Softwr

Cybersecurity · head to head

Authelia vs Apache Spark MLlib

Authelia logo

Authelia

Cybersecurity

Open-source authentication and two-factor portal for reverse proxies

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: Authelia depends on a reverse proxy: it is not a standalone identity provider; 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: Authelia covers Reverse proxy integration, 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 Authelia and Apache Spark MLlib actually diverge.

Attributes where Authelia and Apache Spark MLlib differ
AttributeAutheliaApache Spark MLlib
Pricing modelOpen source, no licence feeopen-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 Authelia

  • Reverse proxy integration
  • Two-factor authentication
  • Access control rules
  • Lightweight backends

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.

Authelia

  • Putting a login and 2FA in front of self-hosted services that have nonenot Apache Spark MLlib
  • Adding SSO across a small set of internal tools without a full identity platformnot Apache Spark MLlib
  • Home and small-team infrastructure behind a single reverse proxynot 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 Authelia
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Authelia
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Authelia
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Authelia

Where each one falls short

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

Authelia

  • Depends on a reverse proxy: it is not a standalone identity provider
  • Configuration is YAML-first with no administrative interface, so changes mean editing files
  • Not a full IAM: user management, provisioning and federation are limited compared with Keycloak
  • Scales poorly as an organisation-wide identity solution, which is not its target

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

Authelia

Free
  • AutheliaFree
    • Full functionality
    • No usage limits
    • Community support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Authelia if

  • You need reverse proxy integration.
  • You want to start without paying.
  • You work on Docker, Kubernetes, Linux, Self-hosted.
  • You also want two-factor authentication.
Authelia 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 Authelia or Apache Spark MLlib better?
Neither clearly leads. Authelia 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, Authelia or Apache Spark MLlib?
Authelia starts at Free and Apache Spark MLlib at Free.
Does Authelia or Apache Spark MLlib run on more platforms?
Authelia runs on Docker, Kubernetes, Linux, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Authelia for free?
Both have a free tier, so you can try either at no cost before committing.
What is Authelia best used for?
Authelia is most often used for putting a login and 2fa in front of self-hosted services that have none, adding sso across a small set of internal tools without a full identity platform, home and small-team infrastructure behind a single reverse proxy. Of those, putting a login and 2fa in front of self-hosted services that have none and adding sso across a small set of internal tools without a full identity platform are not what Apache Spark MLlib is typically brought in for.
What can Authelia do that Apache Spark MLlib cannot?
Authelia covers Reverse proxy integration, Two-factor authentication, Access control rules, Lightweight backends. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Authelia: Is Authelia free?

Yes, open source with no licence fee.

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.

Authelia: Does Authelia need a reverse proxy?

Yes. It integrates through forward authentication with Nginx, Traefik, Caddy or HAProxy rather than sitting in front of traffic itself.

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.

Authelia: Authelia or Keycloak?

Authelia is far lighter and aimed at protecting self-hosted services behind a proxy. Keycloak is a full identity and access management platform, and much more to run.

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.

Share

Related pages

More on Authelia

More on Apache Spark MLlib

Keep looking

Other head to heads