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Machine Learning · head to head

ClearML vs Apache Spark MLlib

ClearML logo

ClearML

Machine Learning

Open-source MLOps platform for experiment tracking and orchestration

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: ClearML broad scope means more to learn and more to run than a focused tracking tool; 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: ClearML covers Experiment tracking, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where ClearML and Apache Spark MLlib differ
AttributeClearMLApache Spark MLlib
Pricing modelOpen-source self-hosted, with paid hosted and enterprise tiersopen-source
PlatformsLinux, macOS, Windows, Docker, KubernetesLinux, macOS, Windows
FoundedUnknown1999

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 ClearML

  • Experiment tracking
  • Remote execution
  • Data versioning
  • Pipelines

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.

ClearML

  • Tracking experiments across a team so results are reproduciblenot Apache Spark MLlib
  • Moving training from laptops to shared GPU hardware without repackagingnot Apache Spark MLlib
  • Versioning datasets alongside the experiments that consumed themnot 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 ClearML
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot ClearML
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot ClearML
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot ClearML

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

ClearML

  • Broad scope means more to learn and more to run than a focused tracking tool
  • Self-hosting the server is real infrastructure — database, file storage and web server
  • Documentation quality is uneven across the newer parts of the platform
  • Smaller community than the most popular tracking tools, so fewer worked examples exist

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

ClearML

Free
  • Open sourceFree
    • Experiment tracking
    • Pipelines
    • Self-hosted server

Apache Spark MLlib

Free

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

Which should you pick?

Choose ClearML if

  • You need experiment tracking.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker, Kubernetes.
  • You also want remote execution.
ClearML 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 ClearML or Apache Spark MLlib better?
Neither clearly leads. ClearML 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, ClearML or Apache Spark MLlib?
ClearML starts at Free and Apache Spark MLlib at Free.
Does ClearML or Apache Spark MLlib run on more platforms?
ClearML runs on Linux, macOS, Windows, Docker, Kubernetes. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use ClearML for free?
Both have a free tier, so you can try either at no cost before committing.
What is ClearML best used for?
ClearML is most often used for tracking experiments across a team so results are reproducible, moving training from laptops to shared gpu hardware without repackaging, versioning datasets alongside the experiments that consumed them. Of those, tracking experiments across a team so results are reproducible and moving training from laptops to shared gpu hardware without repackaging are not what Apache Spark MLlib is typically brought in for.
What can ClearML do that Apache Spark MLlib cannot?
ClearML covers Experiment tracking, Remote execution, Data versioning, Pipelines. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

ClearML: Is ClearML free?

The open-source version is free and self-hostable. Hosted and enterprise tiers are paid.

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.

ClearML: How much code does tracking require?

Very little — adding a couple of lines to an existing training script captures parameters, metrics and environment automatically.

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.

ClearML: Does ClearML replace MLflow?

It covers MLflow’s tracking and adds orchestration, remote execution and data versioning. Whether that breadth is an advantage or extra weight depends on whether you need the rest.

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.

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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