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Cybersecurity · head to head

Chainguard vs Apache Spark MLlib

Chainguard logo

Chainguard

Cybersecurity

Secure-by-default open source software with hardened container images and libraries

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: Chainguard containers Catalog at 19,000 USD/year expensive for teams under 10 people; 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: Chainguard covers Hardened container images, 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 Chainguard and Apache Spark MLlib actually diverge.

Attributes where Chainguard and Apache Spark MLlib differ
AttributeChainguardApache Spark MLlib
Pricing modelLicensing by artifact type and team sizeopen-source
PlatformsCloud, Container, VMLinux, 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 Chainguard

  • Hardened container images
  • CVE remediation SLA
  • SLSA L2/L3 builds
  • Sigstore signatures
  • SBOM generation
  • Language libraries
  • VM images
  • Artifact scanning

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.

Chainguard

  • Deploying hardened container images with minimal attack surfacenot Apache Spark MLlib
  • Meeting supply chain security requirements for regulated industriesnot Apache Spark MLlib
  • Reducing CVE exposure with contractual remediation guaranteesnot Apache Spark MLlib
  • Building secure language packages with automatic backportsnot Apache Spark MLlib
  • Verifying artifact provenance with Sigstore signaturesnot 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 Chainguard
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Chainguard
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Chainguard
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Chainguard

Where each one falls short

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

Chainguard

  • Containers Catalog at 19,000 USD/year expensive for teams under 10 people
  • Per-image pricing for containers requires custom quotes with no transparency
  • Free tier limited to 5 container images for testing
  • Libraries pricing by ecosystem and developer count lacks transparent per-developer cost
  • VM image catalog pricing opacity makes cost estimation difficult

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

Chainguard

Free
  • Free TierFree
    • Five container images to test and deploy
  • Containers Per-Image$undefined/custom
    • Licensed by quantity and type
    • Base images, application images, AI/ML images, FIPS variants
    • Custom pricing per image
  • Containers Catalog$19000/year
    • For 10-person engineering teams
    • 2,000+ container images
    • Contractual CVE remediation SLAs
  • Libraries Licensing$undefined/custom
    • Licensed by ecosystem (Python, Java, JavaScript)
    • Licensed by developer count
    • Unlimited pulls with no metering

Apache Spark MLlib

Free

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

Which should you pick?

Choose Chainguard if

  • You need hardened container images.
  • You want to start without paying.
  • You work on Cloud, Container, VM.
  • You also want cve remediation sla.
Chainguard 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 Chainguard or Apache Spark MLlib better?
Neither clearly leads. Chainguard 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, Chainguard or Apache Spark MLlib?
Chainguard starts at Free and Apache Spark MLlib at Free.
Does Chainguard or Apache Spark MLlib run on more platforms?
Chainguard runs on Cloud, Container, VM. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Chainguard for free?
Both have a free tier, so you can try either at no cost before committing.
What is Chainguard best used for?
Chainguard is most often used for deploying hardened container images with minimal attack surface, meeting supply chain security requirements for regulated industries, reducing cve exposure with contractual remediation guarantees, building secure language packages with automatic backports. Of those, deploying hardened container images with minimal attack surface and meeting supply chain security requirements for regulated industries are not what Apache Spark MLlib is typically brought in for.
What can Chainguard do that Apache Spark MLlib cannot?
Chainguard covers Hardened container images, CVE remediation SLA, SLSA L2/L3 builds, Sigstore signatures. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Chainguard: How much is the Chainguard Containers Catalog?

The Containers Catalog is 19,000 USD per year for 10-person engineering teams, providing access to 2,000+ hardened container images.

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.

Chainguard: What SLAs does Chainguard offer?

Chainguard provides contractual CVE remediation SLAs: 7 days for critical vulnerabilities, 14 days for high/medium/low severity, all with priority support.

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.

Chainguard: Can I try Chainguard before purchasing?

Yes. The free tier includes five container images for testing and deployment, allowing hands-on evaluation.

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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