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Cybersecurity · head to head

Grype vs Apache Spark MLlib

Grype logo

Grype

Cybersecurity

Vulnerability scanner for container images and filesystems

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: Grype depends on public vulnerability databases, so coverage and false positives vary by ecosystem; 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: Grype covers Image and filesystem scanning, 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 Grype and Apache Spark MLlib actually diverge.

Attributes where Grype and Apache Spark MLlib differ
AttributeGrypeApache Spark MLlib
Pricing modelOpen source, no licence feeopen-source
PlatformsLinux, macOS, Windows, DockerLinux, 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 Grype

  • Image and filesystem scanning
  • SBOM-driven
  • Wide ecosystem coverage
  • Pipeline friendly

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.

Grype

  • Re-scanning stored SBOMs as new CVEs are published, without rebuilding imagesnot Apache Spark MLlib
  • Failing CI when a build introduces a known vulnerabilitynot Apache Spark MLlib
  • Auditing what is actually installed inside a third-party imagenot 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 Grype
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Grype
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Grype
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Grype

Where each one falls short

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

Grype

  • Depends on public vulnerability databases, so coverage and false positives vary by ecosystem
  • No triage, exception tracking or reporting UI — that is Anchore’s commercial product
  • Overlaps heavily with Trivy, and most teams pick one rather than running both

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

Grype

Free
  • GrypeFree
    • Full functionality
    • No usage limits
    • Community support

Apache Spark MLlib

Free

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

Which should you pick?

Choose Grype if

  • You need image and filesystem scanning.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, Docker.
  • You also want sbom-driven.
Grype 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 Grype or Apache Spark MLlib better?
Neither clearly leads. Grype 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, Grype or Apache Spark MLlib?
Grype starts at Free and Apache Spark MLlib at Free.
Does Grype or Apache Spark MLlib run on more platforms?
Grype runs on Linux, macOS, Windows, Docker. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Grype for free?
Both have a free tier, so you can try either at no cost before committing.
What is Grype best used for?
Grype is most often used for re-scanning stored sboms as new cves are published, without rebuilding images, failing ci when a build introduces a known vulnerability, auditing what is actually installed inside a third-party image. Of those, re-scanning stored sboms as new cves are published, without rebuilding images and failing ci when a build introduces a known vulnerability are not what Apache Spark MLlib is typically brought in for.
What can Grype do that Apache Spark MLlib cannot?
Grype covers Image and filesystem scanning, SBOM-driven, Wide ecosystem coverage, Pipeline friendly. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Grype: Is Grype free?

Yes, open source from Anchore. Anchore Enterprise is the paid platform around it.

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.

Grype: What is the difference between Grype and Syft?

Syft generates the software bill of materials; Grype matches that inventory against vulnerability data. They are designed to be used together.

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.

Grype: Grype or Trivy?

They cover similar ground. Trivy is broader out of the box, including misconfiguration and secret scanning; Grype pairs more cleanly with an SBOM-first workflow.

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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