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Anki vs Apache Spark MLlib

Anki logo

Anki

Education

Free open source spaced repetition flashcard app, free everywhere except iOS where it is a paid one-time purchase

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: Anki the interface is functional rather than polished, and new users often find the initial setup, deck creation and add-on ecosystem confusing compared with a guided app like Memrise.; 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: Anki covers Spaced repetition scheduling, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

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

Attributes where Anki and Apache Spark MLlib differ
AttributeAnkiApache Spark MLlib
Pricing modelOpen source, no licence fee except a paid iOS appopen-source
PlatformsWindows, Mac, Linux, iOS, Android, WebLinux, macOS, Windows
CategoryEducationMachine Learning
FoundedUnknown1999

Identical on both: starting price (Free), free tier (Yes), 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 Anki

  • Spaced repetition scheduling
  • Cross-platform sync
  • Shared deck library
  • Custom card types
  • Add-ons
  • Open file format

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.

Anki

  • A medical student using a large shared deck to memorise anatomy or pharmacology facts on a fixed exam timelinenot Apache Spark MLlib
  • A language learner building custom vocabulary decks with audio and images tailored to their own study materialnot Apache Spark MLlib
  • A self-directed learner who wants full control over the scheduling algorithm and does not want a subscriptionnot Apache Spark MLlib
  • An iPhone-only user deciding whether the one-time AnkiMobile price is worth it versus using the free web interface insteadnot 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 Anki
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Anki
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Anki
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Anki

Where each one falls short

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

Anki

  • The interface is functional rather than polished, and new users often find the initial setup, deck creation and add-on ecosystem confusing compared with a guided app like Memrise.
  • iOS users pay a one-time fee of around $24.99 to $29.99 for AnkiMobile while every other platform is free, which is an unusual asymmetry that surprises new users comparing app store prices before they understand it funds development.
  • Anki has no content of its own; a new user gets an empty deck and either has to build cards manually or trust the quality of a community-shared deck of unknown accuracy.
  • The default scheduling algorithm requires understanding concepts like ease factor and interval to tune effectively, and default settings are not optimal for every subject or workload.
  • Shared decks, especially large medical school decks, can carry factual errors or become outdated, and there is no editorial review process verifying their accuracy.

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

Anki

Free
  • AnkiFree
    • Desktop apps for Windows, Mac and Linux are free and open source
    • AnkiDroid on Android is free and open source, built by a separate volunteer team
    • AnkiMobile on iOS is a one-time purchase, commonly $24.99 to $29.99, funding development

Apache Spark MLlib

Free

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

Which should you pick?

Choose Anki if

  • You need spaced repetition scheduling.
  • You want to start without paying.
  • You work on Windows, Mac, Linux, iOS, Android, Web.
  • You also want cross-platform sync.
Anki 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 Anki or Apache Spark MLlib better?
Neither clearly leads. Anki 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, Anki or Apache Spark MLlib?
Anki starts at Free and Apache Spark MLlib at Free.
Does Anki or Apache Spark MLlib run on more platforms?
Anki runs on Windows, Mac, Linux, iOS, Android, Web. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Anki for free?
Both have a free tier, so you can try either at no cost before committing.
What is Anki best used for?
Anki is most often used for a medical student using a large shared deck to memorise anatomy or pharmacology facts on a fixed exam timeline, a language learner building custom vocabulary decks with audio and images tailored to their own study material, a self-directed learner who wants full control over the scheduling algorithm and does not want a subscription, an iphone-only user deciding whether the one-time ankimobile price is worth it versus using the free web interface instead. Of those, a medical student using a large shared deck to memorise anatomy or pharmacology facts on a fixed exam timeline and a language learner building custom vocabulary decks with audio and images tailored to their own study material are not what Apache Spark MLlib is typically brought in for.
What can Anki do that Apache Spark MLlib cannot?
Anki covers Spaced repetition scheduling, Cross-platform sync, Shared deck library, Custom card types. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Anki: Why is the iOS app not free when everything else is?

AnkiMobile is a paid one-time purchase, commonly around $24.99 to $29.99, that the developer states directly funds ongoing development of the free desktop apps, AnkiWeb sync and infrastructure used by every platform.

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.

Anki: Is Anki really free on Android?

Yes. AnkiDroid, the Android app, is free and open source, built and maintained by a separate volunteer development team from the desktop project.

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.

Anki: Do I need to pay for sync?

No. AnkiWeb, used to sync decks and review history across devices, is free.

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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