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Apache Superset vs Apache Spark MLlib

Apache Superset logo

Apache Superset

Spreadsheets

Modern data exploration and visualization platform

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: Apache Superset no commercial pricing; open-source project; 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: Apache Superset covers 40+ Visualizations, 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 Apache Superset and Apache Spark MLlib actually diverge.

Attributes where Apache Superset and Apache Spark MLlib differ
AttributeApache SupersetApache Spark MLlib
PlatformsWeb, Self-hosted, DockerLinux, macOS, Windows
CategorySpreadsheetsMachine Learning

Identical on both: starting price (Free), pricing model (open-source), free tier (Yes), user rating (Not yet rated), founded (1999).

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 Apache Superset

  • 40+ Visualizations
  • SQL IDE
  • Semantic Layer
  • Caching
  • Security
  • PostgreSQL
  • MySQL
  • Presto

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.

Apache Superset

  • Self-service analyticsnot Apache Spark MLlib
  • Data explorationnot Apache Spark MLlib
  • Ad-hoc reportingnot Apache Spark MLlib
  • Collaborative analysisnot Apache Spark MLlib
  • Embedded analyticsnot 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 Apache Superset
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Apache Superset
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Apache Superset
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Apache Superset

Where each one falls short

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

Apache Superset

  • No commercial pricing; open-source project
  • Commercial hosting available via Preset.io (separate vendor)

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

Apache Superset

Free
  • Open SourceFree
    • Full Features
    • Self-hosted
    • Community Support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Apache Superset if

  • You need 40+ visualizations.
  • You want to start without paying.
  • You work on Web, Self-hosted, Docker.
  • You also want sql ide.
Apache Superset 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 Apache Superset or Apache Spark MLlib better?
Neither clearly leads. Apache Superset 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, Apache Superset or Apache Spark MLlib?
Apache Superset starts at Free and Apache Spark MLlib at Free.
Does Apache Superset or Apache Spark MLlib run on more platforms?
Apache Superset runs on Web, Self-hosted, Docker. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Superset for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Superset best used for?
Apache Superset is most often used for self-service analytics, data exploration, ad-hoc reporting, collaborative analysis. Of those, self-service analytics and data exploration are not what Apache Spark MLlib is typically brought in for.
What can Apache Superset do that Apache Spark MLlib cannot?
Apache Superset covers 40+ Visualizations, SQL IDE, Semantic Layer, Caching. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Apache Superset: What does Apache Superset cost?

Apache Superset is open-source software available free under the Apache 2.0 license. There are no licensing costs, subscription fees, or per-seat charges for using Superset itself.

Source
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.

Apache Superset: Is there a paid version of Apache Superset?

Apache Superset itself is free and open-source. Preset.io offers commercial Superset hosting with premium support, but this is a separate vendor service, not an official paid tier of Superset.

Source
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.

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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