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File Storage · head to head

Ceph vs Apache Spark MLlib

Ceph logo

Ceph

File Storage

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

From
Free
Rated
-
Full reviewWebsite
Apache Spark MLlib logo

Apache Spark MLlib

Machine Learning

The machine learning library inside Apache Spark, for data that will not fit on one machine

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.; Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • They diverge on capability: Ceph covers RADOS object store, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

Only the attributes on which Ceph and Apache Spark MLlib actually diverge.

Attributes where Ceph and Apache Spark MLlib differ
AttributeCephApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsLinuxLinux, macOS, Windows
CategoryFile StorageMachine Learning
FoundedUnknown1999

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 Apache Spark MLlib

  • DataFrame-based pipelines
  • Distributed algorithms
  • Alternating least squares
  • Feature transformers
  • Model selection
  • Pipeline persistence
  • Language bindings
  • Runs in existing Spark deployments

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

Apache Spark MLlib

  • Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Ceph
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Ceph
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Ceph
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot 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.

Apache Spark MLlib

  • The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
  • There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
  • Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
  • Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
  • The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.

Pricing, plan by plan

Ceph

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

Apache Spark MLlib

Free

No published plan breakdown. See the Apache Spark MLlib 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.
Ceph review

Choose Apache Spark MLlib if

  • You need dataframe-based pipelines.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want distributed algorithms.
Apache Spark MLlib review

Questions people ask

Is Ceph or Apache Spark MLlib better?
Neither clearly leads. Ceph starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Ceph or Apache Spark MLlib?
Ceph starts at Free and Apache Spark MLlib at Free.
Does Ceph or Apache Spark MLlib run on more platforms?
Ceph runs on Linux. Apache Spark MLlib runs on 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 Apache Spark MLlib is typically brought in for.
What can Ceph do that Apache Spark MLlib cannot?
Ceph covers RADOS object store, RADOS Gateway, RBD block devices, CephFS. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

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.

Apache Spark MLlib: What is the difference between spark.ml and spark.mllib?

spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.

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.

Apache Spark MLlib: Do I need a cluster?

Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.

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.

Apache Spark MLlib: Can I use scikit-learn on Spark instead?

Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.

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.

Apache Spark MLlib: How do I serve an MLlib model in real time?

Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.

Apache Spark MLlib: Is it free?

The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.

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