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Machine Learning · head to head

Alteryx vs Apache Spark MLlib

Alteryx logo

Alteryx

Machine Learning

Analytics automation platform

From
$250/month
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

  • Only Apache Spark MLlib has a free tier, so it costs nothing to try first.
  • Each has a real cost: Alteryx starter is $250 per user per month billed annually, and the Professional and Enterprise editions are quote-only; 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: Alteryx covers Data preparation, 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 Alteryx and Apache Spark MLlib actually diverge.

Attributes where Alteryx and Apache Spark MLlib differ
AttributeAlteryxApache Spark MLlib
Starting price$250/monthFree
Pricing modelsubscriptionopen-source
Free tierNoYes
PlatformsWindows, WebLinux, macOS, Windows
Founded19971999

Identical on both: 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 Alteryx

  • Data preparation
  • Data blending
  • Predictive analytics
  • Spatial analytics
  • Reporting
  • Python
  • R
  • Snowflake

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.

Alteryx

  • Data preparation and building AI-ready datasetsnot Apache Spark MLlib
  • Predictive analytics without writing codenot Apache Spark MLlib
  • Automating and orchestrating repeatable analytics workflowsnot Apache Spark MLlib
  • Enterprise reporting with governed, reusable logicnot Apache Spark MLlib
  • Connecting to Snowflake, Databricks and cloud warehouses alongside on-premises systemsnot 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 Alteryx
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Alteryx
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Alteryx
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Alteryx

Where each one falls short

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

Alteryx

  • Starter is $250 per user per month billed annually, and the Professional and Enterprise editions are quote-only
  • Automation runs are metered, with 50 included on Starter and 15,000 on Professional, and more must be bought
  • Cost depends on three separate dimensions at once: edition, user role and automation capacity
  • Advanced analytics, governance and orchestration are withheld from the entry edition

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

Alteryx

$250/month
  • Starter Edition$250/month
    • 1-10 users
    • 50 automation runs included
    • Cloud only
  • Professional Edition$null/month
    • Basic and Full users
    • 15,000 automation runs included
    • Cloud and desktop deployment
  • Enterprise Edition$null/month
    • All user types
    • 15,000 automation runs included
    • All deployment options

Apache Spark MLlib

Free

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

Which should you pick?

Choose Alteryx if

  • You need data preparation.
  • You work on Windows, Web.
  • You also want data blending.
Alteryx 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 Alteryx or Apache Spark MLlib better?
Neither clearly leads. Alteryx starts at $250/month and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Alteryx or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at $250/month for Alteryx and Free for Apache Spark MLlib.
Does Alteryx or Apache Spark MLlib run on more platforms?
Alteryx runs on Windows, Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Spark MLlib for free?
Yes. Apache Spark MLlib has a free tier, so you can try it without paying. Alteryx starts at $250/month.
What is Alteryx best used for?
Alteryx is most often used for data preparation and building ai-ready datasets, predictive analytics without writing code, automating and orchestrating repeatable analytics workflows, enterprise reporting with governed, reusable logic. Of those, data preparation and building ai-ready datasets and predictive analytics without writing code are not what Apache Spark MLlib is typically brought in for.
What can Alteryx do that Apache Spark MLlib cannot?
Alteryx covers Data preparation, Data blending, Predictive analytics, Spatial analytics. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Alteryx: How much does Alteryx Starter cost?

Alteryx Starter Edition costs $250 USD per user per month when billed annually, for teams of 1-10 users.

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.

Alteryx: Is there a free trial for Alteryx?

Yes, Alteryx offers a 30-day free trial to evaluate the platform before committing to a paid plan.

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.

Alteryx: What differentiates Alteryx Professional from Starter?

Professional Edition supports both Basic and Full user roles, includes 15,000 automation runs, offers cloud and desktop deployment, connects to 100+ data sources, and enables advanced data preparation and macros. Professional and Enterprise editions require contacting sales for pricing.

Source
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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