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Cybersecurity · head to head

Logto vs Apache Spark MLlib

Logto logo

Logto

Cybersecurity

Open-source identity and authentication infrastructure for apps and APIs

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: Logto advanced features like RBAC, organizations, MFA, and enterprise SSO are billed as separate paid add-ons rather than included in Pro.; 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: Logto covers Prebuilt sign-in UI, 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 Logto and Apache Spark MLlib actually diverge.

Attributes where Logto and Apache Spark MLlib differ
AttributeLogtoApache Spark MLlib
Pricing modelfreemiumopen-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 Logto

  • Prebuilt sign-in UI
  • Social connectors
  • Role-based access control
  • Multi-factor authentication
  • Machine-to-machine apps
  • Audit logs

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.

Logto

  • Adding sign-in to a new SaaS productnot Apache Spark MLlib
  • Implementing SSO for B2B customersnot Apache Spark MLlib
  • Securing internal APIs with M2M tokensnot Apache Spark MLlib
  • Adding RBAC and organizations to a multi-tenant appnot 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 Logto
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Logto
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Logto
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Logto

Where each one falls short

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

Logto

  • Advanced features like RBAC, organizations, MFA, and enterprise SSO are billed as separate paid add-ons rather than included in Pro.
  • Enterprise pricing is not published and requires contacting sales.
  • Token-based billing can make cost forecasting harder than flat per-MAU pricing for high-traffic apps.

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

Logto

Free
  • FreeFree
    • Up to 50,000 MAU
    • 50K tokens included
    • 3 applications
  • Pro$24/month
    • Unlimited MAU and applications
    • 3 included API resources
    • Passkey sign-in

Apache Spark MLlib

Free

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

Which should you pick?

Choose Logto if

  • You need prebuilt sign-in ui.
  • You want to start without paying.
  • You work on web, api.
  • You also want social connectors.
Logto 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 Logto or Apache Spark MLlib better?
Neither clearly leads. Logto 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, Logto or Apache Spark MLlib?
Logto starts at Free and Apache Spark MLlib at Free.
Does Logto or Apache Spark MLlib run on more platforms?
Logto runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Logto for free?
Both have a free tier, so you can try either at no cost before committing.
What is Logto best used for?
Logto is most often used for adding sign-in to a new saas product, implementing sso for b2b customers, securing internal apis with m2m tokens, adding rbac and organizations to a multi-tenant app. Of those, adding sign-in to a new saas product and implementing sso for b2b customers are not what Apache Spark MLlib is typically brought in for.
What can Logto do that Apache Spark MLlib cannot?
Logto covers Prebuilt sign-in UI, Social connectors, Role-based access control, Multi-factor authentication. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Logto: What does Logto cost?

Logto offers a Free plan at $0/month, a Pro plan starting at $24/month with 50K free tokens included, and a custom-priced Enterprise plan. Extra add-ons like RBAC or MFA are billed separately on Pro.

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.

Logto: Is there a free plan, and what are its limits?

Yes. The Free plan supports up to 50,000 monthly active users, 3 total applications, 1 machine-to-machine app, 3 social connectors, 1 webhook, and 3-day audit log retention.

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.

Logto: How is usage metered?

Logto uses token-based billing: only access tokens issued for authentication and authorization activity are counted, rather than charging purely per MAU.

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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