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Machine Learning · head to head

Apache Spark MLlib vs Stata

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
Stata logo

Stata

Machine Learning

Data science software for research professionals

From
$48/year
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: 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.; Stata the entry Stata/BE edition is capped at 2,048 variables and 798 independent variables in a model
  • They diverge on capability: Apache Spark MLlib covers DataFrame-based pipelines, Stata covers Statistical analysis.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Apache Spark MLlib and Stata differ
AttributeApache Spark MLlibStata
Starting priceFree$48/year
Pricing modelopen-sourcesubscription
Free tierYesNo
PlatformsLinux, macOS, WindowsLinux, Mac, Windows
Founded19991985

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 Apache Spark MLlib

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

Only in Stata

  • Statistical analysis
  • Data management
  • Graphics
  • Econometrics
  • Survey analysis
  • Python
  • ODBC
  • Excel

What people use each for

The jobs each tool is most often brought in to do.

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 Stata
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Stata
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Stata
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Stata

Stata

  • Statistical analysis and data analysisnot Apache Spark MLlib
  • Econometric modelingnot Apache Spark MLlib
  • Biostatistics and epidemiologynot Apache Spark MLlib
  • Academic and research data analysisnot Apache Spark MLlib

Where each one falls short

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

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.

Stata

  • The entry Stata/BE edition is capped at 2,048 variables and 798 independent variables in a model
  • Raising the variable limit to 32,767 requires Stata/SE and 120,000 requires Stata/MP
  • Stata/MP is licensed by core count, so 2 core and 4 core licences are priced separately
  • Student licences require proof of enrolment at a degree granting institution
  • Stata/MP is not sold on a 6 month student term
  • Perpetual student licences cost several times the annual price, for example $298 against $94 for Stata/BE

Pricing, plan by plan

Apache Spark MLlib

Free

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

Stata

$48/year
  • Stata/BE$48/year
    • Basic edition
    • Core features
  • Stata/SE$295/year
    • Standard edition
    • Larger datasets

Which should you pick?

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

Choose Stata if

  • You need statistical analysis.
  • You work on Linux, Mac, Windows.
  • You also want data management.
Stata review

Questions people ask

Is Apache Spark MLlib or Stata better?
Neither clearly leads. Apache Spark MLlib starts at Free and Stata at $48/year, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Spark MLlib or Stata?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at Free for Apache Spark MLlib and $48/year for Stata.
Does Apache Spark MLlib or Stata run on more platforms?
Apache Spark MLlib runs on Linux, macOS, Windows. Stata runs on Linux, Mac, Windows.
Can I use Apache Spark MLlib for free?
Yes. Apache Spark MLlib has a free tier, so you can try it without paying. Stata starts at $48/year.
What is Apache Spark MLlib best used for?
Apache Spark MLlib is most often used for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Of those, training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about and feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data are not what Stata is typically brought in for.
What can Apache Spark MLlib do that Stata cannot?
Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers. Stata covers Statistical analysis, Data management, Graphics, Econometrics.

Answered from the vendors’ own pages

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.

Stata: How much does Stata cost?

Stata does not publish specific pricing on its website. Customers must use the 'Order Stata' or 'Request a quote' functions to obtain pricing. StataNow is available as a subscription option, but specific monthly or annual costs are not displayed publicly.

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.

Stata: What are the differences between Stata editions?

Stata offers multiple editions including Stata/BE and Stata/MP, with different capabilities and performance characteristics. Edition selection affects pricing, but specific comparisons and costs require requesting a quote.

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.

Stata: Does Stata offer a subscription model?

Yes, StataNow is offered as a subscription option that delivers new features immediately upon release. However, specific pricing for StataNow subscriptions is not published on the website.

Source
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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