AI Storage for Performance and Scalability: Top 6 Solutions Compared

TL;DR: AI storage is judged on throughput, IOPS, latency, metadata speed and scale-out design. Cloudian HyperStore is best for exabyte S3 data lakes, WEKA NeuralMesh is best for GPU-fed training, and Everpure FlashBlade for file plus object.

What Is AI Storage? 

Modern AI storage performance and scalability rely on tiered architectures, such as local NVMe caching for rapid model checkpoints and parallel file systems or S3-compatible object storage for multi-petabyte training datasets. Choosing the right configuration prevents costly GPU starvation and latency bottlenecks.

Unlike traditional storage, AI storage is optimized for handling extremely large datasets, often consisting of unstructured data such as images, videos, and text. These systems are engineered to provide high throughput, low latency, and the ability to scale out rapidly, ensuring that machine learning models and AI pipelines can access and process data efficiently.

Key evaluation criteria

The criteria below are the measurements that separate AI storage solutions from general-purpose enterprise storage:

• Read and write throughput: sustained bandwidth in GB/s or TB/s for feeding training jobs and absorbing checkpoints
• IOPS: discrete read and write operations per second, which governs random access to small files and objects
• Latency: delay between request and delivery, critical for inference and interactive workloads
• Metadata performance: speed of listings, attribute lookups and indexing across very large file and object counts
• Scale-up vs. scale-out storage: whether capacity and performance grow by expanding one system or by adding nodes
• GPU cluster scalability: ability to serve data concurrently to hundreds or thousands of GPUs without becoming the bottleneck

Solutions covered in this guide:

• Object storage platforms for AI

• • Cloudian HyperStore: Exabyte-scale S3 storage with GPU-direct access
  • Dell ObjectScale: All-flash object storage with S3 over RDMA
  • Scality RING: Exabyte object storage with independent scaling

• High-performance file and flash platforms

• • WEKA NeuralMesh: Scale-out storage for GPU training and inference
  • DDN Infinia: Metadata-driven storage for inference and RAG
  • Everpure FlashBlade: Unified all-flash file and object storage

This is part of a series of articles about AI infrastructure

In this article:

• What Does AI Storage Need to Deliver?
• Key Metrics for Comparing AI Storage Performance and Scalability
• Common AI Storage Solutions and How They Meet the Criteria
• Comparing AI Storage Solutions for Performance and Scalability

What Does AI Storage Need to Deliver? 

High Throughput for Large AI Datasets

High throughput is essential for AI storage because training and inference processes often require reading and writing vast amounts of data quickly. During model training, data is loaded in large batches, and any bottleneck in data delivery can significantly slow down the learning process. To meet these demands, AI storage systems are built with high-bandwidth connections, parallel processing capabilities, and optimized data paths that allow multiple streams of data to be read or written simultaneously.

In environments where deep learning is prevalent, the throughput requirements can easily exceed those of traditional enterprise workloads. For example, image and video datasets commonly used in computer vision projects can reach petabyte scale, requiring storage systems that can sustain multi-gigabyte-per-second data transfer rates. 

Low Latency and High IOPS

Low latency is another critical aspect of AI storage, particularly during inference and real-time data processing. Latency measures the delay between a data request and its fulfillment. In AI workflows, especially those involving interactive or time-sensitive applications, high latency can degrade performance and responsiveness. AI storage systems must minimize this delay to ensure that data is delivered to processing units as quickly as possible.

High IOPS, or input/output operations per second, is equally important when dealing with workloads that involve many small, random data accesses. Training modern neural networks often requires shuffling and accessing random batches of data, which puts pressure on the storage system to handle numerous simultaneous I/O requests efficiently. Solutions optimized for high IOPS ensure that data bottlenecks do not impede the overall performance of AI pipelines, supporting both training and inference at scale.

Massive Scalability

Massive scalability is a fundamental requirement for AI storage, as data volumes grow exponentially with the adoption of AI in various industries. Storage systems must accommodate not only the current dataset sizes but also future growth without requiring disruptive migrations or upgrades. This is typically achieved through distributed, scale-out architectures that allow organizations to add capacity and performance incrementally by simply adding more nodes or drives.

Scalability also involves maintaining consistent performance as the system grows. Inadequate scaling can result in performance degradation, management complexity, and increased costs. Effective AI storage solutions offer seamless expansion, automatic load balancing, and efficient data distribution to ensure that as more resources are added, the system continues to operate efficiently. 

Related content: Read our guide to storage systems for long-term AI data retention

High-Concurrency Access

High-concurrency access is essential in AI environments where multiple users, applications, or processes need to access the same data simultaneously. This is common in collaborative AI projects, distributed training scenarios, or shared data lakes, where data must be available to many clients at once without contention or performance drops. AI storage systems must be built to handle large numbers of concurrent read and write operations while maintaining consistent throughput and low latency.

To achieve this, AI storage architectures often use distributed file systems, parallel data paths, and advanced caching mechanisms. These features allow for efficient sharing of data resources across large clusters of GPUs or CPUs, supporting high levels of parallelism and collaboration. Without robust support for concurrency, storage systems can become a bottleneck, limiting the speed and scalability of AI development and deployment workflows.

Key Metrics for Comparing AI Storage Performance and Scalability

Each criterion below covers one dimension of AI storage performance and scalability, with the checks to apply while comparing systems.

1. Read and Write Throughput

Throughput is the volume of data a system can move per second, usually stated in GB/s or TB/s. Training jobs read large batches continuously, and checkpoint writes arrive in bursts that can stall a job if the storage cannot absorb them. Vendors publish throughput figures at wildly different cluster sizes, so the number only means something alongside the node count and hardware it was measured on.

Evaluation criteria:

• What aggregate throughput is claimed, and at what cluster size and node configuration?
• Is write throughput published separately from read, or only the read figure?
• Does throughput scale linearly as nodes are added, or does it flatten?
• Are the figures from a benchmark harness, an internal test, or a production deployment?
• What network fabric is assumed to reach the stated numbers?

2. IOPS

IOPS counts discrete read and write operations per second. It matters most where AI pipelines touch many small files or objects: shuffling training samples, reading individual records during inference, or scanning a dataset made of millions of small images. A system that streams large sequential files well can still fall over on random small-object access.

Evaluation criteria:

• Is an IOPS figure published at all, or only throughput?
• How does the system behave with millions of small objects in one bucket or directory?
• Are read and write IOPS reported separately?
• Does IOPS scale with node count in the same way throughput does?
• Are there documented limits on concurrent requests per node or per tenant?

3. Latency

Latency is the time between a request and its fulfilment, measured in milliseconds or microseconds. Inference, retrieval-augmented generation and interactive applications are all latency-bound, and tail latency usually matters more than the average. Media choice, network path and software stack all contribute.

Evaluation criteria:

• Is latency stated in microseconds or milliseconds, and for which operation types?
• Does the system offer direct data paths such as RDMA or GPU-direct access?
• Does latency stay flat as concurrency and capacity grow?
• Is all-flash required to reach the stated latency, or is a hybrid configuration supported?
• Are tail latency or quality-of-service controls available for multi-tenant environments?

4. Metadata performance

Metadata performance covers listings, attribute lookups, directory traversal and indexing. AI datasets routinely hold hundreds of millions of files or objects, and slow metadata operations create bottlenecks that never show up in a bandwidth benchmark. Some systems dedicate hardware or a separate service to metadata; others distribute it across all nodes.

Evaluation criteria:

• Is metadata handled by dedicated servers, distributed across nodes, or held in a database layer?
• Can metadata be placed on faster media than the data itself?
• What object or file counts has the system been run at in production?
• Are there indexing, tagging or search capabilities beyond basic attributes?
• Have users reported metadata as a limiting factor at scale?

5. Scale-Up vs. Scale-Out Storage

Scale-up adds resources to one system and eventually hits an architectural ceiling. Scale-out adds nodes, growing capacity and performance together. For AI workloads that grow unpredictably, the question is not just whether a system scales out, but what expansion costs operationally: rebalancing time, downtime, and whether performance keeps pace with capacity.

Evaluation criteria:

• Does adding a node add compute, network and capacity, or capacity alone?
• Can capacity and performance be scaled independently of each other?
• How long does data rebalancing take after an expansion, and is it disruptive?
• Is the system software-defined on standard servers, or tied to vendor appliances?
• What is the minimum viable cluster size, and the maximum tested size?

6. GPU Cluster Scalability

This is the practical test of everything above: can the storage keep hundreds or thousands of GPUs busy at once. It depends on concurrency handling, direct data paths to GPU memory, and validated integration with GPU platforms. A system that performs well for a single node can still starve a large cluster.

Evaluation criteria:

• Is there validated integration with NVIDIA reference architectures such as DGX SuperPOD?
• Does the system support GPUDirect, RDMA or equivalent direct paths?
• What per-GPU bandwidth is sustained, and across how many GPUs?
• Are multi-tenancy and QoS controls available for shared GPU environments?
• What is the largest GPU deployment the vendor can reference?

Common AI Storage Solutions and How They Meet the Criteria

The table summarizes how each solution measures up against the six criteria. Each is covered in detail below.

Related content: Read our guide to the leading AI storage vendors

Comparing AI Storage Solutions for Performance and Scalability

How we selected these solutions: We shortlisted AI storage platforms based on sustained throughput and IOPS at scale, latency under concurrent access, metadata handling across large file and object counts, scale-out architecture, and validated support for large GPU clusters.

Object Storage Platforms for AI

1. Cloudian HyperStore

Best for: Exabyte-scale S3 data lakes feeding AI training and inference

Strengths: Peer-to-peer scale-out, GPUDirect support, full S3 API compatibility

Things to consider: Monitoring views can take extra steps to surface detail

Cloudian HyperStore is object storage software for large volumes of unstructured data, deployed on-premises or across multiple sites and managed as a single system. It uses a shared-nothing, peer-to-peer architecture in which every node participates equally in serving I/O, with no metadata server, head node or central controller in the data path.

That design shapes how it scales. Adding a node adds CPU, memory, network and disk to the cluster at the same time, so throughput grows alongside capacity rather than funnelling through a fixed set of controllers. For AI pipelines, data is reached through direct parallel access over the S3 API, supporting thousands of concurrent operations.

HyperStore runs as software-defined storage on industry-standard hardware, either on Cloudian appliances or on servers the buyer selects, with all-flash configurations available where performance requirements are highest.

Key features include:

• AI-ready performance: Distributed architecture delivering high throughput and low latency through parallel S3 access, with NVIDIA GPUDirect support, data-locality optimization and all-flash configuration options for data-centric AI applications.
• Linear scaling with capacity: Every node serves I/O with no central choke point, so adding nodes increases CPU, memory, network and disk together and throughput scales alongside capacity.
• Single flat S3 namespace: Distributed infrastructure across data centers, edge sites and cloud regions is presented as one namespace with one set of credentials and one management plane.
• Per-bucket data protection: Erasure coding distributes fragments across nodes, racks or data centers to survive drive, node, rack and full site failure, with replication available per bucket where lower latency matters more than storage efficiency.
• Placement and replication policies: Data can be replicated synchronously between sites for active-active access, asynchronously for disaster recovery, or tiered to public cloud as it ages, enforced automatically per bucket.
• Native multi-tenancy: A single cluster supports many tenants with their own users, groups, IAM policies and role-based access controls, with pooled resources allocated dynamically and per-tenant QoS limits on throughput and request rate.
• Full S3 API compatibility: The AWS S3 SDK is used directly, supporting AWS S3 features and operations for migration and interoperability across hybrid and multi-cloud environments.
• Unified file and object: File and object storage are managed under a single system, reducing the need to run separate platforms for different data types.

2. Dell ObjectScale

Best for: Enterprise object storage for GenAI training and checkpointing

Strengths: All-flash XF960, S3 over RDMA, exascale rack density

Things to consider: Write performance and S3 gaps flagged by reviewers

Dell ObjectScale is an S3-compatible, Kubernetes-native object storage platform available as appliances, as a software update for existing Dell ECS environments, or as software-defined storage on PowerEdge servers. It serves as a storage engine within the Dell AI Data Platform.

The lineup splits by workload. The X560 is HDD-based and aimed at general-purpose workloads and AI data lakes, reaching up to 9.2 PB raw per rack across up to 16 nodes with cache SSD included. The XF960 is all-flash, aimed at AI training and checkpointing, analytics and fast backup, with drives from 7.68 TB to 122.88 TB and up to 47.2 PB raw per rack.

For AI access paths, ObjectScale supports S3 over RDMA with GPU-direct access, and Dell has published material on vector storage, RAG connectors and KV cache offloading built on the platform.

Key features include:

• All-flash and HDD models: ObjectScale XF960 for AI training and checkpointing at up to 2.9 PB raw per node, and X560 for general-purpose and AI data lake capacity at up to 576 TB raw per node.
• S3 over RDMA with GPU-direct access: Direct data paths intended to shorten the route between object storage and GPU memory for AI and HPC environments.
• Unified global namespace: Enterprise-grade S3 storage presented as one namespace spanning an exascale architecture.
• Multi-site replication: Replication across unlimited Virtual Data Centers, supporting disaster recovery and geo-distributed deployments.
• Data protection schemes: Erasure coding for storage efficiency, replication for resilience, and ObjectLock WORM for immutability and compliance retention.
• Smart rebalancing: Data is redistributed automatically across nodes when nodes are added or retired in multi-rack deployments.
• High availability configuration: Nodes support dual controllers and active-active configurations.
• Compliance coverage: TLS 1.3, SEC 17a-4-f, FINRA and GDPR, with a CISA KEV patch cadence, plus integration with Azure Blob Storage for hybrid strategies.

3. Scality RING

Best for: Multi-tenant exabyte object storage for cloud and sovereign environments

Strengths: Independent scaling axes, separate metadata tier, deep resilience

Things to consider: Complex to administer; rebalancing can take a long time

Scality RING is distributed file and object storage built for cloud, service-provider and sovereign cloud environments, running from multi-petabyte to exabyte scale in one logical namespace. It has been in production since 2009 across more than 1,000 enterprises in over 70 countries.

Its architecture, called MultiScale, allows capacity, performance, tenants, sites and protocols to scale independently rather than in lockstep, so adding performance does not require adding capacity and vice versa. In practice, deployments commonly place metadata on NVMe flash while object data sits on higher-capacity media, which is what makes large object counts workable.

Resilience is a core selling point: 14 nines of data durability, erasure coding and self-healing across sites, and stretched-cluster and zero-RPO patterns that survive whole data centers going offline. Scality’s AIConnect technology positions RING data for AI pipelines, and the company markets specifically to neoclouds and GPU cloud operators.

Key features include:

• MultiScale architecture: Capacity, performance, tenants, sites and protocols each scale independently, so one dimension can be expanded without paying for the others.
• Unified S3 namespace: One logical view across on-premises, cloud and edge locations with full S3 fidelity and policy enforced once across the namespace.
• High-concurrency multi-tenancy: Many tenants accessing concurrently with hard isolation between them, aimed at service providers running hundreds of workloads on one platform.
• CORE5 cyber resilience: Five layers of protection against ransomware, exfiltration and credential compromise, with S3 Object Lock and inherent storage immutability.
• Erasure coding and self-healing: Data protection distributed across sites, with multi-geo, stretched-cluster and zero-RPO patterns and 14 nines of durability.
• Sovereignty controls: On-premises, air-gapped and sovereign-cloud deployment, policy-enforced data residency at namespace level, and site-specific IAM and encryption boundaries.
• Hardware flexibility: A hardware-flexible substrate that spans server generations, avoiding forklift replacement as hardware ages.
• Validated ecosystem: More than 150 application providers and every major server platform certified, covering analytics and AI, data protection and archive.

High-Performance File and Flash Platforms

4. WEKA NeuralMesh

Best for: Keeping large GPU clusters fed during training and inference

Strengths: Linear throughput and IOPS scaling, data reduction, deploy anywhere

Things to consider: Small public review base; premium all-flash pricing

WEKA NeuralMesh is a storage and memory platform for AI and HPC workloads. WEKA states that throughput and IOPS scale linearly with consistently low latency as clusters grow from ten nodes to ten thousand, and that the same software runs on-premises, in hybrid environments, in hyperscale clouds and in AI clouds.

A large part of the current product story is data reduction. NeuralMesh applies similarity-based compression that captures structural redundancy across blocks, which targets the kind of near-duplicate data that checkpoints and container layers produce and that byte-exact deduplication misses. Reduction runs in the background so writes commit to NVMe at native speed.

WEKA combines that with thin provisioning, snapshots, single-hop writes, drive sharing and AlloyFlash hybrid flash, and reports up to 1.9x reads, 13x write throughput and 3x lower latency from the combination.

Key features include:

• Linear performance scaling: Throughput and IOPS scale linearly with consistently low latency across cluster sizes from ten to ten thousand nodes.
• Similarity-based data reduction: Up to 6x reduction on AI/ML data with under 5% write overhead, finding shared-pattern blocks cluster-wide across checkpoints and container layers.
• Background-first write path: Writes commit to NVMe at native speed while reduction processing runs in the background, so checkpoint phases do not stall GPU work.
• Cluster-wide deduplication: Redundancy is eliminated across all reduction-enabled filesystems, spanning every project, team and checkpoint in a shared environment.
• Six efficiency pillars combined: Thin provisioning, snapshots, single-hop writes, drive sharing and AlloyFlash hybrid flash together yield up to 1.9x reads, 13x write throughput and 3x lower latency.
• Intelligent replication: Datasets can be made visible across sites and pulled to the next GPU allocation as it becomes available, supporting workload mobility in distributed infrastructure.
• File and object services: An S3 object store alongside file access, with multitenancy, observability and a Kubernetes operator.
• Deployment portability: The same software runs on-premises, in hybrid setups, in hyperscale clouds and in AI clouds without re-architecting.

5. DDN Infinia

Best for: Real-time inference and RAG pipelines across distributed AI environments

Strengths: Distributed metadata indexing, KV cache acceleration, S3 plus POSIX

Things to consider: Newer than DDN’s parallel file system line; thin review base

DDN Infinia is an AI data engine that orchestrates data across distributed AI environments, unifying fragmented silos into one pipeline with low-latency access. It is built on DDN’s HPC background but aimed at enterprise inference, RAG and analytics rather than the classic supercomputing throughput workloads its EXAScaler parallel file system serves.

Metadata is central to the design. Infinia holds structured, semi-structured and unstructured data in one platform with high-speed metadata indexing, supporting search, filtering and retrieval across very large datasets, and it supports millions of metadata tags per object. An event engine can trigger tagging automatically at ingest.

A high-performance KV store and KV cache keep embeddings, vectors and inference state near compute, which is where DDN’s headline claims come from: 75% reduction in token cost, 22x faster RAG performance and 25x lower time to first byte.

Key features include:

• Metadata-driven data platform: Structured, semi-structured and unstructured data unified with high-speed metadata indexing, enabling search, filtering and retrieval across massive datasets.
• Scalable metadata tagging: Millions of metadata tags per object, with an event engine that can trigger automatic tagging and metadata population during ingest or provisioning.
• KV store and KV cache: Embeddings, vectors and inference state held close to compute to reduce latency in production AI workloads.
• Multi-protocol single namespace: Native S3 and POSIX file access on one platform, so object and file workloads run against the same data without copies or separate infrastructure.
• Unified data from edge to cloud: Intelligent placement and movement of data across core, cloud and edge environments.
• Enterprise-scale multi-tenancy: Workload isolation with QoS management, supporting AI-as-a-service and shared infrastructure.
• Published inference figures: 75% reduction in token cost, 18x more tokens per watt, 22x faster RAG performance and 25x lower time to first byte.
• EXAScaler alongside: DDN’s Lustre-based parallel file system remains available for large-scale training throughput, selected by Google as its first-party Lustre offering and present in the AWS and Azure marketplaces.

6. Everpure FlashBlade

Best for: Unified all-flash file and object for AI training, inference and analytics

Strengths: One OS for NFS, SMB and S3; non-disruptive upgrades; //EXA scale

Things to consider: Premium pricing; cloud integration and docs flagged

Everpure FlashBlade, formerly Pure Storage FlashBlade, is a scale-out all-flash array for unstructured data that runs native NFS, SMB and S3 on a single operating system, Purity//FB, without protocol gateways or separate systems per protocol. Pure Storage legally changed its corporate name to Everpure in February 2026, with products transitioning to the new naming through the year.

The lineup separates by workload profile. FlashBlade//E targets cost-sensitive capacity work such as active archive, backup and content libraries. FlashBlade//S handles up to 650 GB/s throughput for AI training and inference, analytics and HPC. FlashBlade//EXA is the extreme-scale option, quoted at over 10 TB/s throughput for large-scale AI and HPC workloads and neocloud deployments, and described as delivering the metadata performance those workloads demand.

Blades, modules and software are upgraded without downtime, avoiding the refresh cycles that normally interrupt storage estates.

Key features include:

• Unified file and object services: Native NFS, SMB and S3 on one operating system, with files and objects treated as equals rather than one served through a gateway over the other.
• Three-tier product lineup: FlashBlade//E for cost-oriented capacity, FlashBlade//S at up to 650 GB/s for AI training and inference, and FlashBlade//EXA at over 10 TB/s for large-scale AI and HPC.
• Zero Move Tiering: Purity//FB manages data placement automatically at granular file level based on access patterns, without moving data between tiers.
• SafeMode snapshots: Immutable snapshots and always-on encryption as ransomware protection for unstructured data.
• Replication capabilities: Rapid replicas and asynchronous replication designed for globally distributed data.
• Non-disruptive upgrades: Blades, modules and software are upgraded without downtime, with more than 500,000 in-platform Purity upgrades performed.
• Sustained per-chassis performance: Up to 55 GB/s per chassis, with up to 90% more performance per watt and 80% more effective capacity per watt.
• AI ecosystem integrations: NVIDIA DGX SuperPOD support, parallelized access for Apache Spark analytics, and Splunk SmartStore.

Related content: Read our guide to AI storage providers

Conclusion

AI storage for large-scale machine learning needs to balance throughput, latency, IOPS, metadata performance, and expansion behavior rather than optimize for a single benchmark. The right architecture depends on whether workloads are dominated by large sequential reads, small random access, checkpoint writes, inference, or shared GPU clusters. Teams should compare published results against their own hardware, network fabric, dataset structure, and concurrency levels, then validate performance with realistic tests before committing to a platform.