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Cybersecurity · head to head

OWASP ZAP vs Apache Spark MLlib

OWASP ZAP logo

OWASP ZAP

Cybersecurity

Free, open-source web application scanner and intercepting proxy, now governed by the Software Security Project.

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: OWASP ZAP authenticated scanning of modern single-page applications is the hard part and ZAP makes you build it by hand: session handling, token refresh and login scripts are configured per application, and a misconfigured session means the scanner logs itself out and reports a clean result for pages it never reached.; 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: OWASP ZAP covers Intercepting proxy, 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 OWASP ZAP and Apache Spark MLlib actually diverge.

Attributes where OWASP ZAP and Apache Spark MLlib differ
AttributeOWASP ZAPApache Spark MLlib
Pricing modelfreeopen-source
PlatformsDesktop, Cli, ApiLinux, macOS, Windows
CategoryCybersecurityMachine Learning
Founded20011999

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 OWASP ZAP

  • Intercepting proxy
  • Passive scanner
  • Active scanner
  • AJAX spider
  • Automation Framework
  • Headless daemon and REST API
  • Docker images
  • Add-on marketplace

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.

OWASP ZAP

  • Adding a baseline security scan to every application's pipeline where per-target commercial licensing would limit coverage to a handfulnot Apache Spark MLlib
  • Manual penetration testing that needs an intercepting proxy, request replay and fuzzing without a paid licence per testernot Apache Spark MLlib
  • Teaching developers what an attack against their own endpoint looks like, using a tool they can install themselvesnot Apache Spark MLlib
  • Pre-release regression scanning of an internal application that would never justify a commercial DAST subscriptionnot 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 OWASP ZAP
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot OWASP ZAP
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot OWASP ZAP
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot OWASP ZAP

Where each one falls short

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

OWASP ZAP

  • Authenticated scanning of modern single-page applications is the hard part and ZAP makes you build it by hand: session handling, token refresh and login scripts are configured per application, and a misconfigured session means the scanner logs itself out and reports a clean result for pages it never reached.
  • There is no support contract in the product, so when a scan breaks the day before a release the escalation path is a GitHub issue and a community chat, which is not an answer that satisfies a delivery manager or an auditor who wants a named responsible party.
  • Active scanning sends genuine attack traffic, so it can create records, trigger emails, exhaust rate limits or destabilise a fragile environment, and pointing it at production without prior agreement produces an incident rather than a test result.
  • Output needs triage: passive rules generate large volumes of low-severity informational findings about headers and cookie flags that bury the few results that matter, and a team without someone tuning the rule set stops reading the report within a few sprints.
  • As a dynamic scanner it can only test what it can reach, so authorisation flaws between accounts, business logic abuse and anything behind an undiscovered endpoint go unreported, and a passing ZAP scan is evidence of nothing more than the absence of the classes of bug it looks for.

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

OWASP ZAP

Free
  • Free & Open SourceFree
    • Full functionality
    • Active & passive scanning
    • Spider

Apache Spark MLlib

Free

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

Which should you pick?

Choose OWASP ZAP if

  • You need intercepting proxy.
  • You want to start without paying.
  • You work on Desktop, Cli, Api.
  • You also want passive scanner.
OWASP ZAP 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 OWASP ZAP or Apache Spark MLlib better?
Neither clearly leads. OWASP ZAP 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, OWASP ZAP or Apache Spark MLlib?
OWASP ZAP starts at Free and Apache Spark MLlib at Free.
Does OWASP ZAP or Apache Spark MLlib run on more platforms?
OWASP ZAP runs on Desktop, Cli, Api. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use OWASP ZAP for free?
Both have a free tier, so you can try either at no cost before committing.
What is OWASP ZAP best used for?
OWASP ZAP is most often used for adding a baseline security scan to every application's pipeline where per-target commercial licensing would limit coverage to a handful, manual penetration testing that needs an intercepting proxy, request replay and fuzzing without a paid licence per tester, teaching developers what an attack against their own endpoint looks like, using a tool they can install themselves, pre-release regression scanning of an internal application that would never justify a commercial dast subscription. Of those, adding a baseline security scan to every application's pipeline where per-target commercial licensing would limit coverage to a handful and manual penetration testing that needs an intercepting proxy, request replay and fuzzing without a paid licence per tester are not what Apache Spark MLlib is typically brought in for.
What can OWASP ZAP do that Apache Spark MLlib cannot?
OWASP ZAP covers Intercepting proxy, Passive scanner, Active scanner, AJAX spider. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

OWASP ZAP: Is it still called OWASP ZAP?

The project left OWASP in August 2024 and is now governed by the Software Security Project, with core development sponsored by Checkmarx. The tool is now just ZAP, though most existing documentation, courses and search results still use the OWASP name.

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.

OWASP ZAP: Is it free for commercial use?

Yes. It is Apache 2.0 licensed, with no per-application, per-scan or per-user cost, and it can be used and modified commercially without a licence agreement.

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.

OWASP ZAP: Can it replace a penetration test?

No. It automates checks for known vulnerability classes against endpoints it can reach. It does not reason about business logic, chain findings into an attack, or test authorisation between accounts, which is most of what a tester actually does.

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.

OWASP ZAP: Does it run in CI?

Yes, through the official Docker images and the Automation Framework, which defines scan jobs in YAML so configuration lives in the repository. A baseline passive scan is the usual starting point because it is fast and non-intrusive.

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.

OWASP ZAP: How does it compare to Burp Suite?

Burp Suite Professional is the more polished manual testing tool and has a stronger scanner and extension ecosystem, but it is licensed per tester and Burp Suite Enterprise per target. ZAP is the better fit where cost per target is the binding constraint; many teams use both.

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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