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Cybersecurity · head to head

Ory Kratos vs Apache Spark MLlib

Ory Kratos logo

Ory Kratos

Cybersecurity

Headless identity and user management API

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: Ory Kratos headless means you build every screen, which is significant work compared with a hosted login page; 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: Ory Kratos covers Headless API, 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 Ory Kratos and Apache Spark MLlib actually diverge.

Attributes where Ory Kratos and Apache Spark MLlib differ
AttributeOry KratosApache Spark MLlib
Pricing modelOpen-source self-hosted, with a paid managed networkopen-source
PlatformsLinux, Docker, Kubernetes, 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 Ory Kratos

  • Headless API
  • Self-service flows
  • Multi-factor authentication
  • Pluggable identity schemas

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.

Ory Kratos

  • Products needing complete control over the look and flow of authenticationnot Apache Spark MLlib
  • Applications that must not hand user identity data to a third partynot Apache Spark MLlib
  • Teams building identity as infrastructure across several servicesnot 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 Ory Kratos
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Ory Kratos
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Ory Kratos
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Ory Kratos

Where each one falls short

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

Ory Kratos

  • Headless means you build every screen, which is significant work compared with a hosted login page
  • More moving parts than a monolithic IAM: Kratos handles identity, and OAuth2 needs Ory Hydra alongside
  • Documentation assumes real familiarity with identity concepts and is not a gentle introduction
  • Self-hosting identity carries the security and availability burden that hosted providers absorb

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

Ory Kratos

Free
  • Self-hostedFree
    • Full identity server
    • All flows
    • Community support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Ory Kratos if

  • You need headless api.
  • You want to start without paying.
  • You work on Linux, Docker, Kubernetes, Self-hosted.
  • You also want self-service flows.
Ory Kratos 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 Ory Kratos or Apache Spark MLlib better?
Neither clearly leads. Ory Kratos 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, Ory Kratos or Apache Spark MLlib?
Ory Kratos starts at Free and Apache Spark MLlib at Free.
Does Ory Kratos or Apache Spark MLlib run on more platforms?
Ory Kratos runs on Linux, Docker, Kubernetes, Self-hosted. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Ory Kratos for free?
Both have a free tier, so you can try either at no cost before committing.
What is Ory Kratos best used for?
Ory Kratos is most often used for products needing complete control over the look and flow of authentication, applications that must not hand user identity data to a third party, teams building identity as infrastructure across several services. Of those, products needing complete control over the look and flow of authentication and applications that must not hand user identity data to a third party are not what Apache Spark MLlib is typically brought in for.
What can Ory Kratos do that Apache Spark MLlib cannot?
Ory Kratos covers Headless API, Self-service flows, Multi-factor authentication, Pluggable identity schemas. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Ory Kratos: Is Ory Kratos free?

Yes, open source and free to self-host. Ory Network is a paid managed service.

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.

Ory Kratos: What does headless mean here?

Kratos provides identity flows as APIs and no user interface. You build the login, registration and recovery screens yourself.

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.

Ory Kratos: Does Kratos do OAuth2?

No. Kratos handles user identity; OAuth2 and OpenID Connect provider functionality is Ory Hydra, a separate component.

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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