File Storage · head to head
Ceph vs scikit-learn
Ceph
File Storage
Open source distributed storage providing object, block and file from one cluster
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ceph ceph assumes an operator who understands placement groups, CRUSH rules and recovery tuning, so organisations without dedicated storage staff routinely end up with a cluster that works until the first failure and then does not.; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
- They diverge on capability: Ceph covers RADOS object store, scikit-learn covers Classification algorithms.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which Ceph and scikit-learn actually diverge.
| Attribute | Ceph | scikit-learn |
|---|---|---|
| Pricing model | Open source, no licence fee | Unknown |
| Platforms | Linux | Python, Linux, macOS, Windows |
| Category | File Storage | Machine Learning |
| Founded | Unknown | 2007 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
What each one covers
Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.
Only in Ceph
- RADOS object store
- RADOS Gateway
- RBD block devices
- CephFS
- CRUSH placement
- Erasure coded pools
Only in scikit-learn
- Classification algorithms
- Regression models
- Clustering methods
- Dimensionality reduction
- Model selection
- NumPy
- SciPy
- Pandas
What people use each for
The jobs each tool is most often brought in to do.
Ceph
- Backing a private cloud where virtual machine disks, shared filesystems and an S3 endpoint all need the same hardwarenot scikit-learn
- Growing past the point where a proprietary array upgrade costs more than a rack of commodity serversnot scikit-learn
- Research and media environments with petabytes of data and staff who can operate storagenot scikit-learn
- Providing an S3 endpoint on premises with multi site replication under your own controlnot scikit-learn
scikit-learn
- Machine learningnot Ceph
- Data analysisnot Ceph
- Model trainingnot Ceph
- Predictive analyticsnot Ceph
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Ceph
- Ceph assumes an operator who understands placement groups, CRUSH rules and recovery tuning, so organisations without dedicated storage staff routinely end up with a cluster that works until the first failure and then does not.
- Small clusters are inefficient: three way replication means a third of raw capacity is usable, and erasure coding needs enough failure domains to be safe, so the economics only work above a certain size.
- Recovery and rebalancing generate heavy internal traffic, so a failed disk can degrade client latency across the cluster unless backfill is throttled correctly beforehand.
- Upgrades must follow a strict daemon order across monitors, managers, OSDs and gateways, and a mistake in that order on a live cluster is difficult to reverse.
- Because it is self hosted, every byte served to the internet is transit you pay for on your own links, so the free licence does not mean free egress and bandwidth planning becomes your problem rather than the providers.
scikit-learn
- No GPU acceleration by default; limited optional GPU support requires external arrays
- Single-machine only; no built-in distributed computing across clusters
- All datasets must fit entirely in RAM; no out-of-core learning
- No production-grade deep learning; neural network support limited to basic multilayer perceptron
- No reinforcement learning algorithms
Pricing, plan by plan
Ceph
Free- CephFree
- Full functionality, no capacity limit
- Object, block and file interfaces
- Community support via mailing list and Slack
scikit-learn
FreeNo published plan breakdown. See the scikit-learn review.
Which should you pick?
Choose Ceph if
- You need rados object store.
- You want to start without paying.
- You work on Linux.
- You also want rados gateway.
Choose scikit-learn if
- You need classification algorithms.
- You want to start without paying.
- You work on Python, Linux, macOS, Windows.
- You also want regression models.
Questions people ask
- Is Ceph or scikit-learn better?
- Neither clearly leads. Ceph starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Ceph or scikit-learn?
- Ceph starts at Free and scikit-learn at Free.
- Does Ceph or scikit-learn run on more platforms?
- Ceph runs on Linux. scikit-learn runs on Python, Linux, macOS, Windows.
- Can I use Ceph for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ceph best used for?
- Ceph is most often used for backing a private cloud where virtual machine disks, shared filesystems and an s3 endpoint all need the same hardware, growing past the point where a proprietary array upgrade costs more than a rack of commodity servers, research and media environments with petabytes of data and staff who can operate storage, providing an s3 endpoint on premises with multi site replication under your own control. Of those, backing a private cloud where virtual machine disks, shared filesystems and an s3 endpoint all need the same hardware and growing past the point where a proprietary array upgrade costs more than a rack of commodity servers are not what scikit-learn is typically brought in for.
- What can Ceph do that scikit-learn cannot?
- Ceph covers RADOS object store, RADOS Gateway, RBD block devices, CephFS. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.
Answered from the vendors’ own pages
Ceph: How many nodes do I need to start?
Three is the practical minimum for a replicated cluster with real fault tolerance, and most production advice starts at five once you account for maintenance windows.
scikit-learn: Does scikit-learn support GPU acceleration?
Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.
SourceCeph: Is it faster than a SAN?
Not on single stream latency. It wins on aggregate throughput and on growing without a forklift upgrade, which is a different property from raw speed.
scikit-learn: Can scikit-learn handle datasets larger than RAM?
No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.
SourceCeph: Can I buy support?
Yes. IBM sells IBM Storage Ceph and SUSE and others have offered supported builds; the upstream project itself is free.
scikit-learn: Is scikit-learn free to use commercially?
Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.
SourceCeph: Should I use it just for S3?
If object is all you need, a dedicated object store is simpler to run. Ceph earns its complexity when you need block and file as well.
scikit-learn: What neural network capabilities does scikit-learn have?
Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.
Sourcescikit-learn: Does scikit-learn include natural language processing?
Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.
Sourcescikit-learn: When was scikit-learn first released?
Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.
SourceRelated pages
More on scikit-learn
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- scikit-learn vs Dask
- scikit-learn vs Fal AI
- scikit-learn vs Groq
- scikit-learn vs TensorFlow
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