88 If you’re evaluating enterprise object storage, there’s a good chance both Scality and Cloudian are on your shortlist. We both deliver S3-compatible, software-defined object storage for petabyte-scale environments — but we took different paths to get here, and those architectural decisions have real consequences at scale. Here’s an honest look at how the two platforms compare. The Object Storage Landscape in 2026 Unstructured data is growing at 25-30% year over year across most industries. Logs, media assets, backups, IoT telemetry, AI training sets — none of it fits neatly into traditional SAN and NAS architectures. Object storage replaces rigid file hierarchies with flat namespaces, metadata-rich objects, and HTTP-based access through the S3 API. It scales horizontally without the management ceilings that legacy systems hit. The question for most organizations isn’t whether to adopt object storage — it’s which platform can handle where you’re going, not just where you are today. Who We Are We founded Scality in 2009 with a focus on solving storage at massive scale. Today we offer two products. RING is our distributed storage platform supporting both file and object protocols, built for large-scale, mission-critical environments. ARTESCA is our Kubernetes-native object storage solution designed for cloud-native workloads and modern infrastructure teams. Together, they cover the full spectrum — from massive multi-site deployments to lean containerized clusters. Cloudian, founded in 2011 and based in San Mateo, California, offers HyperStore, an S3-compatible object storage platform. They’ve focused heavily on S3 API compatibility and hybrid cloud use cases, particularly data tiering to AWS. They also offer HyperFile for file-based access. Architecture: Where the Foundations Diverge Architecture determines everything downstream — scalability ceilings, performance under pressure, and how gracefully a platform handles growth. RING is built on our patented distributed key-value store. We designed this architecture from scratch specifically for storage workloads, rather than adapting an existing database framework. It supports both object and file access through multiple connectors (S3, NFS, FUSE), giving you a single platform for mixed workloads. Critically, RING’s architecture allows fine-grained data placement policies — you get precise control over where data physically resides, down to the site, rack, and drive level. ARTESCA extends our storage expertise into Kubernetes-native environments. It runs as containers, deploys via Helm charts, and integrates into cloud-native CI/CD pipelines without additional abstraction layers. If your team has standardized on Kubernetes, ARTESCA speaks that operational language natively. Cloudian HyperStore is built on a modified Apache Cassandra distributed database. Cassandra is proven technology for distributed systems, but it was originally designed for database workloads, not storage. That means HyperStore inherits both Cassandra’s strengths — such as tunable consistency — and its constraints, particularly around metadata handling at extreme object counts. The practical implication: our purpose-built architecture gives us a structural advantage when deployments push into the hundreds of petabytes or billions of objects, where metadata management becomes one of the hardest engineering problems in storage. Scaling: Ceiling Matters More Than Starting Point Most storage platforms perform well in lab conditions with a handful of nodes. The real test is what happens at scale — when the cluster grows to hundreds of nodes across multiple geographies and manages billions of objects over years of production use. We built RING for exactly this scenario. The architecture scales without introducing metadata bottlenecks, and our geo-distribution capabilities let you build storage fabrics that span data centers and continents while maintaining full control over data placement. Major telcos, media conglomerates, and research institutions run RING at scales that push the boundaries of what software-defined storage can do. Cloudian HyperStore scales from three-node clusters to multi-petabyte environments. It handles mid-range deployments well, but organizations planning for sustained growth into very large object counts or globally distributed architectures should evaluate carefully how the underlying Cassandra-based metadata layer performs under that sustained load. S3 API Compatibility Both platforms support the S3 API — but “S3 compatible” is a broad claim, and marketing materials don’t always tell the full story. Cloudian has built much of their positioning around S3 API breadth, often citing the number of S3 operations they support. It’s worth noting that counting supported API operations is a marketing metric, not an engineering one. What matters in production is whether the operations your applications depend on work correctly under load, at scale, and across failure scenarios. We provide comprehensive S3 API support across both RING and ARTESCA — including S3 Object Lock, versioning, multi-part upload, and the operations that enterprise applications, backup tools, and data pipelines rely on daily. Our customers run Veeam, Commvault, Veritas, and hundreds of S3-native applications without compatibility issues. Where we go further is in what surrounds the API: the ability to combine S3 access with file protocols on the same data, the depth of our data placement controls, and the performance characteristics at scale that the API sits on top of. An API is an interface. What the storage engine does beneath that interface — how it places data, protects it, and performs at billions of objects — is where the long-term differences emerge. Cloud-Native and Hybrid Capabilities The relationship between on-premises storage and public cloud continues to evolve. Both companies offer hybrid integration, but through different lenses. ARTESCA was purpose-built for cloud-native infrastructure. Running natively on Kubernetes means it integrates into container orchestration, GitOps workflows, and modern deployment pipelines without bolt-on adapters. If you’ve standardized on Kubernetes — and that group is growing rapidly — ARTESCA eliminates the operational disconnect between how you manage applications and how you manage storage. RING supports cloud tiering to public cloud targets, but our primary value proposition is more ambitious: keeping data on-premises at scale while delivering the economics and accessibility that you might otherwise go to public cloud for. Cloudian has focused their hybrid story on AWS integration, offering automated tiering between on-premises HyperStore and S3 or Glacier. That’s useful if you’re operating within the AWS ecosystem, though it does create a degree of architectural dependency on a single public cloud provider. Data Protection and Compliance Both platforms support erasure coding, replication, and S3 Object Lock for immutable storage. The differences come down to flexibility and control. RING offers highly granular data protection policies. You can define different erasure coding schemes per storage class and create geo-distribution rules that dictate exactly which sites hold which data fragments. For regulated industries — financial services, healthcare, government, defense — this level of data placement control is often a hard requirement, not a nice-to-have. Data sovereignty regulations increasingly demand proof of where data physically resides, and our policy engine was built to answer that question definitively. Cloudian HyperStore supports configurable erasure coding and replication applied at the bucket level, with cross-site replication for multi-site deployments. They provide compliance features including Object Lock, but the data placement controls are less granular than what RING offers. Performance and AI-Ready Infrastructure Benchmarks depend on hardware, network topology, object sizes, and workload patterns — so take any single-metric comparison with caution. That said, architectural differences create predictable tendencies. RING’s distributed key-value architecture is optimized for sustained, high-bandwidth throughput with very large object counts. This matters most in media and entertainment (ingesting and serving massive video files), scientific computing (managing research datasets), large-scale data protection (handling backup streams from thousands of sources simultaneously), and increasingly, AI/ML pipelines that need to feed training data to GPU clusters at speed. With RING8’s all-flash optimizations, we’ve pushed performance further for latency-sensitive workloads — delivering the throughput that AI infrastructure demands without sacrificing the scale and durability that made RING the platform of choice for the world’s largest unstructured data environments. ARTESCA is optimized for cloud-native workload patterns — containerized applications, DevOps pipelines, and modern data services — rather than raw throughput at extreme scale. We designed it to be fast and efficient within its target footprint. Cloudian HyperStore performs capably in mixed-workload environments and has recently added flash-optimized configurations. Their Cassandra-based metadata layer provides consistent latency characteristics, though organizations should validate performance at their projected scale, particularly for very large object counts where metadata operations become the bottleneck. Operations and Multi-Tenancy Both platforms support multi-tenant deployments, but the approaches differ. RING provides multi-tenancy alongside powerful management and monitoring capabilities suited to organizations with dedicated storage or infrastructure teams. You get tenant isolation, access controls, and the operational depth to match the platform’s flexibility — more control surfaces mean a richer set of levers when you need them. For organizations that require multi-tenancy and granular data placement, geo-distribution, and mixed protocol access, RING delivers all of it on a single platform. ARTESCA follows cloud-native operational patterns: Kubernetes tooling, Helm charts, standard container monitoring. If your team already operates in this world, there’s no new management paradigm to learn. Cloudian has made multi-tenancy and QoS controls a central part of their positioning, with a built-in management console designed for service providers or IT teams offering internal storage-as-a-service. If your primary use case is reselling storage to downstream tenants, that focus is relevant — but it comes at the expense of the architectural depth and scale ceiling that enterprise and large-scale deployments demand. Head-to-Head Summary CriteriaScality (RING / ARTESCA)Cloudian (HyperStore)ArchitecturePurpose-built distributed KV store / Kubernetes-nativeModified Apache CassandraScale CeilingVery high — built for billions of objects, hundreds of PBMid-to-high rangeProtocol SupportS3, NFS, FUSE (RING) / S3 (ARTESCA)S3, with HyperFile for NASData Placement ControlGranular — site, rack, and drive-level policiesBucket-level storage policiesCloud-Native ReadinessNative Kubernetes deployment (ARTESCA)Traditional deployment modelHybrid CloudCloud tiering + K8s-native integrationAWS-focused tieringMulti-TenancySupported with granular controlsSupported — core positioningOperational ModelDepth + flexibility (RING) / K8s-native (ARTESCA)Simplified management console How to Decide These aren’t cosmetic differences — they reflect fundamentally different architectural decisions that play out over years of production use. We’d encourage you to think beyond today’s requirements. How many petabytes will you manage in three years? Is your team consolidating on Kubernetes? Do you need file and object access on the same data? Will data sovereignty regulations require provable control over physical data placement? We built RING and ARTESCA to answer those questions at scale. If you’d like to see how they perform against your specific workloads, request a demo or proof of concept — the best comparison happens in your own environment.
