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Cybersecurity · head to head

Feedzai vs Apache Spark MLlib

Feedzai logo

Feedzai

Cybersecurity

Real-time transaction fraud and financial crime detection for banks and payment processors

From
On request
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

  • Only Apache Spark MLlib has a free tier, so it costs nothing to try first.
  • Each has a real cost: Feedzai pricing is per transaction with an annual minimum, so a bank with seasonal or growing volume commits to a floor it may not use and pays overage above the band.; 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: Feedzai covers Real-time scoring, Apache Spark MLlib covers DataFrame-based pipelines.
  • Prices and features above were last checked on 1 September 2026.

Where they differ

Only the attributes on which Feedzai and Apache Spark MLlib actually diverge.

Attributes where Feedzai and Apache Spark MLlib differ
AttributeFeedzaiApache Spark MLlib
Starting priceOn requestFree
Pricing modelquoteopen-source
Free tierNoYes
PlatformsWeb, LinuxLinux, macOS, Windows
CategoryCybersecurityMachine Learning
FoundedUnknown1999

Identical on both: 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 Feedzai

  • Real-time scoring
  • Rule and model hybrid
  • Case manager
  • Behavioural biometrics
  • Model explainability
  • Deployment options

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.

Feedzai

  • A bank joining an instant payments scheme where transfers are irrevocable and post-hoc recovery is impossiblenot Apache Spark MLlib
  • A card issuer whose existing rules engine cannot be changed without a release, so fraud waves run for daysnot Apache Spark MLlib
  • An acquirer needing per-merchant risk models rather than one portfolio-wide modelnot Apache Spark MLlib
  • A bank required by its regulator to explain automated declines to customers, which rules out opaque scoringnot 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 Feedzai
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Feedzai
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Feedzai
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Feedzai

Where each one falls short

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

Feedzai

  • Pricing is per transaction with an annual minimum, so a bank with seasonal or growing volume commits to a floor it may not use and pays overage above the band.
  • It sits in the authorisation path, which makes every upgrade a change-controlled event with rollback plans, and the operational burden falls on the bank rather than the vendor.
  • Out of the box models need months of the customer own labelled fraud history before they beat the rules they replace, so the value case starts late.
  • AML and fraud are licensed as separate modules, so institutions expecting one platform fee find the transaction monitoring capability is a second line item.
  • The buyer profile is large institutions, so smaller banks and fintechs face minimums that make per-transaction economics unattractive below significant scale.

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

Feedzai

On request
  • Feedzai Financial Crime Platform$undefined/year
    • Priced by transaction volume with annual minimum commitment
    • Modules for fraud, AML and account opening licensed separately
    • Cloud, private cloud and on-premises deployment

Apache Spark MLlib

Free

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

Which should you pick?

Choose Feedzai if

  • You need real-time scoring.
  • You work on Web, Linux.
  • You also want rule and model hybrid.
Feedzai 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 Feedzai or Apache Spark MLlib better?
Neither clearly leads. Feedzai starts at On request and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Feedzai or Apache Spark MLlib?
Apache Spark MLlib has a free tier; the other does not. Paid plans start at On request for Feedzai and Free for Apache Spark MLlib.
Does Feedzai or Apache Spark MLlib run on more platforms?
Feedzai runs on Web, Linux. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Apache Spark MLlib for free?
Yes. Apache Spark MLlib has a free tier, so you can try it without paying. Feedzai starts at On request.
What is Feedzai best used for?
Feedzai is most often used for a bank joining an instant payments scheme where transfers are irrevocable and post-hoc recovery is impossible, a card issuer whose existing rules engine cannot be changed without a release, so fraud waves run for days, an acquirer needing per-merchant risk models rather than one portfolio-wide model, a bank required by its regulator to explain automated declines to customers, which rules out opaque scoring. Of those, a bank joining an instant payments scheme where transfers are irrevocable and post-hoc recovery is impossible and a card issuer whose existing rules engine cannot be changed without a release, so fraud waves run for days are not what Apache Spark MLlib is typically brought in for.
What can Feedzai do that Apache Spark MLlib cannot?
Feedzai covers Real-time scoring, Rule and model hybrid, Case manager, Behavioural biometrics. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Feedzai: Can Feedzai run on-premises?

Yes. On-premises and private cloud deployments are supported, which is why it appears in markets where transaction data cannot legally leave the country.

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.

Feedzai: Does it cover AML as well as fraud?

It does, but transaction monitoring is a separately licensed module. Assume two line items if you want both.

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.

Feedzai: How fast are decisions?

Designed for the authorisation window, typically tens of milliseconds. This is the constraint that rules out batch scoring architectures.

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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