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Cybersecurity · head to head

authentik vs Apache Spark MLlib

authentik logo

authentik

Cybersecurity

Open-source identity provider with flexible authentication flows

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: authentik smaller project than Keycloak, with a correspondingly smaller community and fewer integration guides; 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: authentik covers Configurable flows, 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 authentik and Apache Spark MLlib actually diverge.

Attributes where authentik and Apache Spark MLlib differ
AttributeauthentikApache Spark MLlib
Pricing modelOpen-source core with a paid enterprise tieropen-source
PlatformsDocker, Kubernetes, Linux, Self-hostedLinux, macOS, Windows
CategoryCybersecurityMachine 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 authentik

  • Configurable flows
  • Protocol support
  • Application proxy
  • Modern admin interface

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.

authentik

  • Self-hosted SSO across internal services without commercial identity pricingnot Apache Spark MLlib
  • Putting authentication in front of applications that have none, via the proxynot Apache Spark MLlib
  • Teams who tried Keycloak and wanted something less heavynot 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 authentik
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot authentik
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot authentik
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot authentik

Where each one falls short

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

authentik

  • Smaller project than Keycloak, with a correspondingly smaller community and fewer integration guides
  • The flow model is flexible but conceptually unfamiliar, and simple setups can feel over-abstracted
  • Enterprise support and some governance features sit behind the paid tier
  • Self-hosted identity is still yours to secure, patch and keep available

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

authentik

Free
  • Open sourceFree
    • Full identity provider
    • All protocols
    • Community support

Apache Spark MLlib

Free

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

Which should you pick?

Choose authentik if

  • You need configurable flows.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Linux, Self-hosted.
  • You also want protocol support.
authentik 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 authentik or Apache Spark MLlib better?
Neither clearly leads. authentik 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, authentik or Apache Spark MLlib?
authentik starts at Free and Apache Spark MLlib at Free.
Does authentik or Apache Spark MLlib run on more platforms?
authentik runs on Docker, Kubernetes, Linux, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use authentik for free?
Both have a free tier, so you can try either at no cost before committing.
What is authentik best used for?
authentik is most often used for self-hosted sso across internal services without commercial identity pricing, putting authentication in front of applications that have none, via the proxy, teams who tried keycloak and wanted something less heavy. Of those, self-hosted sso across internal services without commercial identity pricing and putting authentication in front of applications that have none, via the proxy are not what Apache Spark MLlib is typically brought in for.
What can authentik do that Apache Spark MLlib cannot?
authentik covers Configurable flows, Protocol support, Application proxy, Modern admin interface. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

authentik: Is authentik free?

The open-source edition is free and complete for most use. An enterprise tier adds support and additional features.

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.

authentik: authentik or Keycloak?

authentik is generally reported as easier to run and administer; Keycloak is more established with a larger community and Red Hat behind it.

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.

authentik: Can authentik protect apps with no login of their own?

Yes. Its application proxy places authentication in front of services that have no built-in authentication.

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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