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Cybersecurity · head to head

Ory vs Apache Spark MLlib

Ory logo

Ory

Cybersecurity

Open-source identity, authentication, and permissions infrastructure

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 production and Growth plans are billed annually ($770/year and $9,350/year), which is a larger upfront commitment than monthly-only competitors.; 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 covers Authentication APIs, 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 and Apache Spark MLlib actually diverge.

Attributes where Ory and Apache Spark MLlib differ
AttributeOryApache Spark MLlib
Pricing modelusage-basedopen-source
Platformsweb, apiLinux, 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

  • Authentication APIs
  • Permissions engine
  • Machine-to-machine tokens
  • B2B organizations
  • SAML SSO
  • Multi-region deployments

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

  • Adding self-hosted or cloud identity to a new productnot Apache Spark MLlib
  • Implementing fine-grained permission checksnot Apache Spark MLlib
  • Supporting B2B organizations and multi-tenancynot Apache Spark MLlib
  • Building machine-to-machine authentication for microservicesnot 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
  • 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
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Ory
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Ory

Where each one falls short

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

Ory

  • Production and Growth plans are billed annually ($770/year and $9,350/year), which is a larger upfront commitment than monthly-only competitors.
  • SAML SSO and multi-region deployments are reserved for the custom-priced Enterprise tier.
  • Usage-based pricing across aDAU, M2M tokens, and permission checks makes cost estimation more complex than flat per-MAU billing.

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

Free
  • DeveloperFree
    • Community support
    • No production environments
  • Production$64/month
    • $21 monthly credit included
    • 1 production environment
    • 3 staging environments
  • Growth$779/month
    • $255 monthly credit included
    • 2 production environments
    • B2B organizations (max 3)

Apache Spark MLlib

Free

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

Which should you pick?

Choose Ory if

  • You need authentication apis.
  • You want to start without paying.
  • You work on web, api.
  • You also want permissions engine.
Ory 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 or Apache Spark MLlib better?
Neither clearly leads. Ory 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 or Apache Spark MLlib?
Ory starts at Free and Apache Spark MLlib at Free.
Does Ory or Apache Spark MLlib run on more platforms?
Ory runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Ory for free?
Both have a free tier, so you can try either at no cost before committing.
What is Ory best used for?
Ory is most often used for adding self-hosted or cloud identity to a new product, implementing fine-grained permission checks, supporting b2b organizations and multi-tenancy, building machine-to-machine authentication for microservices. Of those, adding self-hosted or cloud identity to a new product and implementing fine-grained permission checks are not what Apache Spark MLlib is typically brought in for.
What can Ory do that Apache Spark MLlib cannot?
Ory covers Authentication APIs, Permissions engine, Machine-to-machine tokens, B2B organizations. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Ory: What does Ory cost?

Ory has a free Developer tier, a Production plan at $770/year including a $21 monthly credit, a Growth plan at $9,350/year including a $255 monthly credit, and custom Enterprise pricing.

Source
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: How is usage metered?

Beyond the included credit, Ory charges per average daily active user (aDAU), per machine-to-machine token, and per permission check, with lower per-unit rates on the Growth plan.

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.

Ory: What payment methods are supported?

Ory accepts credit cards (Visa, MasterCard, Amex) and bank transfer, processed via Stripe.

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.

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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