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File Storage · head to head

Ceph vs Keras

Ceph logo

Ceph

File Storage

Open source distributed storage providing object, block and file from one cluster

From
Free
Rated
-
Full reviewWebsite
Keras logo

Keras

Machine Learning

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite

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.; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • They diverge on capability: Ceph covers RADOS object store, Keras covers Sequential and Functional API.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Ceph and Keras actually diverge.

Attributes where Ceph and Keras differ
AttributeCephKeras
Pricing modelOpen source, no licence feeopen-source
PlatformsLinuxPython, Google Colab, Jupyter
CategoryFile StorageMachine Learning
FoundedUnknown2015

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 Keras

  • Sequential and Functional API
  • Pre-built neural network layers
  • Model training and evaluation
  • Transfer learning
  • Model serialization
  • TensorFlow
  • JAX
  • PyTorch

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 Keras
  • Growing past the point where a proprietary array upgrade costs more than a rack of commodity serversnot Keras
  • Research and media environments with petabytes of data and staff who can operate storagenot Keras
  • Providing an S3 endpoint on premises with multi site replication under your own controlnot Keras

Keras

  • 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.

Keras

  • Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • Error messages can be vague and unhelpful, making debugging challenging
  • Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch

Pricing, plan by plan

Ceph

Free
  • CephFree
    • Full functionality, no capacity limit
    • Object, block and file interfaces
    • Community support via mailing list and Slack

Keras

Free
  • Open SourceFree
    • High-level API
    • Pre-built layers
    • Model serialization

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.
Ceph review

Choose Keras if

  • You need sequential and functional api.
  • You want to start without paying.
  • You work on Python, Google Colab, Jupyter.
  • You also want pre-built neural network layers.
Keras review

Questions people ask

Is Ceph or Keras better?
Neither clearly leads. Ceph starts at Free and Keras at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Ceph or Keras?
Ceph starts at Free and Keras at Free.
Does Ceph or Keras run on more platforms?
Ceph runs on Linux. Keras runs on Python, Google Colab, Jupyter.
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 Keras is typically brought in for.
What can Ceph do that Keras cannot?
Ceph covers RADOS object store, RADOS Gateway, RBD block devices, CephFS. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning.

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.

Keras: What is Keras?

Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.

Source
Ceph: 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.

Keras: What model architectures does Keras support?

Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.

Source
Ceph: Can I buy support?

Yes. IBM sells IBM Storage Ceph and SUSE and others have offered supported builds; the upstream project itself is free.

Keras: Can Keras models run on TPUs and GPUs?

Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.

Source
Ceph: 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.

Keras: Does Keras offer pre-trained models?

Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.

Source
Keras: Who should use Keras?

Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.

Source
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