Storage Scale vs Ceph for AI Inference: How to Choose
Storage Scale vs Ceph for AI inference: how to choose.
We deploy both. We've run both in production for years. And no, the answer isn't "use both" — it's understanding what each does well, where each falls short, and which fits your workload.
The same question keeps showing up in different disguises: "Storage Scale or Ceph for AI inference?"
We're an IBM Business Partner. We sell Storage Scale. We also deliver Ceph training and run Ceph consulting engagements in production. We work with both daily, so what follows comes from building real architectures — not from reading datasheets.
This is what we'd tell a client sitting across the table designing their AI storage architecture.
Before you choose: what AI inference actually needs from storage
Most people start with the product. We start with the access pattern, because that's what determines whether the choice works or gives you headaches for years.
A real inference environment — not a Llama demo on a laptop — looks like this:
And all of that gets consumed by a zoo of processes: GPU nodes running vLLM or TGI, CPU nodes, Spark or Ray preprocessing, Docling and OCR pipelines, vector databases, legacy apps coming in over NFS, and someone from compliance who needs to see a PDF from Windows via SMB.
If your setup looks like this — many consumers, many protocols, shared data — that pattern should drive the decision. Not the price per TB or the vendor logo.
Where Storage Scale wins for AI inference
Native POSIX: your frameworks expect a directory, not a bucket
This seems obvious until you have to set it up. Look at how the tools you'll actually use load models:
They want a path. A directory. Not an S3 endpoint. Storage Scale (formerly GPFS) is a native parallel POSIX filesystem. That /models is a shared directory that all nodes read concurrently, with real concurrent access, no intermediate copies. Nothing to invent.
Zero copies between layers: the argument we find most convincing
With Ceph S3, the typical pattern for serving a model goes: download from bucket → write to local disk or PVC → start the inference engine → serve. That's three steps before the first query lands. And if you have 16 nodes, all 16 download their own copy.
With Storage Scale, the inference engine points to /gpfs/models/llama-70b/ and starts. Done. No download, no cache, no "does this node have the latest version?". When you update the model, you update it once and every node sees it.
This matters most when you're iterating — swapping models, testing LoRA adapters, rotating configurations. With local cache you end up maintaining sync scripts, invalidation logic and disk cleanup. With a parallel filesystem there's nothing to maintain.
Multi-protocol on the same file
This is what solves the enterprise headache. A single file in Storage Scale can be consumed via POSIX (GPU node), S3 (modern app), NFS (data team), SMB (someone on Windows) and CSI (Kubernetes pod). The same file. Not a copy per protocol. Not a different namespace per interface.
IBM implements this through Cluster Export Services (CES), which exposes S3, NFS and SMB access over the same data in the parallel filesystem.
In an environment where modern containers coexist with legacy applications nobody dares touch, this is what lets you build an AI factory without breaking what already works.
Metadata: when you have millions of small files
Enterprise RAG isn't "three PDFs in a bucket". It's millions of documents, millions of chunks, millions of embeddings, config files, auxiliary indices, shards, logs. Heavy operations on large directories with many small files. Storage Scale has been solving this in HPC environments for decades — Summit, Sierra and other supercomputers ran on GPFS. CephFS can handle this, but in our experience it takes significantly more design effort to keep it from struggling.
Where Ceph wins for AI inference storage
Massive object storage: real S3, not S3 as an afterthought
Ceph was built as distributed storage for objects, blocks and files. Its RGW (RADOS Gateway) provides a full S3 API with lifecycle policies, versioning, multi-tenancy, IAM — everything you need to run a proper object store. It's not a bolt-on. It's the core.
If your inference pipeline is S3-native — models downloaded from a bucket, datasets read via API, results written as objects — Ceph handles it well. A well-designed Ceph cluster scales horizontally to hundreds of PB by adding commodity nodes.
Cost per TB: Ceph wins this one outright
Let's be direct: Storage Scale costs more. It needs IBM licences, hardware with specific requirements, and people who know how to run it (there aren't many). Ceph runs on commodity hardware, has no software licence cost, and a team with solid Linux experience can operate it.
For a client with petabytes of data where most of it is cold — training datasets, historical archives, model backups — there's no reason to pay Storage Scale prices for TBs that get read once a month. Ceph with well-configured erasure coding is the right answer there.
Kubernetes: Rook makes everything trivial
For teams that live in Kubernetes or OpenShift, Ceph with Rook is hard to beat. A single operator that gives you RBD (ReadWriteOnce), CephFS (ReadWriteMany) and RGW (S3) from one cluster. OpenShift Data Foundation (ODF) is literally Ceph packaged by Red Hat — we cover this in detail in our Ceph vs MinIO 2026 guide.
Storage Scale has CSI too, but Rook/Ceph has been in the Kubernetes ecosystem longer and the community is much larger. If your team thinks in operators, Helm charts and GitOps, Ceph speaks their language.
Block storage for VMs and databases
If you also run OpenStack, virtualisation, or need block volumes for databases alongside inference, Ceph's RBD is best-in-class. Storage Scale doesn't compete here — it's not its territory.
CERN runs over 60 PB on Ceph in production, underpinning its OpenStack infrastructure. They've gone from a few PB to exabyte scale in a decade, adding nodes without architectural disruption. We cover this in more depth in our article on open source storage for AI and HPC.
What each gets wrong — and nobody likes talking about
This is where most articles get vague. We won't. We deploy both, and both have things we don't like.
What we don't like about Storage Scale
- It's expensive. IBM licences, specific hardware requirements, and a rack with Storage Scale ECE plus GPUs isn't a small investment. For an AI pilot or a startup, it doesn't make sense.
- Running it requires HPC expertise. It's not that it's difficult — it's a different world from cloud-native. If your team lives in Kubernetes and has never touched a parallel filesystem, the learning curve is real.
- S3 isn't its strong suit. Storage Scale has S3 access via CES, and it works. But if you compare it with Ceph's RGW on pure S3 features — lifecycle, multi-tenancy, advanced versioning — Ceph has the edge.
- Block storage: essentially absent. If you need RBD or block volumes for VMs, Storage Scale is not your tool.
What we don't like about Ceph
- CephFS is not GPFS. CephFS works, but for many concurrent clients doing parallel I/O across millions of files (the classic AI/HPC pattern), Storage Scale has considerably more mileage. We explained this in our 2023 comparison.
- Local cache adds complexity. If your models live in S3, every inference node downloads its copy. With 4 nodes that's trivial. With 32, you're maintaining sync scripts, tracking cache versions, and hoping local disks don't fill up.
- Multi-protocol isn't clean. Ceph speaks RGW (S3), RBD (block), CephFS (file) and NFS (via Ganesha). But each protocol operates on its own pool or namespace. You can't transparently read the same file via S3 and NFS the way Storage Scale lets you.
- Metadata under pressure. Intensive operations on directories with millions of small files (the RAG use case) can bottleneck in CephFS if the design isn't right. Ceph doesn't forgive improvisation.
Mounting s3fs or goofys to give POSIX semantics to Ceph S3 so you can use from_pretrained() directly. Technically works. In production, the POSIX semantics are partial, performance is unpredictable, and the errors get creative. We don't recommend it as a permanent solution.
Storage Scale vs Ceph for AI storage: summary
The short version: Storage Scale wins when data is "alive" — many processes reading shared models, POSIX pipelines, mixed environments where Kubernetes and legacy apps coexist. Ceph wins when data is objects — S3 as the primary interface, models cached locally, cloud-native teams, tight budget.
Using Storage Scale as a cheap object store is a waste of money. Trying to make CephFS behave like an HPC parallel filesystem is asking for trouble. Each is very good at what it does.
What we'd tell a client asking today
If pushed: for AI inference in enterprise environments with shared data and RAG, Storage Scale causes fewer headaches. Models are alive, shared, accessible via whatever protocol each consumer needs. No sync scripts, no cache prayers.
But if your pattern is genuinely cloud-native — stateless pods, S3 as source of truth, an SRE team that knows how to run Ceph — then Ceph is the right call and it'll cost you considerably less. We're not saying that to be polite: we've seen it work this way in production many times.
And if your environment runs sensitive data on IBM Power, integrates with DB2 or Oracle, and inference will coexist with HPC or analytics workloads — Storage Scale has no real competitor there. It's its natural territory. Storage Scale combined with Content-Aware Storage in IBM Fusion is starting to turn storage into an active data preparation engine for RAG.
It depends. Tell us your use case and we'll tell you which.
We run both in production. Tell us about your workload and we'll point you in the right direction.
