Best Enterprise Storage for AI Training and Inference: Top 8 in 2026

TL;DR: Enterprise storage for AI delivers the throughput, low latency, and petabyte-to-exabyte scale that GPU training and inference demand. Best for object storage: Cloudian HyperStore; best for for high-performance file workloads: VAST Data and WEKA.

What Is Enterprise Storage for AI?

Enterprise AI storage requires ultra-fast NVMe flash and parallel file or object systems. Training needs massive throughput to feed GPUs. Inference needs sub-millisecond latency for fast, real-time responses. Storage pipelines must scale seamlessly from raw data collection to final production use.

Unlike traditional storage, these systems must manage not only vast amounts of data but also enable rapid data access, high throughput, and seamless scalability. AI projects often require ingesting, processing, and analyzing massive datasets at speeds and scales that exceed the capabilities of standard enterprise storage solutions. As a result, AI-ready storage is purpose-built to handle these requirements reliably and efficiently.

Key requirements of enterprise storage for AI training and inference include:

• High sequential read and write throughput: AI storage must sustain fast sequential data transfers during training, preprocessing, and augmentation to prevent I/O bottlenecks and GPU idle time.
• Parallel access from large GPU clusters: Storage systems must serve shared datasets to hundreds or thousands of GPUs simultaneously using parallel file systems, distributed data placement, and high-bandwidth networks.
• Fast access to training datasets: Indexing, caching, and high-speed transfers help data scientists retrieve datasets quickly, accelerate model iteration, and improve GPU utilization.
• Distributed file system support: Distributed file systems provide shared, scalable access to petabyte-scale datasets across compute nodes while maintaining high throughput and low latency.
• Rapid recovery from training failures: Checkpoints, snapshots, and automated backups allow interrupted training jobs to resume without restarting, reducing downtime and lost compute resources.

This is part of a series of articles about AI infrastructure

In this article:

• Enterprise AI Storage Solutions at a Glance
• Why AI Workloads Require Specialized Enterprise Storage
• Enterprise Storage Requirements for AI Training and Inference
• Notable Enterprise Storage Solutions for AI

Enterprise AI Storage Solutions at a Glance

The table below summarizes the key differences between the solutions covered in this article. We explore each of them in more detail in the sections that follow.

Why AI Workloads Require Specialized Enterprise Storage 

Massive and Rapidly Growing Data Volumes

AI workloads generate and consume enormous amounts of data, often far exceeding the capacity of conventional storage systems. Modern AI models are trained on datasets that can range from terabytes to petabytes, and these datasets are continually growing as organizations collect more information from sensors, logs, user interactions, and other sources. 

Traditional storage solutions struggle to keep up with this scale, leading to bottlenecks that slow down AI development cycles and model training times. The velocity at which data is generated and needs to be ingested also presents a significant challenge. AI applications often require near-real-time access to fresh data for tasks such as continuous learning or adaptive inference. 

How enterprise storage helps:

Without storage systems built to handle both the volume and speed of this data, organizations risk underutilizing their AI infrastructure and failing to achieve optimal results from their models.

High-Throughput GPU Data Access

AI training and inference tasks are typically accelerated by GPUs, which can process data orders of magnitude faster than traditional CPUs. However, to fully utilize these GPUs, the underlying storage must deliver data at a rate that matches the GPUs’ processing speed. If the storage cannot keep up, GPUs become idle, resulting in wasted computational resources and increased costs.

High-throughput data access is especially critical during model training, when large datasets must be streamed continuously to multiple GPUs in parallel. This requires storage architectures that can deliver sustained, multi-gigabyte-per-second throughput without interruption or latency spikes. 

How enterprise storage helps:

Enterprise storage for AI addresses this need by employing technologies such as NVMe drives, high-speed networking, and parallel file systems, ensuring that data pipelines remain unimpeded.

Related content: Read our guide to NVIDIA GPUDirect Storage, its key features and use cases.

Low-Latency Inference Requirements

Inference (the process of making predictions using trained AI models) requires low-latency data access, especially in real-time applications like fraud detection, autonomous vehicles, or interactive AI assistants. 

Delays in fetching input data or storing output predictions can severely impact the user experience and, in some cases, jeopardize safety or compliance. As AI expands into latency-sensitive domains, the need for responsive storage systems becomes more pronounced.

How enterprise storage helps:

To meet these requirements, enterprise storage for AI is optimized for rapid data retrieval and minimal I/O wait times. Technologies such as solid-state drives (SSDs), memory caching, and tiered storage architectures are leveraged to reduce latency. These features ensure that inference workloads can deliver results within strict time constraints, enabling organizations to deploy AI solutions in environments where every millisecond counts.

Support for Structured and Unstructured Data

AI workloads span a wide range of data types, from structured tabular data used in traditional analytics to unstructured formats such as images, video, audio, and text. Effective AI storage solutions must support seamless ingestion, storage, and retrieval of both structured and unstructured data. This flexibility is crucial for organizations that deploy diverse AI models across different business functions.

How enterprise storage helps:

Enterprise storage for AI incorporates features that enable efficient management of heterogeneous data types. These systems often support object storage, file storage, and database integration within a unified platform. 

Enterprise Storage Requirements for AI Training and Inference 

1. High Sequential Read and Write Throughput

AI training processes involve reading and writing large volumes of data in a sequential manner, especially when dealing with image or video datasets. High sequential throughput is essential to prevent I/O bottlenecks that can stall model training and reduce overall efficiency. Enterprise storage for AI is engineered to maximize read and write speeds by using technologies such as: 

• NVMe SSDs
• Parallel data paths
• Optimized caching algorithms

Maintaining high throughput is not only critical during training but also during data preprocessing and augmentation steps, which are common in AI pipelines. If the storage system cannot keep pace with these operations, downstream tasks are delayed, negatively impacting project timelines. By investing in storage architectures that prioritize sequential performance, organizations can ensure that their AI workloads run smoothly from start to finish.

2. Parallel Access from Large GPU Clusters

Modern AI models are often trained on clusters of GPUs working in parallel to accelerate learning. These clusters require simultaneous access to the same training data, which places significant demands on the storage infrastructure. Enterprise storage for AI must be capable of serving data to hundreds or thousands of GPUs without contention or performance degradation.

To achieve this, storage solutions use: 

• Parallel file systems
• High-bandwidth networking
• Distributed data placement 

These technologies ensure that each GPU node receives the data it needs without waiting for other nodes to finish their operations. As a result, AI training jobs scale efficiently across large clusters, reducing training times and enabling the use of more complex models.

3. Fast Access to Training Datasets

Quick access to training data is a foundational requirement for efficient AI development. Data scientists frequently iterate on models, requiring rapid retrieval of different datasets or data subsets for experimentation. Slow storage systems can hinder this process, forcing teams to wait for data to load before they can proceed with training or validation.

Enterprise storage for AI addresses this need by:

• Optimizing data indexing
• Caching frequently accessed datasets
• Supporting high-speed data transfers

These features reduce wait times and enable more agile model development cycles. Fast data access also supports better utilization of expensive GPU resources, ensuring that hardware investments translate directly into productivity gains.

4. Distributed File System Support

Distributed file systems are critical for supporting the scale and performance requirements of AI workloads. They allow data to be stored and accessed across multiple servers, providing a unified namespace and enabling seamless data sharing among compute nodes. This is especially important for AI training jobs that run on large clusters and require concurrent access to shared datasets.

Enterprise storage for AI often integrates distributed file systems such as: 

• Lustre
• IBM Spectrum Scale
• Open-source alternatives like Ceph 

These systems are designed to handle petabyte-scale data volumes while maintaining high throughput and low latency. By supporting distributed architectures, organizations can scale their storage infrastructure in line with growing AI demands without compromising performance.

5. Rapid Recovery from Training Failures

AI training jobs can be lengthy and resource-intensive, making them susceptible to interruptions from hardware failures, power outages, or software errors. Rapid recovery capabilities in enterprise storage are essential to minimize lost time and computational resources when such failures occur. Organizations can resume training from the point of failure rather than starting over, using features such as:

• Checkpointing
• Automated backups
• Snapshotting 

These recovery mechanisms not only reduce downtime but also improve overall workflow resilience. By ensuring that progress is preserved and can be quickly restored, enterprise storage systems help maintain the momentum of AI projects. This reliability is particularly valuable in production environments where delays or data loss can have significant operational or financial impacts.

Notable Enterprise Storage Solutions for AI

How we selected these solutions: We shortlisted enterprise storage platforms for AI based on high-throughput data delivery to GPUs, low-latency access for training and inference, scalability from petabytes to exabytes, support for both structured and unstructured data, and enterprise-grade security and resilience.

Object Storage Platforms

1. Cloudian HyperStore

Best for: On-prem, S3-native object storage for data-centric AI

Strengths: Native S3, GPUDirect throughput, multi-tenancy, ransomware defense

Things to consider: Capacity dashboard and some S3 feature parity still maturing

Cloudian HyperStore is an S3-compatible object storage platform for managing large volumes of unstructured data across on-premises and multi-cloud environments. It is built on a shared-nothing, peer-to-peer architecture in which every node serves I/O, with no metadata server, head node, or central controller in the data path. Adding a node adds CPU, memory, network, and disk at the same time, so throughput scales linearly with capacity.

For AI, HyperStore provides direct parallel access through its S3 API, supporting thousands of concurrent operations. It supports NVIDIA GPUDirect and RDMA for S3-compatible storage, along with data-locality optimization and all-flash configuration options, to move data from storage to GPUs while bypassing CPU bottlenecks. The platform is software-defined and runs on Cloudian appliances or industry-standard servers.

Key features include:

• S3 API compatibility: HyperStore is built specifically for the S3 API and uses the AWS S3 SDK, supporting AWS S3 features and operations for integration with cloud-native and AI applications across hybrid and multi-cloud environments.
• GPUDirect and RDMA performance: Support for RDMA for S3-compatible storage delivers up to 35 GB/s per node on reads while reducing CPU utilization, providing direct high-speed data transfers from storage to GPUs for training and inference.
• Exabyte-scale, non-disruptive expansion: A modular design scales from small deployments to exabytes across one or multiple sites, managed as a single system with a flat, global S3 namespace and one management plane.
• Data protection options: Erasure coding distributes data fragments across nodes, racks, or data centers to protect against drive, node, rack, and full site failure. Replication is available where lower latency matters, configured per bucket.
• True multi-tenancy: A single cluster supports many tenants, each with its own users, groups, IAM policies, and role-based access controls, with per-tenant QoS to enforce throughput limits and per-user metering for chargeback.
• HyperScale AI Data Platform: An integrated option built on NVIDIA’s AI Data Platform reference design with RTX Pro 6000 GPUs, using S3 RDMA to transform on-premises unstructured data into AI-ready data while keeping it under local control.
• Military-grade security: Secure multi-tenancy, encryption in transit and at rest, KMIP, role-based access controls, IAM, SAML integration, MFA, Secure Shell, and Object Lock for ransomware defense, with support for machine learning frameworks including PyTorch, TensorFlow, and Kafka.

Limitations (as reported by users on G2):

• Capacity management dashboard: Some users would like the capacity management and monitoring dashboard to provide clearer views and more detail.
• AWS and S3 feature parity: A few reviewers note that integration with AWS could be tighter and that keeping pace with the newest S3 features would be helpful.
• Onboarding for new users: Advanced configuration can require documentation or hands-on support, giving new administrators a learning curve before the interface feels intuitive.

2. MinIO AIStor

Best for: S3-compatible object storage for AI training and inference

Strengths: Software-defined, exabyte single namespace, broad S3 support

Things to consider: Monitoring depth and clustering docs need in-house expertise

MinIO AIStor is a software-defined, S3-compatible object store designed for AI and analytics workloads at scale. It uses a flat namespace that grows from petabytes to exabytes without breaking into separate systems, and its distributed architecture is built to sustain performance as deployments expand across clusters, data centers, and clouds. It can run on the edge, in the core, or in the cloud.

AIStor targets AI pipelines with low-latency access and high concurrency for training, inference, and fine-tuning. It connects to storage through the S3 API and other interfaces, and integrates with common AI and analytics frameworks. A separate product, MemKV, provides a context memory store for AI inference.

Key features include:

• Native S3 and multi-protocol access: AIStor supports the S3 API, S3 Express, an Iceberg catalog, SFTP, and data sharing interfaces, so object, table, and file data can be reached through standard tools.
• Exabyte-scale single namespace: Data grows from petabytes to exabytes in one namespace, with the distributed architecture designed to hold performance steady as capacity increases.
• AI framework integration: AIStor connects with PyTorch, TensorFlow, Spark, and Apache Iceberg, alongside the S3 API, so existing AI and data-lakehouse tooling can read and write in place.
• Data protection and resilience: Erasure coding, object immutability, anti-ransomware controls, versioning, and lifecycle management protect stored data, with a key management server and encryption for security.
• Replication and data management: Site-to-site replication, tiering, and acceleration features manage and move data across environments, with caching used to feed compute during repeated access.
• Observability and administration: Built-in metrics, tracing, and logging integrate with tools such as Prometheus and OpenTelemetry, with console-based monitoring, traffic management, and multi-tenancy support.

Limitations (as reported by users on G2):

• Monitoring and observability depth: Some users want stronger built-in usage monitoring for production operations, citing occasional performance dips under heavy read and write activity.
• Clustering documentation: Reviewers running distributed deployments outside container platforms report gaps in documentation and performance-tuning guidance, requiring in-house expertise.
• Management interface at scale: A few users note that the browser interface can become difficult to use when a single bucket holds very large numbers of objects.
• Subscription model: The commercial subscription is oriented toward large-capacity deployments, which some smaller users find harder to justify.

3. Scality RING

Best for: Object storage across the full AI data pipeline

Strengths: Multi-protocol, 14-nines durability, CORE5, RING XP flash tier

Things to consider: Setup complexity and frequent patching add overhead

Scality RING is scale-out file and object storage software designed for large, unpredictable workloads. It scales in multiple dimensions, including capacity, performance, and number of buckets, across servers, sites, and clouds, and presents a hybrid-cloud S3 namespace from a single system. The software runs on industry-standard servers under a usable-capacity subscription model.

For AI, Scality positions RING as a repository across the pipeline, from ingesting and preparing datasets to training, checkpointing, and inference. Its all-flash configuration, RING XP, adds a performance tier with microsecond-level latency for small objects, aimed at model training, fine-tuning, and real-time inference where GPUs must stay fed.

Key features include:

• Multi-protocol access: RING provides S3 object storage alongside NFS and SMB file access from the same infrastructure, with support for user and application metadata tagging and metadata search.
• RING XP all-flash tier: An all-flash configuration on AMD EPYC and NVMe servers delivers microsecond response times for small (4KB) objects through a simplified AI object storage API, plus a fast S3 mode for high-throughput workloads.
• High durability and availability: Data durability is provided through erasure coding, replication, and self-healing, with multi-geography options that maintain access during failures, upgrades, and expansions.
• CORE5 cyber resilience: Five layers of cyber resilience include S3 Object Lock immutability, AWS-compatible IAM and MFA, 256-bit encryption at rest, HTTPS connections, and configurable retention and compliance modes.
• Multidimensional scaling: Capacity, performance, and operations scale independently from terabytes to exabytes on a choice of HDD, SSD, or NVMe media, without lock-in to specific hardware.
• AI ecosystem integration: RING XP integrates with AI frameworks and orchestration tools and has a joint solution with WEKA, and Scality reports more than 150 validated application partners.

Limitations (as reported by users on PeerSpot):

• S3 feature completeness: Some users report that not every S3 feature is implemented, citing specific backup-tool checksum behavior and functions such as S3 Select as gaps versus native AWS S3.
• Initial setup complexity: Reviewers describe a complex initial deployment with many commands and components, often requiring professional services or significant expertise.
• Patching and updates: Several users note that frequent patches and updates add operational overhead and can affect stability between releases.
• Management interface: The interface is described as focused on administration and monitoring, with users asking for a more modern and capable management UI.
• Entry cost: The solution is better suited to large deployments, and smaller organizations may find the cost of entry high.

High-Performance File and Parallel Storage Platforms

4. VAST Data Platform

Best for: Unified all-flash platform for the entire AI pipeline

Strengths: DASE all-flash, multiprotocol, data reduction, RAG engine

Things to consider: Write throughput trails reads; premium pricing

The VAST Data Platform is an all-flash data platform built on an architecture VAST calls DASE, or Disaggregated and Shared Everything, which separates storage from compute. It unifies storage, database, and compute services into a single system and is designed to serve the AI data pipeline from data capture and preparation through model training and serving, scaling from terabytes to exabytes.

The platform handles files, objects, blocks, tables, and streaming data within one architecture, reachable through multiple protocols. It applies data reduction on flash to make an all-flash design practical at scale, and adds services for real-time analytics and retrieval-augmented generation on top of the stored data.

Key features include:

• DASE all-flash architecture: Disaggregating compute from storage lets the platform scale parallel data access across nodes while running entirely on flash, with data reduction applied to control cost.
• Universal multiprotocol data store: VAST DataStore handles file, object, and block data in one system, removing separate storage tiers for analytics, HPC, and AI workloads.
• Integrated database: VAST DataBase combines database and data-warehouse functions in one system, handling transactions and analytics at petabyte scale.
• Programmable data engine: VAST DataEngine provides serverless triggers, functions, and in-place processing to automate data pipelines and process data where it resides.
• Real-time RAG services: VAST InsightEngine processes raw enterprise data into AI-ready form for retrieval-augmented generation, developed in partnership with NVIDIA.
• Global namespace and virtualization support: VAST DataSpace provides a global namespace across locations, and the platform supports Kubernetes and virtualization environments with multi-tenancy and QoS controls.

Limitations (as reported by users on PeerSpot; this is a highly rated product with limited critical reviews):

• Write versus read performance: Some users note that write performance is lower than read performance, and would like the read/write balance improved.
• Deployment complexity: A few reviewers report that initial deployment and tuning can be involved, particularly with certain flash configurations.
• Pricing: The platform is described as not the cheapest option, positioning it as a premium purchase.

5. WEKA NeuralMesh

Best for: Feeding GPU clusters for training and inference

Strengths: Microsecond latency, zero-tuning, multiprotocol, tiering

Things to consider: Newer platform; benefits depend on fast networking and NVMe

NeuralMesh by WEKA is a software-defined, fully distributed storage system built on a containerized, microservices architecture. It interconnects data, compute, and storage services and is organized into five components, Core, Accelerate, Deploy, Observe, and Enterprise Services, that handle resilience, performance, deployment, monitoring, and data services. The system is designed to become more resilient and efficient as it scales from petabytes to exabytes.

For AI and HPC, NeuralMesh combines memory and flash storage in a single low-latency pool and creates direct paths between data and applications to deliver microsecond latency at scale. It runs on bare metal, in public cloud, or in hybrid environments, and add-on capabilities extend GPU memory and fuse compute with storage.

Key features include:

• Distributed core architecture: The Core component distributes data and metadata across the system, automatically balancing I/O to prevent hotspots, with erasure coding and rapid file-level rebuilds for durability at exabyte scale.
• Acceleration for GPUs: The Accelerate component uses a zero-copy path, POSIX kernel bypass with SPDK and DPDK, virtual metadata servers, and adaptive caching to deliver microsecond latency and remove metadata bottlenecks.
• Multi-protocol single namespace: Built-in POSIX, NFS, SMB, and S3 support in one namespace lets different workloads access the same data without separate systems.
• Flexible deployment: Containerized microservices run on bare metal, cloud, or hybrid with identical software, and provide portability across AWS, Azure, GCP, OCI, and neoclouds without refactoring.
• Enterprise data services: Snapshots and clones, incremental snap-to-object backup, tiering of cold data to object storage, end-to-end encryption with FIPS 140-3 Level 1, and inline data reduction are integrated into the platform.
• Observability and multi-tenancy: Multi-cluster dashboards, real-time performance intelligence, smart alerting, and Prometheus export provide visibility, with tenant isolation and role-based access controls for secure sharing.

Limitations (based on publicly available sources; WEKA storage is highly rated with few critical reviews):

• Newer platform: A critical reviewer on Gartner Peer Insights notes that the platform is relatively new and not as battle-tested as longer-established enterprise storage vendors.
• Infrastructure dependency: The design is NVMe-flash native and relies on high-speed networking, so realizing its full benefit depends on suitable infrastructure investment.
• Analytics depth: Independent comparisons note that its focus is on high-throughput performance rather than the depth of built-in data analytics found in some competing platforms.

6. Pure Storage FlashBlade

Best for: Unified all-flash file and object storage for AI

Strengths: One OS for NFS/SMB/S3, non-disruptive upgrades, tiering

Things to consider: Premium pricing; limited data deduplication

Pure Storage FlashBlade, from the vendor now operating under the Everpure brand, is a unified all-flash file and object storage array that runs native NFS, SMB, and S3 on a single operating system, Purity//FB, without gateways or protocol forks. It is a scale-out platform that treats files and objects as equals, and it upgrades blades, modules, and software without downtime.

The FlashBlade line spans three models: FlashBlade//E for capacity-oriented workloads, FlashBlade//S for high-performance file and object use including AI training and inference, and FlashBlade//EXA for large-scale AI and HPC. It integrates with NVIDIA DGX SuperPOD, Apache Spark, and Splunk, and includes immutable snapshots and always-on encryption.

Key features include:

• Unified file and object: Purity//FB serves native NFS, SMB, and S3 from one operating system, so file and object workloads run on a single platform without gateways or added management complexity.
• All-flash performance and efficiency: The all-flash architecture delivers high throughput with data reduction while lowering footprint and cooling, with sustained performance up to 55 GB/s per chassis and higher across the model line.
• Tiered product line: FlashBlade//S provides up to 650 GB/s of throughput for AI training and inference, and FlashBlade//EXA targets large-scale AI and HPC with more than 10 TB/s of throughput.
• Non-disruptive upgrades: Blades, modules, and software can be upgraded without downtime, avoiding disruptive refresh cycles as workloads grow.
• Cyber resilience: SafeMode Snapshots provide immutable recovery points as a defense against ransomware, with always-on encryption protecting stored data.
• Zero Move Tiering and replication: Purity//FB places data at a granular file level based on access patterns, and provides rapid replicas and asynchronous replication for distributed data.

Limitations (as reported by users on PeerSpot):

• Pricing and licensing: Users describe the pricing as high and ask for simpler, more cost-effective licensing.
• Technical support: Several reviewers report that support response times and consistency have declined and can slow issue resolution.
• Cloud and S3 integration: Users would like stronger public-cloud integration and improvements to S3-side features such as authentication and Kubernetes integration.
• Data reduction: Some users note that data reduction relies on compression with limited deduplication, reducing effective savings in certain environments.
• Documentation: Setup and configuration documentation is cited as an area that could be more comprehensive.

7. DDN Data Intelligence Platform

Best for: High-throughput data fabric for large AI clusters

Strengths: EXAScaler parallel file system plus Infinia object platform

Things to consider: Premium pricing; node-by-node upgrades take time

The DDN Data Intelligence Platform is built from two components that together form an AI data fabric across on-premises, cloud, and edge environments. EXAScaler is a parallel file system, based on Lustre, aimed at high-throughput AI and HPC workloads, while Infinia is a software-defined, multi-protocol platform for inference, data analytics, data preparation, and model loading. The platform integrates closely with NVIDIA and scales from terabytes to exabytes.

DDN positions the platform for large GPU environments, citing deployments spanning tens of thousands of GPUs and large numbers of concurrent clients. It uses metadata-driven data management to reduce data movement and keep GPUs supplied during training, checkpointing, and inference, and supports secure multi-tenancy for shared and sovereign AI environments.

Key features include:

• EXAScaler parallel file system: A Lustre-based parallel file system delivers multiple TB/sec of throughput for large-scale training and checkpointing, and is used in NVIDIA environments for training large models.
• Infinia software-defined platform: Infinia provides native multi-tenancy, metadata indexing, and multi-protocol access including S3 and POSIX, with sub-millisecond latency and KV cache acceleration for inference and RAG.
• NVIDIA integration: The platform works with NVIDIA components including NeMo, NIM microservices, DGX systems, BlueField DPUs, and Spectrum-X networking to accelerate AI data pipelines.
• Unified AI data fabric: Infinia and EXAScaler combine into a single data fabric across enterprise, cloud, and edge, with metadata-driven management to eliminate silos and reduce data movement.
• Multi-tenancy and security: Secure multi-tenancy, granular access controls, and dynamic performance allocation support shared infrastructure and sovereign AI deployments.
• Media-agnostic scaling: The EXAScaler and Infinia platforms are designed to sustain GPU performance across a mix of high-performance SSDs, lower-cost SSDs, and HDD tiers, scaling from terabytes to exabytes.

Limitations (as reported by users on PeerSpot):

• Cost: Users report that the platform, and EXAScaler in particular, is expensive relative to alternatives on a total-cost-of-ownership basis.
• Upgrade process: Reviewers describe node-by-node upgrades and major OS updates as time-consuming, requiring multiple reboots even though the cluster stays available.
• Protocol scope: Some users note that EXAScaler is not multi-protocol by nature and requires additional hardware to support protocols such as NFS or object storage.
• Feature and management gaps: A reviewer notes that the commercial version can lag the open-source Lustre release on some features, and that management could be improved.

8. NetApp AFX

Best for: Enterprise disaggregated storage for AI data pipelines

Strengths: ONTAP management, 4 TB/s throughput, metadata engine, AIDE

Things to consider: Cost and licensing complexity; CLI-heavy management

NetApp AFX is a disaggregated storage system for enterprise AI, built on NetApp ONTAP. It separates compute from storage so the two scale independently and linearly, with granular QoS, and it integrates into a hybrid multi-cloud data estate. AFX is certified for NVIDIA DGX SuperPOD and pairs with the NetApp AI Data Engine to move data through the AI pipeline from preparation to application deployment.

The system delivers high throughput and large capacity in a single cluster while carrying ONTAP’s data management and security features. An always-current metadata engine helps teams find and curate datasets, and real-time ransomware detection and fine-grained access controls protect data as it moves across environments.

Key features include:

• Disaggregated scaling: Compute and storage scale independently and linearly, with granular QoS, so applications hold performance as the system grows to up to 128 storage controllers.
• High throughput and capacity: AFX delivers up to 4 TB/s of throughput in a single cluster and up to 1 exabyte of capacity in a single cluster with FabricPool tiering.
• ONTAP data management: Built on ONTAP, AFX provides enterprise data management, fine-grained access controls, and real-time ransomware detection across hybrid multi-cloud environments.
• AI Data Engine: The NetApp AI Data Engine is an ONTAP-integrated service that provides a metadata catalog, in-place vectorization for training, fine-tuning, and RAG, and governance from data preparation to application deployment.
• Metadata engine for datasets: An always-current metadata engine lets teams find and curate the right datasets, supported by optional data compute nodes that index enterprise data.
• Protocol and efficiency support: AFX supports pNFS, NFS, SMB, S3, and NFS/RDMA protocols, with data reduction reported at 24% in pre-release testing on ONTAP 9.19.1.

Limitations (as reported by users on PeerSpot; drawn from the ONTAP-based NetApp AFF line that AFX is built on):

• Cost and licensing: Users describe the platform as costly with a complex licensing and product portfolio that could be simplified.
• Learning curve: Reviewers note a steep learning curve for new administrators and a reliance on the command line, with the GUI not covering all functions.
• Technical support: Some users report that first-level support response times and depth of knowledge can slow escalation on critical issues.
• Usable capacity buffer: A reviewer notes the need to keep utilization below a threshold to preserve performance, reducing usable headroom compared with some competitors.

Conclusion

Enterprise AI initiatives depend on storage infrastructure that can deliver consistent throughput, low latency, and seamless scalability as datasets and GPU environments continue to grow. By adopting storage platforms designed for parallel access, distributed architectures, strong data protection, and support for both structured and unstructured data, organizations can accelerate model development, improve infrastructure utilization, and build AI environments that remain performant and resilient from data ingestion through training, inference, and long-term data management.