS S3 features and SDK for hybrid/multi-cloud environments.
• Unified file and object storage: Combines object and file data management into a single system to streamline operations and reduce TCO.
• Strong Security: Offers encryption at rest/in transit, IAM, SAML, MFA, Object Lock, and the most complete set of data security certifications.
• Hardware flexibility: Deploy on your hardware of choice or use Cloudian appliances; cut costs by up to 70% vs proprietary storage platforms.

2. Ceph

Ceph is a unified, open-source storage platform that provides object, block, and file storage in a distributed system. It is intended for high performance, scalability, and fault tolerance, making it suitable for cloud infrastructure and data-intensive workloads. Ceph uses the CRUSH algorithm to distribute across the cluster, ensuring performance without relying on traditional RAID.

Key features include:

• Unified storage: Offers object, block, and file access in one system, reducing complexity and overhead.
• Scalability: Scales horizontally to exabytes with no single point of failure.
• CRUSH algorithm: Distributes data evenly without central metadata servers.
• Self-healing and self-managing: Automatically detects, recovers from, and adapts to hardware failures.
• S3 and Swift support: Provides compatibility with common object storage APIs for cloud integration.

3. Scality RING

Scality RING is a software-defined object storage solution built for petabyte-scale environments. It supports unstructured data workloads across a range of use cases including backup, media archives, and cloud-native applications. RING delivers high throughput and resiliency by distributing data across a scale-out cluster using erasure coding. Designed for hybrid and multi-cloud deployments, it integrates with public cloud services and supports S3 and NFS protocols.

Key features include:

• Scalability: Supports large numbers of objects and high capacity in one namespace.
• Multi-protocol support: Access data via S3, NFS, SMB, or POSIX connectors.
• Hybrid cloud integration: Enables data tiering and replication across on-prem and cloud platforms.
• High durability and availability: Uses erasure coding and geo-distribution to protect data.
• Enterprise-grade security: Includes role-based access, encryption, and compliance certifications.

4. Quantum ActiveScale

Quantum ActiveScale is a scale-out object storage system for managing large unstructured datasets in on-prem and hybrid cloud environments. It targets data archiving, research, media, and analytics workflows by providing long-term durability, performance, and integration with S3-compatible applications.

Key features include:

• Scalability: Grows from terabytes to exabytes using scale-out architecture.
• S3 compatibility: Provides native support for S3 applications and tools.
• Hybrid cloud support: Enables replication and tiering between on-prem and cloud.
• Data durability: Uses erasure coding and bit integrity checking for long-term reliability.
• Lifecycle policies: Automates data retention, migration, and deletion.

5. Dell EMC ECS

Dell EMC Elastic Cloud Storage (ECS) is an object storage platform built for modern workloads, offering multi-protocol access, strong consistency, and geo-replication. ECS supports cloud-native applications, backup, and compliance archiving, and is available as software-defined or appliance-based deployments.

Key features include:

• Multi-protocol access: Supports S3, OpenStack Swift, NFS, and HDFS.
• Geo-distribution: Enables global data access with active-active replication.
• Scalability: Expands to exabyte-scale across geographically dispersed sites.
• Data protection: Includes erasure coding, WORM support, and compliance features.
• API integration: Offers S3 compatibility and SDK support for cloud applications.

How to Choose the Right Object Storage Product

Selecting the right object storage solution depends on technical requirements, budget constraints, and workload types. Enterprise storage vendors are increasingly converging on S3 as the universal object API, so broad S3 compatibility has become a baseline requirement for interoperability across backup, analytics, and AI tools. Here are key factors to consider when evaluating object storage products:

• Scalability and performance: Evaluate how well the platform scales with increasing data volumes and whether it supports high-throughput or low-latency workloads required by applications.
• API and protocol support: Ensure compatibility with standard APIs like S3 and protocols such as NFS or SMB, especially if integration with existing tools, workflows, or cloud environments is necessary.
• Deployment flexibility: Consider whether the solution supports various deployment models—on-premises, in the cloud, or hybrid—and if it can run on preferred hardware or infrastructure.
• Durability and data protection: Look for features like erasure coding, replication, versioning, and object locking to ensure data integrity, availability, and protection against corruption or deletion.
• Security and compliance: Assess support for encryption (in transit and at rest), access controls, audit logging, and compliance certifications relevant to the industry.
• Management and automation: Evaluate the ease of use for monitoring, provisioning, and automating storage lifecycle policies, especially for large-scale environments.
• Cost efficiency: Factor in licensing models, total cost of ownership, hardware requirements, and operational costs, including power, cooling, and support.
• Vendor ecosystem and support: Consider the availability of integrations with backup, archiving, or analytics tools, as well as the quality of vendor documentation, community support, and professional services.

Conclusion

Object storage delivers a scalable, durable, and flexible foundation for managing unstructured data across a wide variety of use cases. Its architectural advantages and compatibility with modern cloud environments make it a core component of modern data infrastructure, supporting growth, simplifying management, and enabling seamless access regardless of scale or location.