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Rollup vs Apache Spark MLlib

Rollup logo

Rollup

Web Development

JavaScript module bundler built for libraries

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: Rollup slower than the Go and Rust bundlers that followed it, since it is written in JavaScript; 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: Rollup covers Tree shaking, 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 Rollup and Apache Spark MLlib actually diverge.

Attributes where Rollup and Apache Spark MLlib differ
AttributeRollupApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
CategoryWeb DevelopmentMachine Learning
FoundedUnknown1999

Identical on both: starting price (Free), free tier (Yes), platforms (Linux, macOS, Windows), 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 Rollup

  • Tree shaking
  • Clean output
  • Multiple output formats
  • Plugin API

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.

Rollup

  • Publishing a JavaScript library in several module formatsnot Apache Spark MLlib
  • Builds where output size and cleanliness matter more than build speednot Apache Spark MLlib
  • Producing ES module output for consumers who will bundle it themselvesnot 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 Rollup
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Rollup
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Rollup
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Rollup

Where each one falls short

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

Rollup

  • Slower than the Go and Rust bundlers that followed it, since it is written in JavaScript
  • Application concerns like dev servers and hot reloading are not its job, so app builds need Vite on top
  • Configuration for non-trivial applications gets verbose compared with tools that assume more

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

Rollup

Free
  • RollupFree
    • Full functionality
    • Commercial use permitted
    • Community support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Rollup if

  • You need tree shaking.
  • You want to start without paying.
  • You work on Linux, macOS, Windows.
  • You also want clean output.
Rollup 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 Rollup or Apache Spark MLlib better?
Neither clearly leads. Rollup 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, Rollup or Apache Spark MLlib?
Rollup starts at Free and Apache Spark MLlib at Free.
Does Rollup or Apache Spark MLlib run on more platforms?
Both run on Linux, macOS, Windows, so platform support will not decide this one for you.
Can I use Rollup for free?
Both have a free tier, so you can try either at no cost before committing.
What is Rollup best used for?
Rollup is most often used for publishing a javascript library in several module formats, builds where output size and cleanliness matter more than build speed, producing es module output for consumers who will bundle it themselves. Of those, publishing a javascript library in several module formats and builds where output size and cleanliness matter more than build speed are not what Apache Spark MLlib is typically brought in for.
What can Rollup do that Apache Spark MLlib cannot?
Rollup covers Tree shaking, Clean output, Multiple output formats, Plugin API. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Rollup: Is Rollup free?

Yes, open source under the MIT licence.

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.

Rollup: Rollup or webpack?

Rollup is the usual choice for libraries thanks to cleaner output and better tree shaking. webpack remains stronger for complex applications with heavy asset handling.

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.

Rollup: Do I need Rollup if I use Vite?

Not directly. Vite uses Rollup for production builds, so you already benefit from it.

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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