01AI data platforms & arrays
Cloudian HyperStore
S3-compatible object storage that Cloudian positions for large-scale, data-intensive AI workloads.
Best for: Exabyte-scale AI data lakes on-premises.
Visit Cloudian HyperStore →AI Storage Solutions
Storage built for AI: all-flash and object platforms, parallel file systems, cloud services and data-orchestration tools that keep GPUs fed during training, inference and RAG.
28solutions
5Categories
6FAQs answered
02AI data platforms & arrays
IBM Storage
IBM's storage portfolio covering file, block and object for AI, analytics and hybrid cloud.
Best for: IBM and mixed hybrid-cloud estates.
Visit IBM Storage →03AI data platforms & arrays
Everpure (formerly Pure Storage)
A data platform that consolidates storage environments for AI pipelines, training and inference.
Best for: Simple operations with all-flash performance.
Visit Everpure (formerly Pure Storage) →04AI data platforms & arrays
VAST Data
A unified data platform with a global namespace for files, objects, tables and vectors, aimed at AI workloads.
Best for: AI pipelines on one data platform.
Visit VAST Data →05AI data platforms & arrays
Dell PowerScale and ObjectScale
Dell's AI data platform built around PowerScale and ObjectScale storage.
Best for: Dell AI Factory environments.
Visit Dell PowerScale and ObjectScale →06AI data platforms & arrays
NetApp AI data infrastructure
NetApp's AI offerings on ONTAP, including data management for training, inference and RAG.
Best for: Existing NetApp estates adding AI.
Visit NetApp AI data infrastructure →07AI data platforms & arrays
WEKA
A software-defined data platform designed for GPU-heavy AI and HPC workloads.
Best for: Keeping large GPU clusters busy.
Visit WEKA →08AI data platforms & arrays
DDN
High-performance storage and data-intelligence platforms for AI and HPC, including the EXAScaler line.
Best for: Supercomputing-class AI storage.
Visit DDN →09AI data platforms & arrays
Hitachi iQ
Hitachi Vantara's AI-ready infrastructure and storage portfolio.
Best for: Enterprise AI with Hitachi storage.
Visit Hitachi iQ →10AI data platforms & arrays
HPE Alletra storage
HPE's modular storage platform for block, file and object workloads.
Best for: HPE and GreenLake AI environments.
Visit HPE Alletra storage →11Parallel & high-performance file systems
Lustre
The open-source parallel file system used in many of the world's largest HPC and AI clusters.
Best for: Open-source high-throughput file storage.
Visit Lustre →12Parallel & high-performance file systems
BeeGFS
A parallel cluster file system for HPC and AI, developed by ThinkParQ.
Best for: Straightforward parallel file system.
Visit BeeGFS →13Parallel & high-performance file systems
IBM Storage Scale
IBM's parallel file system and global data platform, formerly known as Spectrum Scale.
Best for: Large shared namespaces across sites.
Visit IBM Storage Scale →14Parallel & high-performance file systems
CephFS
The distributed POSIX file system in the open-source Ceph storage platform.
Best for: Open-source unified storage.
Visit CephFS →15Parallel & high-performance file systems
JuiceFS
A distributed file system that stores data in object storage, with POSIX, HDFS and S3 access.
Best for: Shared files for ML on top of object storage.
Visit JuiceFS →16Cloud AI storage services
Amazon S3 Express One Zone
A high-performance S3 storage class for latency-sensitive workloads such as ML training.
Best for: Fast object access in AWS.
Visit Amazon S3 Express One Zone →17Cloud AI storage services
Amazon FSx for Lustre
A managed Lustre file system on AWS for HPC and ML workloads.
Best for: Managed parallel file system in AWS.
Visit Amazon FSx for Lustre →18Cloud AI storage services
Google Cloud Managed Lustre
A managed parallel file system on Google Cloud for AI and HPC.
Best for: Managed Lustre on Google Cloud.
Visit Google Cloud Managed Lustre →19Cloud AI storage services
Google Cloud Hyperdisk
Google Cloud's high-performance block storage, with a variant aimed at loading ML models and data quickly.
Best for: Fast model loading on Google Cloud.
Visit Google Cloud Hyperdisk →20Cloud AI storage services
Azure Managed Lustre
A managed Lustre file system on Azure for AI training and HPC workloads.
Best for: Managed Lustre on Azure.
Visit Azure Managed Lustre →21Cloud AI storage services
Azure Blob Storage
Azure's object storage for unstructured data, including data lakes for analytics and AI.
Best for: Object storage for AI data on Azure.
Visit Azure Blob Storage →22Object storage for data lakes
MinIO AIStor
MinIO's S3-compatible object store positioned for AI and analytics data.
Best for: Self-hosted S3 for AI pipelines.
Visit MinIO AIStor →23Object storage for data lakes
Scality RING
A software-defined, scale-out object and file storage platform.
Best for: Large on-premises data lakes.
Visit Scality RING →24Object storage for data lakes
Ceph
An open-source distributed storage system providing object, block and file storage.
Best for: Open-source scale-out storage.
Visit Ceph →25Data orchestration & access
Hammerspace
Software that presents data across sites and clouds in a single global namespace.
Best for: Using data wherever GPUs are.
Visit Hammerspace →26Data orchestration & access
Alluxio
A data-access layer that caches and accelerates data from remote storage for analytics and AI.
Best for: Speeding up training reads from cloud storage.
Visit Alluxio →27Data orchestration & access
lakeFS
Git-like version control for data lakes, to branch, test and roll back datasets.
Best for: Reproducible training datasets.
Visit lakeFS →28Data orchestration & access
NVIDIA GPUDirect Storage
NVIDIA technology that lets storage transfer data directly to GPU memory, bypassing the CPU.
Best for: Cutting I/O bottlenecks to GPUs.
Visit NVIDIA GPUDirect Storage →Choosing the right approach
Match your requirement to a starting point, then compare vendors.
| Fastest training reads | A parallel file system or all-flash platform such as WEKA, DDN or VAST |
|---|---|
| Large raw datasets and checkpoints | S3-compatible object storage with a fast tier |
| Cloud training | Managed Lustre, FSx or S3 Express One Zone |
| Data spread across sites | A global namespace such as Hammerspace |
| Reproducible datasets | Data versioning such as lakeFS |
| Vector search for RAG | A vector database alongside the storage layer |
Related pages on PeterIndia.net
Neighbouring directories and hubs.
IT StorageThe storage hub.Object Storage SolutionsObject storage compared.File Storage SolutionsShared file storage.Block Storage SolutionsBlock volumes.Software-Defined StorageStorage as software.Vector DatabasesEmbedding storage for RAG.Datasets for Generative AIData to train and tune models.AI Chip ProvidersThe accelerators storage feeds.GPU Cloud ProvidersCloud GPUs for AI.Private AI InfrastructureBuild AI on your own hardware.Enterprise Storage SolutionsEnterprise arrays and services.
Frequently asked questions
Quick answers.
What is AI storage?
AI storage is storage designed for the data patterns of machine learning: very high throughput for training, low latency for inference and RAG, capacity for huge datasets and checkpoints, and support for many data types.
What makes storage AI-ready?
Look for high throughput and low latency, scale from terabytes to exabytes, automated data management and tiering, support for files, objects and other data types, and integration with frameworks such as PyTorch and TensorFlow.
Do I need a parallel file system?
Large training clusters often do, because many GPUs read data at once. Smaller teams can start with fast NAS or object storage and add a parallel file system when GPUs sit idle waiting for data.
File or object storage for AI?
Object storage is cheap and scales well for datasets and checkpoints. File systems give POSIX access and higher performance for training. Many platforms offer both, or put a fast file tier over object storage.
What is GPUDirect Storage?
It is an NVIDIA technology that moves data directly between storage and GPU memory, avoiding a copy through the CPU and reducing latency and CPU load.
Where do vector databases fit?
Vector databases store embeddings for similarity search in RAG and recommendation systems. They sit beside the main storage layer, though some platforms now offer vector features as well.
Descriptions summarise each vendor's or project's own positioning, checked on 5 October 2026. Product names and offerings change often, so confirm details on the linked site before deciding.