# How to Save 90% on Object Storage Capacity with Cloudian + HPE Alletra

HPE and Cloudian have joined forces to offer a solution that can save 90% on object storage capacity, while still delivering the superior S3 API fidelity of the world’s leading object storage platform.

![HPE Cloudian Data Reduction](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.25.31-AM.png?w=1024)

Modern data platforms have converged on S3-compatible object storage as their preferred foundation. The reasons are clear: horizontal scalability, open API compatibility, and operational simplicity. At the same time, enterprises increasingly want to maximize physical storage efficiency — taking advantage of the inline deduplication and compression capabilities available in today’s NVMe storage arrays.

Traditionally, these have been separate conversations. Object storage platforms are typically deployed on direct-attached disks, while deduplication and compression have lived in purpose-built primary storage arrays. The question this joint Cloudian and HPE validation set out to answer is straightforward: what happens when you combine the best of both?

| **The opportunity:** By running Cloudian HyperStore S3-compatible object storage on top of an HPE Alletra NVMe array with inline deduplication and compression, organizations can capture substantial capacity savings, without changing a single S3 API call or application configuration. |
| --- |

#### The Architecture: Cloudian HyperStore on HPE Alletra NVMe

The joint Cloudian and HPE solution layers two complementary technologies: Cloudian HyperStore provides the S3 abstraction layer — scale-out, policy-driven, fully API-compatible — while HPE Alletra Storage MP provides the physical storage substrate with inline data reduction beneath it. Each layer is independently optimized.

| **Cloudian HyperStore** | **HPE Alletra Storage MP B10240** |
| --- | --- |
| — Full S3 API compatibility— Scale-out, software-defined architecture — Policy-based lifecycle and tiering — Multi-site replication (sync/async) — Kubernetes and AI platform integration — Runs on HPE ProLiant DL380 Gen11 | — All-NVMe array, sub-millisecond latency— Inline deduplication + compression — Six-nines (99.9999%) availability — 64 Gb Fibre Channel fabric connectivity — Independent capacity scaling — HPE InfoSight predictive analytics |

HyperStore operates transparently on top of the HPE Alletra SAN backend. From the S3 layer’s perspective, it is simply writing to block storage — all S3 APIs, replication policies, lifecycle rules, and multi-site capabilities are fully preserved. Beneath the surface,**Alletra applies inline deduplication and compression before every write**, consuming physical capacity only for the unique, compressed data blocks.

Crucially, the two layers scale independently: HyperStore scales S3 throughput by adding compute nodes, while HPE Alletra MP scales capacity and storage performance by adding NVMe enclosures — without the over-provisioning that plagues monolithic storage architectures.

**Reference Architecture**

*![HPE Alletra Cloudian reference architecture](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.28.39-AM.png?w=1024)*

*Figure 1 — Cloudian HyperStore + HPE Alletra B10000 Reference Architecture: 3-node HyperStore cluster on HPE ProLiant DL380 Gen11, connected to HPE Alletra B10240 via 64 Gb Fibre Channel. Client traffic distributed via HAProxy across all nodes.*

## Performance Test Results

All performance testing was conducted using Intel Gosbench, a widely adopted object storage benchmark that generates high-concurrency, realistic S3 workloads. Gosbench was executed simultaneously on all three HyperStore nodes, with HAProxy distributing requests to prevent local affinity and ensure accurate cluster-wide measurements.

**Read Throughput: Linear Scaling to Cluster Saturation**

*![HPE Alletra Cloudian performance](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.30.32-AM.png?w=1024)*

*Figure 2 — HyperStore Read Bandwidth (Gosbench, MB/s vs. Worker Threads). Throughput scales linearly with worker threads until cluster-wide saturation, confirming no configuration bottlenecks.*

Read throughput demonstrated strong and consistent linear scaling with worker thread count. The performance plateau at saturation reflects a fully optimized cluster — the ceiling is the available compute and network bandwidth, not a software or storage bottleneck.

**Write Throughput: Consistent Under Replication Overhead**

*![HPE Alletra Cloudian performance](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.32.11-AM.png?w=1024)*

*Figure 3 — HyperStore Write Bandwidth (Gosbench, MB/s vs. Worker Threads). Consistent write throughput maintained even with RF3 replication enabled across all three nodes.*

Write performance remained stable and predictable even with Cloudian’s replication factor of 3 enabled across the cluster. The HPE Alletra NVMe backend absorbed write-intensive bursts without throttling, allowing HyperStore to sustain strong write bandwidth under load.

**IOPS: Small-Object Concurrency for AI Inference Workloads**

*![HPE Alletra Cloudian performance](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.33.29-AM.png?w=1024)*

*Figure 4 — HyperStore IOPS Results (Gosbench, IOPS vs. Worker Threads). Strong transaction scalability under increasing concurrency, validating suitability for metadata-intensive AI inference pipelines.*

IOPS testing validated the architecture’s suitability for small-object, high-transaction workloads — characteristic of AI inference pipelines and metadata-heavy operations. Latency remained in the sub-millisecond to low-millisecond range across moderate concurrency levels, with predictable linear increase as the cluster approached saturation.

## Data Reduction Results: The Headline Finding

Performance numbers are table stakes for enterprise storage. The finding that differentiates this architecture is what happened on the back end — the data reduction efficiency delivered by HPE Alletra’s inline deduplication and compression beneath a live HyperStore object storage workload.

**Test 1 — Mixed Real-World Dataset: 22:1 Reduction**

The first test used a representative enterprise dataset: operating system ISO images, virtualization packages, video media files, and application binaries — content typical of backup, archive, and media repository workloads. These file types share significant repeated data blocks both within and across objects, making them strong candidates for block-level deduplication.

*![HPE Alletra Cloudian performance](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.35.22-AM.png?w=1024)*

*Figure 5 — HPE Alletra Capacity Analytics: Test 1 Results. 113.9 TiB of logical data reduced to 10.9 TiB physical — a 22:1 data reduction ratio representing over 90% storage savings.*

| *Test 1 result: 113.9 TiB of logical object data → 10.9 TiB of physical storage consumed. Data reduction ratio: 22:1. Storage savings: >90%.* |
| --- |

**Test 2 — Gosbench Synthetic Dataset: >25:1 Reduction**

The second test used programmatically generated Gosbench synthetic objects — millions of objects across mixed sizes, written under high-concurrency conditions. These datasets, representative of AI training and analytics workloads, contain highly compressible and deduplicated content, establishing an upper bound on data reduction efficiency for this class of workload.

*![HPE Alletra Cloudian data reduction](https://cloudian.com/wp-content/uploads/2026/03/Screenshot-2026-03-27-at-9.36.31-AM.png?w=1024)*

*Figure 6 — HPE Alletra Data Reduction Results: Test 2. 48.6 TiB of logical data reduced to 4.0 TiB physical — exceeding 25:1 data reduction, representing over 96% storage savings.*

| *Test 2 result: 48.6 TiB of logical object data → 4.0 TiB of physical storage consumed. Data reduction ratio: >25:1. Storage savings: >96%.* |
| --- |

**Summary: Both Tests Side by Side**

| **Metric** | **Test 1: Mixed Real-World** | **Test 2: Gosbench Synthetic** |
| --- | --- | --- |
| Logical Data Written | 113.9 TiB | 48.6 TiB |
| Physical Storage Used | 10.9 TiB | 4.0 TiB |
| Data Reduction Ratio | **22:1** | **>25:1** |
| Storage Savings | **>90%** | **>96%** |
| S3 Compatibility Impact | None — fully transparent | None — fully transparent |
| HyperStore Config Changes | None required | None required |

A critical architectural note: inline deduplication operates at the block level beneath HyperStore, transparently reducing physical capacity across stored objects. No changes to HyperStore configuration or S3 operations are required.

## Summary: What This Architecture Actually Delivers

The validated test results confirm that it is now possible to run enterprise-class S3-compatible object storage — with full API compatibility, scale-out architecture, and multi-site replication — while simultaneously achieving data reduction ratios that have historically been the exclusive domain of primary storage arrays.

| **Key takeaway:**For organizations managing hundreds of terabytes or petabytes of object data, a 22:1 to 25:1 reduction ratio translates directly into fewer drives, fewer enclosures, lower power consumption, and a dramatically reduced data center footprint — with no application changes and no S3 capability trade-offs. |
| --- |

The architecture is purpose-built for the workloads that are driving storage growth today:

| **🤖 AI Data Lakes**High-bandwidth object storage for training datasets and inference artifacts, with data reduction maximizing physical efficiency. | **💾 Backup & Archive**Common VM and file backup sets achieve 20:1+ deduplication ratios, dramatically reducing long-term retention costs. |
| --- | --- |
| **🎬 Media Repositories**Large video and image asset libraries managed via S3 compatibility, with direct integration into media workflows and CDN pipelines. | **☁️ Cloud-Native Applications**On-premises S3 with full data sovereignty — no API changes, seamless integration with AWS, Azure, and GCP. |

Organizations with existing HPE Alletra MP deployments can layer HyperStore directly on available capacity — enabling object storage workloads on infrastructure already in place, without a new storage procurement cycle.

Download the[full technical white paper](https://data.cloudian.com/WP-Cloudian-HPE-Alletra-Whitepaper)for complete hardware specifications, detailed benchmark methodology, and extended performance and capacity analytics from both test datasets.

**cloudian.com | hpe.com**