Neocloud storage is moving away from “ordinary cloud block volumes attached to GPU VMs” toward AI data-plane engineering: S3-compatible object storage, very fast parallel/shared filesystems, NVMe caching, RDMA fabrics, GPUDirect-style paths, and DPU/SmartNIC offload.

A useful mental model is:

Object storage is becoming the system of record; high-performance file/NVMe tiers feed the GPUs; local NVMe caches smooth bursts; RDMA/DPUs reduce CPU bottlenecks.

1. Why neocloud storage is different

Neoclouds exist because AI workloads are bottlenecked by more than GPU count. Training and inference clusters need:

Workload	Storage pressure
Dataset loading	Huge sequential reads, many workers, high fan-out
Checkpointing	Large concurrent writes without pausing training
Fine-tuning	Lots of smaller jobs, shared datasets, repeat reads
Inference	Model weight loading, KV cache pressure, vector/RAG data
Multi-tenant GPU clouds	Isolation, quotas, predictable noisy-neighbour control
Sovereign/private AI clouds	Data locality, encryption, auditability, compliance

The big shift is that storage is now treated as part of the GPU utilization stack. Bad storage means idle GPUs. Idle GPUs destroy the economics of neoclouds.

2. Object storage is becoming the default data lake

S3-compatible object storage is now central. It is used for datasets, model artefacts, checkpoints, logs, and long-term retention. Crusoe documents S3-compatible object storage for AI/ML workloads, and Nebius offers AI-focused object storage classes including an “Enhanced” class aimed at streaming data to GPUs and checkpointing.

The trend is not “object storage replaces everything.” It is:

Object storage becomes the durable source of truth, while file/NVMe/cache tiers are used to make GPUs fast.

Nebius is a good example of the direction: its Enhanced Object Storage class claims up to 2 GiB/s write throughput per GPU and positions it for GPU streaming and checkpointing. It also claims better latency and throughput versus standard object storage when bucket and client are in the same region.

Why neoclouds like object storage:

Reason	Why it matters
S3 API compatibility	Easy integration with PyTorch, Hugging Face, Spark, Ray, lakehouse tools
Scale-out metadata	Better for billions of objects than classic filesystems in some patterns
Durability	Better system-of-record semantics
Multi-region potential	Important for sovereign and multi-cloud AI
Cost tiering	Hot/warm/cold separation becomes easier
Tenant isolation	Buckets, IAM, encryption, audit trails

3. Parallel filesystems remain critical for hot AI workloads

For serious training, POSIX-like shared filesystems are still heavily used: Weka, VAST, DDN EXAScaler/Lustre, IBM Spectrum Scale/GPFS, BeeGFS, DAOS, and sometimes CephFS. These matter because many AI pipelines still expect filesystem semantics, fast directory traversal, shared mounts, and high concurrent read/write throughput.

CoreWeave publicly describes its storage as AI-focused and says it uses managed storage services with partners including VAST Data and WEKA. VAST also announced a major commercial partnership with CoreWeave, with Reuters reporting a $1.17 billion agreement for VAST to become a main data platform supporting CoreWeave’s GPU-powered cloud services.

This is the clearest signal: neoclouds are not treating storage as a commodity sidecar. They are signing huge strategic storage deals because storage is part of the AI factory.

Common hot-tier technologies:

Technology	Typical role
VAST Data	High-performance NFS/S3-ish unified AI data platform
WEKA	High-performance parallel filesystem for GPU workloads
DDN EXAScaler / Lustre	HPC-style parallel filesystem, common in supercomputing
IBM Spectrum Scale / GPFS	Enterprise/HPC parallel filesystem
BeeGFS	HPC parallel filesystem, often simpler to deploy than Lustre
DAOS	Object-oriented HPC storage, strong RDMA/NVMe direction
Ceph / CephFS / RADOSGW	Open-source block/file/object, popular where cost/control matter

The split is usually:

Tier	Storage
Ultra-hot	Local NVMe on GPU nodes
Hot shared	Weka/VAST/DDN/Lustre/GPFS/BeeGFS
Durable system of record	S3-compatible object storage
Cold/archive	Lower-cost object storage, tape, external cloud, erasure-coded pools

4. Local NVMe cache is becoming a major design pattern

A major trend is object storage plus local NVMe acceleration. Instead of forcing every GPU worker to read repeatedly from a remote shared filesystem, neoclouds cache datasets, model weights, and shards on local NVMe near the GPU.

SemiAnalysis described this emerging pattern as S3-compatible object storage paired with large distributed local NVMe caches, citing CoreWeave’s LOTA as an example.

Why this matters:

Without local cache	With local NVMe cache
Repeated remote reads hammer shared storage	Hot data stays close to GPUs
GPU nodes wait on network/storage	GPU utilization improves
Object store latency hurts training	Cache hides latency
Checkpoints overload central storage	Burst writes can be staged/absorbed
Scaling storage requires huge back-end spend	Cache distributes load across GPU fleet

This is one of the most important neocloud differentiators. Hyperscalers already built decades of object/file/cache layers. Neoclouds are now building AI-specific versions faster and with less legacy.

5. Checkpointing has become a first-class storage problem

Large model training produces enormous checkpoints. A checkpoint storm can saturate storage, networks, metadata servers, and object-store request paths.

Modern neocloud storage has to support:

Requirement	Why
High parallel write bandwidth	Thousands of GPUs checkpoint together
Low training interruption	Checkpointing must not stall expensive GPU jobs
Async/staged checkpointing	Write locally first, flush later
Incremental/delta checkpoints	Reduce write volume
Fast restore	Failed training jobs must resume quickly
Cross-region replication	Disaster recovery and customer portability

This is why high-performance object storage and parallel filesystems are both used. Object storage is durable and scalable; the hot filesystem/NVMe tier absorbs the burst.

6. RDMA, InfiniBand and RoCE are storage technologies now

In AI clusters, networking and storage are merging. Storage traffic increasingly runs over high-performance fabrics: NVIDIA Quantum InfiniBand, Spectrum-X Ethernet, RoCE, NVMe-over-Fabrics, and RDMA-aware object/file stacks.

The reason is simple: GPUs consume data at extreme rates. TCP/IP and CPU-mediated I/O paths become expensive.

Emerging direction:

Layer	Trend
Network	InfiniBand, RoCE, Spectrum-X Ethernet
Storage protocol	NVMe-oF, RDMA object/file access
Data movement	GPUDirect Storage-style paths
Offload	BlueField DPU / SmartNIC
Security	Inline encryption, tenant isolation, confidential computing

NVIDIA’s 2026 BlueField-4 STX announcement is a good example of where this is heading: storage architecture built around DPUs, ConnectX networking, RDMA, NVMe SSDs, and KV-cache/agentic-AI pressure. Reports say cloud providers including CoreWeave, Lambda, and Oracle Cloud Infrastructure are early adopters, with STX systems expected in the second half of 2026.

7. KV cache and inference storage are now separate concerns

Training storage is mostly about datasets and checkpoints. Inference storage is increasingly about:

Inference pressure	Storage impact
Huge model weights	Fast model loading and warm pools
Long context windows	KV cache can exceed GPU memory
Multi-tenant serving	Fast model swap and isolation
RAG	Vector DBs, document stores, embeddings
Agentic workflows	More intermediate state and context persistence

This means storage for neoclouds is no longer just “feed training jobs.” It is also serve inference economically.

Long-context inference creates a new tiering problem:

Tier	Used for
HBM	Active tokens, attention state
GPU memory pools	Hot model execution
Host RAM	Overflow and staging
Local NVMe	KV cache spill, model cache
Object storage	Model artefacts, datasets, logs

That is why DPU/NVMe/KV-cache work matters. It is not academic; it directly affects token throughput and cost per million tokens.

8. Storage-compute disaggregation is increasing

Classic HPC often had tightly coupled storage appliances near compute. Neoclouds increasingly want disaggregated storage:

Disaggregated model	Benefit
Compute scales independently	Add GPUs without duplicating storage
Storage scales independently	Add capacity/bandwidth separately
Better fleet utilization	Avoid stranded disks or stranded GPUs
Easier multi-tenancy	Central policy, quotas, billing
Better lifecycle management	Different refresh cycles for GPU and storage hardware

But disaggregation only works if the network is excellent. Otherwise, you simply move the bottleneck from disk to fabric.

This is why AI neoclouds pair storage disaggregation with RDMA, high-radix fabrics, telemetry, and placement-aware scheduling.

9. Open-source storage still matters, but the top end often buys commercial

For a neocloud, storage choices usually split by market segment.

Use case	Likely storage choice
Cost-sensitive GPU cloud	Ceph, MinIO, JuiceFS, BeeGFS
Sovereign/private cloud	Ceph, MinIO, NetApp, VAST, WEKA
Large training clusters	VAST, WEKA, DDN, Lustre, GPFS
Kubernetes-native AI platform	S3 + CSI volumes + cache + object gateways
HPC/AI hybrid	Lustre, GPFS, BeeGFS, DAOS
Inference platform	Object storage + local NVMe model cache + vector DB

Ceph is attractive because it gives block, file, and object in one open platform. MinIO is attractive for S3-compatible object storage. But for very large GPU clusters, commercial platforms often win because the cost of underutilized GPUs dwarfs storage licensing costs.

10. Kubernetes is shaping storage interfaces

Most neoclouds expose GPU infrastructure through Kubernetes or Kubernetes-like orchestration. That affects storage architecture.

Important pieces:

Kubernetes storage component	Role
CSI drivers	Attach block/file volumes
Object bucket claims / operators	Provision S3 buckets
Local PersistentVolumes	Use node NVMe
Topology-aware scheduling	Place pods near data/cache
RDMA device plugins	Expose high-performance fabric
Data preload jobs	Stage datasets before GPU jobs
Checkpoint controllers	Manage checkpoint lifecycle
Kubeflow/Ray/Slurm integration	AI job orchestration

The future pattern is likely not “one filesystem mounted everywhere.” It is workflow-aware storage orchestration: datasets staged, caches warmed, checkpoints flushed, model artefacts versioned, and GPU jobs scheduled based on both compute and data locality.

11. Observability for storage is becoming essential

For SREs, the storage layer needs deep telemetry. Neoclouds must know whether a training job is slow because of GPU, network, storage, framework, or tenant interference.

Metrics that matter:

Area	Metrics
GPU impact	GPU idle time due to input stalls
Filesystem	metadata ops/sec, read/write latency, throughput, queue depth
Object storage	request rate, 4xx/5xx, p99 latency, multipart throughput
NVMe	wear, temperature, IOPS, bandwidth, latency, queue depth
Network	RDMA retransmits, congestion, ECN, PFC pause frames
Checkpointing	checkpoint duration, failure rate, restore time
Cache	hit ratio, eviction rate, warm-up time
Tenant fairness	noisy neighbour detection, quota pressure

For an SRE, the winning skill is correlating GPU utilization + network fabric + storage latency + application checkpoint/data-loader behaviour.

12. Likely direction over the next 2–3 years

The strongest trends are:

S3-compatible object storage becomes the durable AI data substrate.
High-performance POSIX filesystems remain critical for hot training paths.
Local NVMe cache becomes standard on GPU nodes.
RDMA/NVMe-oF/GPUDirect/DPU offload moves into mainstream AI storage.
Checkpointing becomes a product feature, not an afterthought.
Inference storage becomes as important as training storage because of model caches, KV caches, RAG, and long context windows.
Storage scheduling becomes integrated with Kubernetes, Slurm, Ray, and AI platform layers.
Commercial AI storage vendors keep winning at the high end because GPU idle time is too expensive.
Open-source stacks like Ceph, MinIO, BeeGFS, DAOS, and JuiceFS remain important for sovereign, private, and cost-controlled neoclouds.
Storage observability becomes a differentiator for SRE/platform teams.

Bottom line

Neocloud storage is becoming a tiered AI data plane:

Cold / durable:
  S3-compatible object storage, erasure coding, replication, lifecycle policies

Warm / shared:
  High-performance object storage, lakehouse data, model artefacts

Hot / training:
  VAST, WEKA, DDN/Lustre, GPFS, BeeGFS, DAOS, CephFS

Ultra-hot / node-local:
  NVMe cache, staged datasets, checkpoint burst buffers, model cache

Fabric/offload:
  InfiniBand, RoCE, NVMe-oF, GPUDirect Storage, BlueField DPUs, SmartNICs

For SRE/platform engineering, the practical takeaway is: learn object storage deeply, learn parallel filesystems, understand NVMe/RDMA fabrics, and build observability that proves whether GPUs are waiting on storage.

What neoclouds need from storage

For neoclouds, storage has to solve several hard problems at once:

Requirement	Why it matters
Very high read throughput	Training jobs need to feed thousands of GPUs continuously
Fast checkpoint writes	Large model checkpoints can create synchronized write storms
Low metadata overhead	AI datasets can contain millions or billions of files/objects
S3 + POSIX access	AI pipelines use object APIs, filesystems, containers, notebooks, and distributed jobs
Multi-tenancy	GPU cloud customers need isolation, quotas, billing, and policy control
Data locality and caching	Model weights and datasets need to be close to compute
RDMA / GPUDirect / NVMe paths	CPU-mediated I/O can become the bottleneck
Operational observability	SREs need to prove whether GPUs are idle because of storage, network, or application issues

That is why neocloud storage is usually a tiered AI data plane rather than one generic SAN/NAS array.

1. VAST Data

What VAST is

VAST Data is one of the most visible AI-era storage companies. It positions itself not merely as storage, but as a broader AI data platform combining storage, database-like services, global namespace, and data services. Its architecture is heavily aimed at exabyte-scale unstructured data, AI training/inference pipelines, GPU clouds, and large shared datasets.

VAST has strong neocloud credibility because CoreWeave uses VAST as a major data platform; Reuters reported a $1.17 billion VAST-CoreWeave commercial agreement, with VAST supporting CoreWeave’s GPU-powered cloud services for training and running AI models.

Relevant technologies

VAST’s platform is especially relevant to neoclouds because it tries to collapse several separate storage roles into one platform:

Area	VAST approach
File storage	High-performance NFS-style shared access
Object storage	S3-compatible access for AI data lakes and cloud-native workflows
Namespace	Global namespace for distributed datasets
Data services	Data platform features beyond basic storage
AI use case	Shared data layer for training, inference, RAG, and multi-tenant GPU clouds

VAST explicitly markets its platform for AI clouds and service providers, saying its platform consolidates storage, database, and global namespace capabilities for service-provider productization.

Why it suits neoclouds

VAST is attractive when a neocloud wants:

A single high-performance unstructured data platform rather than separate NAS, object store, metadata store, and data-service islands.
A platform that can support both training and inference data flows.
Multi-tenant AI cloud storage with service-provider features.
Global-scale datasets, data sharing, and data mobility.

Strengths

Strength	Why it matters
Strong AI-cloud market fit	Built around large-scale AI data rather than generic enterprise NAS alone
Unified file/object story	Useful because AI workflows often mix POSIX and S3
Strong CoreWeave validation	Neocloud adoption is a major signal
Global namespace / data platform direction	Useful for distributed GPU clouds
All-flash performance orientation	Good for hot AI datasets

Watch-outs

VAST is powerful but not necessarily the cheapest or simplest. It is best suited to large-scale AI environments where GPU economics justify premium storage. For small clusters or cost-sensitive internal platforms, Ceph, MinIO, BeeGFS, or simpler NAS/object storage may be more appropriate.

Neocloud fit

Very strong fit for GPU cloud providers, AI factories, large-scale model training, RAG platforms, and shared AI data services.

2. DDN

What DDN is

DDN is a long-standing HPC and AI storage specialist. It is deeply associated with supercomputing, Lustre, parallel filesystems, large research systems, national labs, and NVIDIA DGX-oriented AI infrastructure.

For neoclouds, DDN is important because it represents the HPC-derived high-performance storage path: extreme throughput, parallel file access, fast checkpointing, and close alignment with GPU supercomputing.

Relevant technologies

DDN’s AI portfolio includes systems such as the AI400X2 Turbo and its EXAScaler/Lustre-based AI storage platforms. DDN states that the AI400X2 Turbo can deliver up to 115 GB/s read, 75 GB/s write, and 3 million IOPS for large AI workloads.

DDN has also described previous AI400X2 systems delivering more than 90 GB/s and 3 million IOPS to an NVIDIA DGX A100 system, with all-NVMe usable capacity options.

Why it suits neoclouds

DDN fits neoclouds that look more like GPU supercomputers as a service than conventional cloud file services.

Use case	DDN suitability
Large model training	Very strong
Checkpoint-heavy workloads	Very strong
HPC + AI convergence	Very strong
DGX SuperPOD / BasePOD style clusters	Strong
Research AI clusters	Strong
Generic enterprise file sharing	Less differentiated

Strengths

Strength	Why it matters
HPC heritage	Mature for large parallel workloads
Lustre / EXAScaler expertise	Well suited to training and checkpointing
NVIDIA AI infrastructure alignment	Important for DGX-style deployments
Very high throughput appliances	Directly addresses GPU starvation
Proven in supercomputing	Good for national-lab and research-scale environments

Watch-outs

DDN can feel more like HPC infrastructure than cloud-native storage. For neoclouds serving many different customers, additional layers may be needed for S3 abstraction, self-service provisioning, tenant controls, Kubernetes integration, and cloud-style billing.

Neocloud fit

Excellent fit for the hot training tier, checkpoint tier, and HPC/AI supercomputing-style neoclouds.

3. WEKA

What WEKA is

WEKA is a software-defined, high-performance data platform aimed at AI, ML, HPC, and cloud-native data-intensive workloads. Unlike DDN’s stronger appliance/HPC feel, WEKA is often positioned as a more cloud-like, software-defined parallel filesystem/data platform.

WEKA says its AI/ML platform can run the entire AI data pipeline on one platform, on-premises or in public cloud, and can combine multiple sources into a single high-performance computing system.

WEKA also states that its Data Platform is certified as a high-performance data-store solution for NVIDIA Cloud Partners, supporting large-scale AI deployments with high throughput and scalability.

Relevant technologies

Area	WEKA approach
Core architecture	Distributed, software-defined high-performance filesystem
Deployment	On-prem, cloud, hybrid
AI use case	Training, inference, data pipelines, HPC/AI convergence
GPU cloud angle	NVIDIA Cloud Partner certification
Data access	High-performance file access, cloud integration, tiering patterns

Reuters reported that WEKA raised $140 million in a Series E round in 2024 at a $1.6 billion valuation, with participation from NVIDIA and Qualcomm Ventures, and described WEKA as providing high-performance and scalable file storage for data-intensive applications.

Why it suits neoclouds

WEKA is particularly interesting for neoclouds because it is:

Software-defined, which suits cloud-style automation.
Strong on parallel file performance.
Designed for hybrid cloud and cloud-native data workflows.
Less tied to one hardware appliance model than some traditional storage systems.
Attractive where a neocloud wants high performance but also elasticity and automation.

Strengths

Strength	Why it matters
Software-defined architecture	Easier to automate and integrate with cloud platforms
Strong AI/HPC performance story	Good for feeding GPUs
Hybrid/on-prem/cloud positioning	Useful for neoclouds spanning sites
NVIDIA Cloud Partner certification	Strong signal for GPU-cloud relevance
Pipeline-oriented messaging	Useful for MLOps and AI data workflows

Watch-outs

WEKA is still a specialized platform. Teams need to understand its deployment, networking, failure domains, tiering, and cost model. It is not simply a drop-in replacement for an enterprise NAS if the workload is generic office file sharing.

Neocloud fit

Very strong fit for cloud-native AI storage, training clusters, hybrid AI infrastructure, and service-provider GPU clouds.

4. Pure Storage

What Pure is

Pure Storage is a major all-flash enterprise storage company. For neoclouds, the relevant products are less about traditional block storage and more about FlashBlade, AIRI, and AI-ready data platforms.

Pure describes FlashBlade as a scale-out platform for both file and object storage, intended for unstructured data.

Pure also markets AIRI as a pre-certified NVIDIA DGX BasePOD stack using FlashBlade, aimed at accelerating enterprise AI deployment and improving GPU utilization.

Relevant technologies

Area	Pure approach
File/object storage	FlashBlade
AI integrated stack	AIRI with NVIDIA DGX BasePOD
Enterprise consumption	Evergreen / as-a-service style models
Kubernetes	Portworx for cloud-native storage
Enterprise AI	Validated AI infrastructure rather than pure HPC

Why it suits neoclouds

Pure is attractive where the neocloud or private AI cloud wants:

Enterprise-grade all-flash storage.
Strong supportability and lifecycle management.
Unified file/object for AI datasets.
Validated NVIDIA AI infrastructure.
Kubernetes storage through Portworx.
Simpler operations than more HPC-centric stacks.

Strengths

Strength	Why it matters
Operational simplicity	Pure is known for manageability
All-flash performance	Good for AI hot data
FlashBlade file/object	Useful for unstructured AI datasets
AIRI / NVIDIA validation	Useful for enterprise AI stacks
Portworx	Stronger Kubernetes story than many array vendors
Evergreen model	Attractive for lifecycle management

Watch-outs

Pure’s sweet spot is often enterprise AI infrastructure rather than the very largest hyperscale/neocloud training fabrics. It can absolutely support AI workloads, but at very large neocloud scale, vendors like VAST, WEKA, and DDN may be more directly associated with GPU cloud hot-path storage.

Neocloud fit

Strong fit for enterprise neoclouds, private AI clouds, AI inference platforms, Kubernetes AI platforms, and medium-to-large GPU clusters.

5. Qumulo

What Qumulo is

Qumulo is a scale-out file storage company focused on unstructured data, cloud file storage, and hybrid/multi-cloud data access. It is not traditionally as HPC-heavy as DDN or as AI-cloud famous as VAST, but it has an increasingly relevant story for AI data mobility.

Qumulo says its Data Platform helps make billions of files accessible to AI workflows on-premises or in cloud, without copying or migrating data, and supports running AI workloads wherever compute is available across AWS, Azure, GCP, OCI, or on-premises.

Relevant technologies

Area	Qumulo approach
Core platform	Scale-out file storage
Cloud model	Cloud-native and hybrid file services
Data mobility	Access data where GPU compute exists
AI/ML use case	Training, inference, GPU workflows
Differentiator	Multi-cloud file fabric / cloud file access

Qumulo also markets high-performance cloud file storage for demanding cloud-based workflows.

Why it suits neoclouds

Qumulo is most interesting when the problem is:

“My data is in one place, but GPU capacity is somewhere else.”

That is an increasingly common neocloud problem. GPU liquidity means customers may want to run jobs wherever GPUs are available, but data gravity makes that hard.

Strengths

Strength	Why it matters
Strong cloud file story	Useful for hybrid and multi-cloud AI
Unstructured-data focus	AI datasets are often unstructured
Data mobility positioning	Good for GPU capacity arbitrage
Simpler than HPC Lustre-style systems	Easier for enterprise teams
Cloud deployment options	Useful for burst and hybrid workflows

Watch-outs

Qumulo is generally less associated with the absolute highest-end model-training hot path than DDN, VAST, or WEKA. For large synchronous training jobs, you would need to validate throughput, metadata, GPU utilization, and checkpoint behavior carefully.

Neocloud fit

Good fit for hybrid AI, cloud file services, inference/RAG data access, and enterprise AI workflows. Less obviously the first choice for the largest frontier-model training tier.

Specialist comparison: VAST vs DDN vs WEKA vs Pure vs Qumulo

Vendor	Best described as	Strongest neocloud role
VAST	AI data platform / unified file-object-global namespace	Large GPU clouds, shared AI data platform, training + inference
DDN	HPC/AI parallel storage specialist	Extreme training throughput, checkpoints, DGX/SuperPOD-style systems
WEKA	Software-defined high-performance AI filesystem/data platform	Cloud-native AI storage, hybrid training, scalable GPU clouds
Pure	Enterprise all-flash file/object + AI integrated stacks	Enterprise AI clouds, private AI, Kubernetes AI, validated DGX stacks
Qumulo	Scale-out cloud file platform	Hybrid/multi-cloud AI data access, unstructured AI data, GPU liquidity

Simplified ranking by neocloud use case

Use case	Strongest candidates
Frontier-scale training	DDN, VAST, WEKA
GPU cloud shared data platform	VAST, WEKA
DGX/SuperPOD-style HPC AI	DDN, IBM, Dell, Pure, NetApp depending on architecture
Enterprise private AI cloud	Pure, NetApp, Dell, IBM, VAST, WEKA
Kubernetes-heavy AI platform	Pure/Portworx, WEKA, NetApp, Dell, VAST
Hybrid/multi-cloud file access	Qumulo, NetApp, WEKA, VAST
RAG / inference data serving	VAST, Pure, Qumulo, NetApp, WEKA
Open HPC-style AI	DDN/Lustre, HPE ClusterStor, IBM Storage Scale

Now compare with standard storage companies

6. Dell Technologies

What Dell offers

Dell has one of the broadest enterprise infrastructure portfolios: servers, networking, storage, data protection, and AI reference architectures. For AI/neocloud storage, the most relevant product is usually PowerScale, Dell’s scale-out NAS platform based on the Isilon lineage.

Dell has a PowerScale reference architecture for NVIDIA DGX SuperPOD aimed at high-performance scale-out AI enterprise environments.

Dell also says PowerScale introduced GPUDirect Storage and NFS over RDMA capabilities in earlier AI work, and that the PowerScale F710 became the first Ethernet-based storage certified for NVIDIA DGX SuperPOD.

Strengths

Strength	Why it matters
Broad enterprise footprint	Many customers already buy Dell infrastructure
PowerScale maturity	Proven scale-out NAS
NVIDIA AI Factory alignment	Easier procurement for enterprise AI
End-to-end stack	Servers, storage, networking, services
Good for enterprise standardization	Procurement and support are straightforward

Weaknesses versus specialists

Dell can be very strong for enterprise AI, but it may feel less AI-native than VAST or WEKA and less HPC-specialized than DDN. Its advantage is breadth, support, and integration; its disadvantage is that neoclouds may want more specialized storage economics, performance models, or cloud-native multi-tenant features.

Neocloud fit

Strong for enterprise AI factories and private AI clouds. For a pure-play neocloud, Dell can be part of the stack, but the hot AI storage layer may still be evaluated against VAST, WEKA, DDN, or Pure.

7. HPE

What HPE offers

HPE’s strongest AI/HPC storage story comes from the Cray acquisition and the Cray ClusterStor line. ClusterStor E1000 embeds the open-source Lustre parallel filesystem and is designed for HPC-style performance.

That makes HPE very relevant where neoclouds look like AI supercomputers, especially when paired with HPE Cray compute, Slingshot networking, and HPC operating models.

Strengths

Strength	Why it matters
Cray/HPC heritage	Very strong for supercomputing-style AI
Lustre-based architecture	Well understood in HPC training/checkpoint workloads
Large-scale systems expertise	Suitable for national-lab and research-scale AI
Full HPC stack	Compute, networking, storage, services
Enterprise support for open HPC tech	Easier than self-supporting Lustre

Weaknesses versus specialists

HPE ClusterStor is excellent for HPC-style AI, but it is not necessarily the easiest platform for a cloud-native multi-tenant neocloud. It may need additional layers for S3 workflows, self-service storage provisioning, Kubernetes-native integration, billing, and customer isolation.

Neocloud fit

Strong for AI supercomputing and HPC-AI clouds. Less obviously ideal for a general-purpose GPU neocloud where customers expect cloud-native object/file abstractions and rapid self-service.

8. IBM

What IBM offers

IBM’s key product is IBM Storage Scale, formerly GPFS. This is one of the most mature parallel filesystems in the world and is heavily used in HPC, research, analytics, and enterprise high-performance data environments.

IBM positions Storage Scale with NVIDIA as an integrated solution for enterprise AI applications at scale, and IBM lists reference architectures for NVIDIA HGX, GB200/GB300 NVL72, DGX BasePOD, and DGX SuperPOD.

IBM also describes the Storage Scale System 6000 AI Data Platform as delivering massive throughput with integrated GPU acceleration and content-aware storage.

Strengths

Strength	Why it matters
GPFS / Storage Scale maturity	Very strong for parallel file workloads
Enterprise and HPC credibility	Works in serious regulated and research environments
NVIDIA reference architectures	Relevant to GPU clusters
Multi-protocol and data-management features	Useful in enterprise AI
Strong metadata and policy capabilities	Important for large datasets

Weaknesses versus specialists

IBM Storage Scale is powerful but can be complex. It may require deep skills to operate well. Compared with VAST or WEKA, it can feel more traditional/HPC-enterprise than AI-cloud-native. Compared with DDN, it is less specifically a turnkey Lustre appliance model.

Neocloud fit

Strong for enterprise/HPC AI platforms, regulated AI environments, and large shared filesystems. Good fit where operational maturity exists.

9. NetApp

What NetApp offers

NetApp is a major enterprise storage incumbent with ONTAP, AFF, StorageGRID, Cloud Volumes, Astra/Trident, and AI reference architectures. For AI, NetApp markets AIPod reference architectures and high-performance storage platforms for AI/ML workloads.

NetApp’s strength is not only performance; it is also enterprise data management: snapshots, replication, tiering, governance, cloud integration, and mature NAS/SAN operations.

Strengths

Strength	Why it matters
Enterprise NAS maturity	Many organizations already trust NetApp
ONTAP features	Snapshots, replication, policy, multiprotocol access
Strong hybrid-cloud story	Good for enterprise AI data mobility
Kubernetes integration	Trident/Astra ecosystem
AIPod architectures	Validated AI stack approach
StorageGRID object storage	Useful for object/data-lake layer

Weaknesses versus specialists

NetApp is strong in enterprise AI, but for pure GPU-cloud hot-path storage it may face tough competition from VAST, WEKA, DDN, and Pure. Its strongest argument is enterprise integration and governance rather than being the most AI-native scale-out training filesystem.

Neocloud fit

Strong for enterprise private AI, regulated environments, hybrid cloud, RAG, and data management. Less clearly the first choice for maximum-throughput frontier-model training.

Specialist vendors vs traditional vendors

High-level comparison

Dimension	VAST / DDN / WEKA / Pure / Qumulo	Dell / HPE / IBM / NetApp
Market posture	AI-forward or specialist storage	Broad enterprise/HPC incumbents
Neocloud messaging	Stronger for VAST, WEKA, DDN; growing for Pure/Qumulo	Strong but often under “AI factory” or enterprise AI
Procurement	More specialized	Easier for enterprise standardization
Operations	Can be highly specialized	More familiar to enterprise infra teams
Performance focus	Often optimized for GPU data paths	Varies: very strong in HPC products, broader in enterprise portfolios
Cloud-native fit	WEKA, VAST, Qumulo, Pure/Portworx strong	NetApp/Dell/Pure strong enterprise Kubernetes stories; HPE/IBM more HPC-enterprise
Multi-tenancy	Stronger in AI-cloud platforms, but varies	Often needs enterprise/cloud management wrappers
Best use	AI factories, GPU clouds, hot training tiers	Enterprise AI, HPC systems, validated infrastructure, regulated environments

The main architectural difference

The specialist vendors tend to start from this problem:

“How do we feed and protect GPU workloads at massive scale?”

The incumbents often start from this problem:

“How do we extend proven enterprise/HPC storage into AI infrastructure?”

Both are valid. The right answer depends on whether the neocloud is optimizing for maximum GPU utilization, enterprise governance, cost, cloud-native operations, or HPC-style throughput.

Where each company fits in a neocloud architecture

Durable object layer

Best candidates
VAST, Pure FlashBlade, NetApp StorageGRID, Dell object options, MinIO/Ceph alternatives, Qumulo where file-first access dominates

For a neocloud, object storage is usually the system of record for datasets, model artefacts, logs, and checkpoints.

Hot training filesystem

Best candidates
DDN, WEKA, VAST, IBM Storage Scale, HPE ClusterStor, Dell PowerScale, Pure FlashBlade, NetApp AFF/AIPod depending on workload

This is the tier that decides whether GPUs sit idle.

Checkpoint tier

Best candidates
DDN, WEKA, VAST, HPE ClusterStor, IBM Storage Scale, Dell PowerScale, Pure FlashBlade

Checkpointing is especially hard because many workers write at once. You want high write bandwidth, good metadata behavior, and fast restore.

Inference/model-serving tier

Best candidates
VAST, Pure, Qumulo, NetApp, WEKA, Dell PowerScale

Inference storage is increasingly about model-weight caching, RAG/vector data, document stores, and KV-cache spill/adjacent storage.

Hybrid/multi-cloud AI data fabric

Best candidates
Qumulo, NetApp, WEKA, VAST, Pure, Dell

This matters when GPU capacity is distributed and customers want to run workloads wherever GPUs are available.

Practical decision matrix

Scenario	Best short-list
Building a CoreWeave-style GPU neocloud	VAST, WEKA, DDN
Building a DGX SuperPOD-style training cluster	DDN, IBM Storage Scale, Dell PowerScale, Pure, NetApp, HPE ClusterStor
Building an HPC/AI research cloud	DDN, HPE ClusterStor, IBM Storage Scale, WEKA, VAST
Building an enterprise private AI cloud	Pure, NetApp, Dell, IBM, VAST
Building cloud-native Kubernetes AI services	WEKA, Pure/Portworx, NetApp/Trident, VAST, Qumulo
Building hybrid AI data access across public clouds	Qumulo, NetApp, WEKA, VAST
Cost-controlled internal GPU platform	Ceph, MinIO, BeeGFS, plus selected commercial tier if needed
Maximum raw checkpoint/training performance	DDN, WEKA, VAST, HPE ClusterStor, IBM Storage Scale

SRE/platform engineering view

For an SRE, the important thing is not the logo on the array. It is whether the storage platform exposes the right primitives and telemetry.

What to evaluate in a proof of concept

Area	What to test
GPU utilization	Does storage keep GPUs above target utilization?
Read throughput	Can it sustain distributed dataloader reads?
Write throughput	Can it handle synchronized checkpoints?
Metadata	Does performance collapse with millions/billions of files?
Small files	Can it handle image/text/token shards efficiently?
Object API	Does S3 behavior work with ML tooling?
POSIX semantics	Does NFS/POSIX behavior match frameworks?
Failure recovery	What happens during node, disk, controller, or network loss?
Multi-tenancy	Quotas, isolation, noisy-neighbour handling
Kubernetes integration	CSI, topology awareness, dynamic provisioning
Observability	Prometheus metrics, logs, tracing, audit events
Network behavior	RDMA errors, ECN/PFC, retransmits, queue depth
Cost model	Cost per usable TB, cost per GB/s, cost per GPU kept busy

Metrics you should demand

Metric category	Examples
GPU correlation	GPU idle time due to input stalls
Filesystem	p95/p99 latency, throughput, metadata ops/sec
Object storage	S3 request rate, p95/p99 latency, multipart failures
Checkpoints	checkpoint duration, restore time, failure rate
Cache	hit ratio, eviction rate, warm-up time
NVMe	wear, queue depth, bandwidth, latency
RDMA/network	congestion, retransmits, packet drops, PFC pause frames
Tenant fairness	per-tenant throughput, throttling, noisy-neighbour impact

Bottom line

For neoclouds, I would group the vendors like this:

Category	Vendors	Interpretation
AI-cloud specialists	VAST, WEKA	Strongest “modern AI data platform” positioning
HPC/AI performance specialists	DDN, HPE ClusterStor, IBM Storage Scale	Strongest for supercomputer-like training and checkpointing
Enterprise AI all-flash platforms	Pure, Dell, NetApp	Strong for private AI, validated stacks, enterprise operations
Hybrid/cloud file specialists	Qumulo, NetApp, WEKA	Strong where data mobility and multi-cloud GPU access matter
Broad incumbents	Dell, HPE, IBM, NetApp	Strongest when enterprise support, procurement, and full-stack integration matter

My practical shortlist would be:

VAST if building a large AI-cloud data platform.
DDN if the priority is maximum training/checkpoint performance with HPC-style operations.
WEKA if you want software-defined, high-performance AI storage with cloud-like flexibility.
Pure if you want enterprise AI simplicity, all-flash file/object, and Kubernetes/Portworx options.
Qumulo if the key challenge is hybrid/multi-cloud file access and moving AI workloads to wherever GPU capacity exists.
Dell / HPE / IBM / NetApp if you need enterprise procurement, validated reference architectures, broad support, and integration with existing infrastructure standards.

For neocloud storage, the open-source products are mostly used in four layers:

Layer	Open-source products
Object storage / S3 layer	Ceph RGW, MinIO
Parallel training filesystem	Lustre, BeeGFS, DAOS
Cloud-native POSIX-over-object layer	JuiceFS
Kubernetes persistent storage	Longhorn, OpenEBS, Rook-Ceph

The main difference versus VAST, DDN, WEKA, Pure, Qumulo, Dell, HPE, IBM, and NetApp is that open-source storage usually gives you control, flexibility, and cost leverage, but you take on more integration, tuning, operational risk, and support responsibility.

1. Ceph

Ceph is the broadest open-source storage platform in this list. It provides object, block, and file storage from one distributed cluster built on commodity hardware. The core storage layer is RADOS; on top of that you typically use RADOS Gateway for S3-compatible object storage, RBD for block volumes, and CephFS for POSIX-style shared file storage. The Ceph project describes it as an open-source distributed storage system providing unified object, block, and file services.

Where it fits in a neocloud

Ceph is attractive as a general-purpose storage substrate for neoclouds and private AI clouds:

Ceph interface	Neocloud use
RGW / S3	Dataset buckets, model artefacts, checkpoint archive, logs
RBD	VM volumes, Kubernetes PersistentVolumes, control-plane storage
CephFS	Shared filesystems for tools, notebooks, pipelines, moderate AI jobs
Rook-Ceph	Kubernetes-native Ceph deployment and management

Likely consumers

Ceph is likely to be used by:

Consumer	Why they would use Ceph
Cost-sensitive neoclouds	Avoid proprietary array/platform costs
Sovereign/private cloud operators	Full control over hardware, data locality, encryption, operations
OpenStack clouds	Ceph is a common backend for Cinder, Glance, Nova ephemeral disks, and object storage
Kubernetes platform teams	Rook-Ceph gives block, file, and object in-cluster
Universities/research clouds	Commodity hardware + open platform fits constrained budgets
MSPs and regional clouds	They can build S3/block/file services without hyperscaler dependency

Pros

Pro	Why it matters
Unified block/file/object	One storage platform can support many cloud primitives
Commodity hardware	Good for cost control and sovereignty
Mature ecosystem	Widely deployed in OpenStack, Kubernetes, and private cloud
S3-compatible object via RGW	Useful for AI datasets and model artefacts
Strong failure-domain controls	CRUSH maps allow rack/host/device-aware placement
Erasure coding	Useful for capacity-efficient object/archive tiers
Rook integration	Strong Kubernetes deployment model

Cons

Con	Why it matters
Operational complexity	Ceph rewards expertise; poor design causes pain
Performance tuning burden	Network, OSD layout, BlueStore, DB/WAL, replication, EC pools all matter
Not automatically a high-end AI filesystem	CephFS is useful, but not usually the first choice for extreme GPU training hot paths
Hardware-sensitive	Mixed disks, weak networks, or poor failure domains cause instability
Upgrades require discipline	Large clusters need careful version, PG, and recovery management
Metadata-heavy workloads can hurt	Billions of tiny files or hot directory patterns need careful design

Neocloud verdict

Ceph is excellent for the durable storage core of a private/neocloud platform: object, block, VM volumes, Kubernetes PVs, and archive. It is less likely to be the absolute fastest hot training filesystem compared with DDN/Lustre, WEKA, VAST, or BeeGFS.

2. MinIO

MinIO is an S3-compatible object store focused on performance, simplicity, and cloud-native deployment. The MinIO GitHub project describes it as a high-performance S3-compatible object storage solution released under the GNU AGPL v3.0 licence, designed for AI/ML, analytics, and data-intensive workloads.

Where it fits in a neocloud

MinIO fits the object storage / AI data lake layer:

Use	MinIO fit
Model artefact storage	Strong
Dataset buckets	Strong
Checkpoint archive	Strong
Loki/Mimir/Tempo-style object backend	Strong
Kubernetes-native S3 service	Strong
POSIX shared training filesystem	Not its role

Likely consumers

Consumer	Why
Kubernetes platform teams	Easy to deploy as S3-compatible object storage
AI/ML platform teams	Familiar S3 API for datasets and artefacts
Smaller neoclouds	Simpler than building full Ceph object initially
SaaS/platform teams	Embedded object storage for internal services
Labs and homelabs	Lightweight way to learn S3-style infrastructure
Edge/private AI deployments	Compact S3 layer close to compute

Pros

Pro	Why it matters
S3 API focus	Works with common ML, data, backup, and observability tools
Operational simplicity	Easier mental model than full Ceph
Kubernetes-friendly	Common in Helm/operator-based deployments
High-performance object store	Good for object-native AI pipelines
Good developer experience	`mc` client, simple bucket model, familiar S3 semantics
Useful for observability stacks	Common backend for Loki, Mimir, Tempo, Thanos-style systems

Cons

Con	Why it matters
Object only	No native block or POSIX filesystem role
AGPL considerations	Commercial/service-provider use needs legal review and possibly subscription planning
Not a parallel filesystem	Training code expecting POSIX/NFS/Lustre semantics needs another layer
Metadata/object pattern matters	Lots of tiny objects or poor multipart usage can become inefficient
Multi-tenant cloud service design is on you	IAM, quotas, chargeback, isolation, and lifecycle policy need careful platform work
Enterprise support may be required	For production neocloud use, support/subscription questions matter

Neocloud verdict

MinIO is a strong choice for the S3-compatible data lake/system-of-record tier, especially in Kubernetes and private AI environments. Pair it with a hot filesystem or NVMe cache layer for GPU training.

3. Lustre

Lustre is the classic open-source parallel filesystem for HPC. The Lustre project describes it as an open-source parallel filesystem for leadership-class HPC simulation environments. OpenSFS describes Lustre as POSIX-compliant, scalable to thousands of clients, hundreds of petabytes, and several TB/s of sustained I/O bandwidth, with broad use in major supercomputing sites.

Where it fits in a neocloud

Lustre belongs in the hot training / checkpoint tier:

Use	Lustre fit
Large distributed training reads	Very strong
Checkpoint-heavy workloads	Very strong
HPC + AI clusters	Very strong
Scratch filesystem	Very strong
Long-term object archive	Not its primary role
Cloud-style S3 service	Needs additional layer

Likely consumers

Consumer	Why
HPC centres	Existing Lustre skills and workflows
National labs and universities	Proven at large scale
AI research clusters	High-throughput POSIX filesystem for training
Neoclouds with HPC DNA	Good fit for GPU supercomputing-as-a-service
Weather, simulation, genomics, physics groups	Traditional parallel I/O workloads
DDN/HPE-style deployments	Commercial Lustre appliances often wrap open Lustre

Pros

Pro	Why it matters
Extreme parallel throughput	Feeds many GPU/CPU clients
Mature HPC ecosystem	Known by schedulers, MPI users, HPC admins
POSIX/global namespace	Works with legacy scientific/AI workflows
Strong for large sequential I/O	Good for sharded datasets and checkpoints
Commercial support ecosystem	DDN, HPE, Whamcloud/OpenSFS ecosystem
Proven at supercomputer scale	Important for trust in extreme workloads

Cons

Con	Why it matters
Operationally specialized	Requires Lustre/HPC storage expertise
Not cloud-native by default	Self-service, quotas, S3, Kubernetes integration need extra work
Metadata bottlenecks possible	Small-file workloads need careful MDT design
Failure handling requires expertise	OST/MDT recovery, networking, failover need discipline
Tenant isolation is not automatic	Neocloud multi-tenancy needs wrapping layers
Less natural for object-native data lakes	Often paired with S3/object storage rather than replacing it

Neocloud verdict

Lustre is one of the strongest open-source choices for maximum hot-tier AI training and checkpoint performance, especially where the neocloud behaves like an HPC GPU supercomputer.

4. BeeGFS

BeeGFS is another parallel cluster filesystem designed for performance and ease of deployment. Its GitHub README describes it as a parallel cluster filesystem focused on performance and designed for easy installation and management. BeeGFS markets itself for large-scale HPC and AI clusters.

One important caveat: the current BeeGFS licensing model has evolved. BeeGFS says its Community licence allows use as a high-performance scratch filesystem, access to source code, and internal modification, while defining boundaries for fair use. So it is “source-available/open community” in practice, but you should review the licence carefully for commercial neocloud service-provider use.

Where it fits in a neocloud

Use	BeeGFS fit
AI scratch filesystem	Strong
HPC/AI shared filesystem	Strong
GPU cluster shared training data	Strong
Easier parallel FS deployment than Lustre	Often a strength
Object storage system of record	Not its main role
Enterprise multi-tenant cloud storage	Needs platform wrapping

Likely consumers

Consumer	Why
Universities and research labs	Performance without full Lustre complexity
Smaller HPC/AI clusters	Easier to deploy and operate
AI teams needing scratch/shared POSIX	Good practical shared filesystem
Sovereign AI clouds	More control over stack
Platform teams prototyping AI hot tiers	Faster path than complex HPC appliances
Specialist MSPs	Can build custom high-performance storage services

Pros

Pro	Why it matters
Easier than Lustre for many teams	Lower operational entry barrier
Good performance for HPC/AI	Suitable hot shared tier
Flexible hardware choices	Can run on commodity servers/NVMe
Good for scratch workloads	Matches many training/intermediate-data patterns
Familiar POSIX-style access	Easy for users and frameworks
Good fit for medium-scale clusters	Strong balance of performance and manageability

Cons

Con	Why it matters
Licence/commercial-use review needed	Important for neoclouds selling services
Smaller ecosystem than Lustre	Fewer very-large reference architectures
Not object-native	Needs S3/object tier alongside it
Needs tuning	Network, metadata, chunking, client config matter
Multi-tenancy is not turnkey	Quotas, customer isolation, chargeback need extra layers
Less vendor gravity than DDN/VAST/WEKA	May be harder to get enterprise confidence

Neocloud verdict

BeeGFS is attractive for medium-to-large AI/HPC scratch and shared file tiers, especially where Lustre feels too heavy and commercial platforms are too expensive.

5. DAOS

DAOS, Distributed Asynchronous Object Storage, is an open-source software-defined high-performance storage system for AI and HPC workloads. The DAOS project describes it as an open-source platform for AI and HPC. Its GitHub repository describes DAOS as an open-source software-defined object store designed for massively distributed non-volatile memory, licensed under BSD-2-Clause Plus Patent License.

Where it fits in a neocloud

DAOS is best seen as a next-generation HPC/AI object storage layer, not as ordinary S3 object storage.

Use	DAOS fit
Extreme HPC/AI I/O	Strong
NVMe-heavy storage pools	Strong
Scientific workflows	Strong
Object-native high-performance workloads	Strong
POSIX compatibility via FUSE	Possible, but not the core ideal
Simple S3-compatible cloud storage	Not the obvious choice

Likely consumers

Consumer	Why
Exascale/HPC centres	Designed for high-performance object I/O
National labs	Strong fit for scientific computing
Weather/simulation/analytics platforms	Can suit high-throughput structured I/O
Advanced AI research infrastructure	Interesting for metadata-heavy/high-performance data paths
Storage R&D teams	Architecture is advanced and worth evaluating
Cloud providers with deep storage engineering	Can build differentiated services, but needs skill

Pros

Pro	Why it matters
Designed for high-performance object I/O	Avoids some traditional POSIX bottlenecks
NVMe/NVM-oriented architecture	Good match for modern flash-heavy clusters
Open governance direction via DAOS Foundation	Better long-term ecosystem prospects
Multiple access interfaces	Native APIs, POSIX/FUSE-style compatibility options
Strong HPC/AI ambition	Relevant to future AI storage designs
Potentially excellent metadata behavior	Important for complex scientific/AI workloads

Cons

Con	Why it matters
More specialized and less mainstream	Harder hiring/support than Ceph or Lustre
Application model matters	Best performance may require DAOS-aware software
Not a generic enterprise NAS/S3 replacement	Needs careful workload matching
Operational maturity varies by environment	Requires skilled engineering
Smaller ecosystem	Fewer off-the-shelf integrations than S3/POSIX stacks
Migration path may be harder	Existing apps usually expect POSIX, S3, or NFS

Neocloud verdict

DAOS is interesting for advanced HPC/AI storage engineering, but it is less likely to be the first generic storage choice for a commercial neocloud unless the team has deep HPC/storage expertise.

6. JuiceFS

JuiceFS is an open-source distributed POSIX filesystem built on object storage plus a separate metadata engine. Its documentation describes it as an open-source, high-performance distributed filesystem under Apache 2.0, providing full POSIX compatibility and allowing object storage to be mounted like a massive local disk across hosts, platforms, and regions. JuiceFS stores file data in object storage and metadata separately in engines such as Redis, PostgreSQL, MySQL, or similar systems.

Where it fits in a neocloud

JuiceFS is a bridge between object storage and POSIX workflows.

Use	JuiceFS fit
POSIX access over S3/object storage	Strong
Cloud-native shared filesystem	Strong
Hybrid/multi-cloud data access	Strong
ML datasets stored in object storage	Strong
Extreme hot training filesystem	Depends heavily on cache, metadata, and workload
Replacement for Lustre at frontier scale	Usually not the first assumption

Likely consumers

Consumer	Why
AI platform teams using S3	Gives POSIX mounts over object storage
Kubernetes-heavy teams	Can mount shared data into pods
Hybrid cloud users	Object backend can be cloud or private S3
Data science teams	Familiar file semantics over object data
Cost-sensitive private AI clouds	Avoids premium commercial filesystem
Platform teams needing simple global data access	Useful abstraction layer

Pros

Pro	Why it matters
POSIX over object storage	Useful where apps are not object-native
Cloud-native architecture	Good fit for Kubernetes and hybrid cloud
Apache 2.0 licence	Easier for commercial use than copyleft/open-core concerns
Works with many object stores	S3, MinIO, Ceph RGW, public cloud object stores
Separate metadata layer	Can be fast if metadata engine is designed well
Good for multi-cloud data workflows	Object storage backend gives portability

Cons

Con	Why it matters
Metadata engine becomes critical	Redis/Postgres/MySQL/TiKV availability and performance matter
FUSE overhead	May not match kernel-native/parallel FS performance in hot paths
Cache design is essential	Without local cache, object latency hurts
Consistency and semantics need validation	POSIX-over-object is not identical to local filesystem behavior under all workloads
Not automatically suitable for checkpoint storms	Needs testing under real AI write patterns
Adds another moving part	Object store + metadata DB + clients + cache

Neocloud verdict

JuiceFS is a very useful cloud-native POSIX compatibility layer over object storage, especially for AI platforms that already use S3/MinIO/Ceph. It is not automatically a replacement for DDN/Lustre/WEKA/VAST in the hottest training tier.

7. Longhorn

Longhorn is a CNCF-incubating distributed block storage system for Kubernetes. The Longhorn project describes it as cloud-native distributed block storage built using Kubernetes and container primitives. The project website says Longhorn provides simplified, 100% open-source persistent block storage with snapshots and backups.

Where it fits in a neocloud

Longhorn is for Kubernetes PersistentVolumes, not for feeding thousands of GPUs with training data.

Use	Longhorn fit
Kubernetes app volumes	Strong
Stateful services	Strong
Small databases, control-plane tools, dashboards	Strong/moderate
Edge Kubernetes storage	Strong
AI training dataset hot tier	Weak
Massive shared filesystem	Not its role

Likely consumers

Consumer	Why
Kubernetes platform teams	Easy persistent volumes
Homelabs and small private clouds	Simple UI and snapshots
Edge AI clusters	Lightweight distributed block storage
Internal developer platforms	Good default storage class
Observability/control-plane stacks	Grafana, small DBs, app storage
Rancher/SUSE users	Strong ecosystem fit

Pros

Pro	Why it matters
Very Kubernetes-native	Works naturally with CSI and PVs
Easy to deploy	Low barrier compared with Ceph
UI, snapshots, backups	Operationally friendly
Good for small/medium clusters	Practical default block storage
Runs on local node disks	Useful for commodity Kubernetes
CNCF project	Community and ecosystem visibility

Cons

Con	Why it matters
Block storage only	Not object or shared file storage
Not a high-performance AI hot tier	Wrong tool for large distributed training reads
Replica traffic can be expensive	Network overhead matters
Performance depends heavily on disks/network	NVMe and 25/100GbE matter if pushing it
Large-scale operations need care	Rebuilds, snapshots, backups, and node failures can hurt
Not ideal for very write-heavy DBs without validation	Test before production-critical use

Neocloud verdict

Longhorn is good for Kubernetes platform services and persistent volumes, not for neocloud AI data-plane storage. Use it for Grafana, metadata services, control-plane apps, small databases, or edge workloads—not the main GPU training filesystem.

8. OpenEBS

OpenEBS is an open-source Kubernetes-native storage platform. OpenEBS says it turns storage available on Kubernetes worker nodes into local or distributed PersistentVolumes. Its documentation describes OpenEBS as enabling dynamic local or replicated container-attached Kubernetes PersistentVolumes, and notes it is a leading choice for NVMe-based deployments.

Where it fits in a neocloud

OpenEBS is a Kubernetes PV/storage-class framework:

Use	OpenEBS fit
LocalPV for fast node-local storage	Strong
NVMe-backed Kubernetes workloads	Strong
Replicated PVs	Strong depending on engine
Control-plane/stateful app storage	Strong
Main AI object store	Not its role
Parallel training filesystem	Not its role

Likely consumers

Consumer	Why
Kubernetes SRE/platform teams	Declarative PV management
AI platform teams needing local NVMe PVs	Useful for cache, scratch, model staging
Edge/private Kubernetes operators	Lightweight and flexible
Teams wanting local-first performance	LocalPV can be very fast
Developers running stateful workloads	Simple Kubernetes-native pattern
Cost-sensitive clusters	Uses existing node disks

Pros

Pro	Why it matters
Kubernetes-native	Managed through familiar APIs and storage classes
Strong LocalPV story	Excellent for NVMe local cache/scratch
Flexible engines	Local and replicated options
Good for AI cache tiers	Node-local NVMe can be exposed cleanly
Lightweight compared with Ceph	Easier to reason about for some use cases
Works well with declarative GitOps	Good platform engineering fit

Cons

Con	Why it matters
Not a shared AI filesystem	It provides PVs, not a Lustre/WEKA/VAST equivalent
LocalPV ties workloads to nodes	Scheduling and failure handling become important
Replication adds overhead	Network and rebuild cost matter
Operational model varies by engine	Need to choose LocalPV vs replicated engines carefully
Not an object store	Needs MinIO/Ceph/etc. for S3
Not enough alone for neocloud storage	It is one layer, not the whole data plane

Neocloud verdict

OpenEBS is excellent for Kubernetes-local NVMe storage, scratch, cache, and application PVs. It complements object stores and parallel filesystems rather than replacing them.

Product-by-product summary

Product	Type	Best neocloud role	Most likely consumers
Ceph	Distributed block/file/object	Durable core: S3, block, CephFS, OpenStack/K8s backend	OpenStack clouds, sovereign clouds, universities, MSPs
MinIO	S3-compatible object store	AI object store / data lake / model artefacts	K8s teams, AI platforms, smaller neoclouds
Lustre	Parallel POSIX filesystem	Hot training and checkpoint filesystem	HPC centres, AI supercomputing clouds, research labs
BeeGFS	Parallel cluster filesystem	AI/HPC scratch and shared file tier	Medium HPC/AI clusters, labs, sovereign AI
DAOS	High-performance object store for HPC/AI	Advanced HPC/AI object I/O tier	Exascale/HPC centres, national labs, deep storage teams
JuiceFS	POSIX filesystem over object storage	Cloud-native POSIX-over-S3 layer	AI platform teams, hybrid cloud, K8s teams
Longhorn	Kubernetes distributed block storage	Kubernetes PVs for apps/control plane	K8s operators, edge clusters, homelabs
OpenEBS	Kubernetes local/replicated PV storage	Local NVMe cache/scratch and PVs	K8s SREs, AI platform teams, edge/private clouds

Which consumers are most likely to choose open source?

1. Regional and sovereign neoclouds

They care about data locality, independence from hyperscalers, and cost control. They are likely to combine:

Ceph RGW or MinIO      -> object storage
Ceph RBD              -> VM/block volumes
Lustre/BeeGFS         -> hot AI training filesystem
OpenEBS/Longhorn      -> Kubernetes PVs

Their main challenge is staffing: they need SREs who understand Linux storage, networking, failure domains, Kubernetes, and observability.

2. Universities, research institutes, and HPC centres

They are likely to use:

Lustre / BeeGFS / DAOS -> HPC/AI filesystem or object layer
Ceph / MinIO           -> object/data archive
Slurm                  -> scheduler
Kubernetes             -> newer AI/platform layer

They often have the right culture for open source: deep systems expertise, slower procurement, and strong need for customisation.

3. Enterprise private AI platforms

They may use open source selectively:

MinIO                 -> S3-compatible internal object store
Rook-Ceph             -> Kubernetes/OpenStack backend
Longhorn/OpenEBS      -> developer platform PVs
JuiceFS               -> POSIX over object for AI teams

Large enterprises may still prefer Pure, NetApp, Dell, IBM, or HPE for production-critical support, but open source often appears in platform engineering and internal AI labs.

4. Cost-sensitive AI startups

They may choose:

MinIO + JuiceFS + local NVMe
or
Ceph + Kubernetes CSI
or
BeeGFS for scratch

They want to avoid large upfront storage contracts, but the risk is that storage failures can consume engineering time and hurt GPU utilization.

5. Homelab and learning environments

For learning neocloud storage, the best sequence is:

1. MinIO
2. Longhorn or OpenEBS
3. Rook-Ceph
4. JuiceFS over MinIO/Ceph
5. BeeGFS
6. Lustre
7. DAOS

That sequence moves from cloud-native and approachable toward HPC-specialist.

Open source versus commercial specialist storage

Dimension	Open source	Commercial specialist platforms
CapEx/licensing	Lower licence cost	Higher licence/subscription cost
Control	Very high	Vendor-controlled roadmap/support
Hardware choice	Flexible	Sometimes certified hardware only
Operational burden	Higher	Lower if vendor support is strong
Time to production	Longer	Usually faster
Performance ceiling	Can be excellent	Often easier to reach reliably
Support	Community/self/vendor optional	Enterprise support included/expected
Multi-tenancy	You build/integrate	Often stronger product features
Observability	You assemble/export	Often more integrated
Best fit	Skilled teams, sovereign clouds, research, cost control	GPU clouds where idle GPU cost dwarfs storage cost

Practical architecture patterns

Pattern A: Open-source private/neocloud core

Object/system of record:   Ceph RGW or MinIO
Block volumes:             Ceph RBD
Shared file:               CephFS or JuiceFS
Hot AI scratch:            BeeGFS or Lustre
Kubernetes PVs:            Rook-Ceph, OpenEBS, or Longhorn
Local NVMe cache:          OpenEBS LocalPV or node-local PVs

Best for: regional clouds, sovereign AI platforms, research clouds, cost-sensitive private AI.

Pattern B: HPC-first AI cloud

Hot training filesystem:   Lustre or BeeGFS
Experimental object tier:  DAOS
Durable object archive:    Ceph RGW or MinIO
Scheduler:                 Slurm
Kubernetes layer:          Separate platform for services/inference

Best for: GPU supercomputing, research, scientific AI, training-heavy clusters.

Pattern C: Kubernetes-first AI platform

Object store:              MinIO or Ceph RGW
POSIX over object:         JuiceFS
Kubernetes PVs:            Longhorn / OpenEBS / Rook-Ceph
Local cache:               OpenEBS LocalPV / node NVMe
GPU orchestration:         Kubernetes + Volcano/Kueue/Ray/Kubeflow

Best for: MLOps, fine-tuning platforms, inference, RAG, internal AI platforms.

My practical recommendations

For a neocloud or AI platform, I would not pick one open-source storage product and expect it to do everything.

Sensible shortlist by layer

Layer	Best open-source candidates
S3/object system of record	MinIO, Ceph RGW
OpenStack/private cloud storage	Ceph
Kubernetes PVs	Rook-Ceph, Longhorn, OpenEBS
POSIX over object storage	JuiceFS
Hot AI scratch/shared filesystem	Lustre, BeeGFS
Advanced HPC/AI object storage	DAOS
Local NVMe cache	OpenEBS LocalPV, Kubernetes Local PVs

Best default combinations

For a small AI platform:

MinIO + OpenEBS/Longhorn + local NVMe

For a serious private cloud:

Ceph + Rook-Ceph + MinIO or Ceph RGW + OpenEBS LocalPV

For a training-heavy GPU cluster:

Lustre or BeeGFS + MinIO/Ceph object archive + local NVMe cache

For an advanced HPC/AI lab:

Lustre/BeeGFS + DAOS evaluation + Ceph/MinIO object tier

The cleanest SRE takeaway is:

Use object storage for durable truth, parallel filesystems for GPU training throughput, Kubernetes PV systems for platform services, and local NVMe for cache/scratch. Do not force one open-source storage system to solve every neocloud storage problem.