Softwr

Cybersecurity · head to head

Semgrep vs Apache Spark MLlib

Semgrep logo

Semgrep

Cybersecurity

Open-source static analysis tool for finding security bugs and enforcing code standards.

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: Semgrep free tier caps out at 10 contributors and 10 repositories.; 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: Semgrep covers Static code 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 Semgrep and Apache Spark MLlib actually diverge.

Attributes where Semgrep and Apache Spark MLlib differ
AttributeSemgrepApache Spark MLlib
Pricing modelfreemiumopen-source
Platformsweb, api, linux, mac, windowsLinux, 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 Semgrep

  • Static code scanning
  • Supply chain scanning
  • Secrets detection
  • Cross-file analysis
  • AI-powered triage and remediation
  • CI/CD integration

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.

Semgrep

  • Scanning code for security vulnerabilities in CI/CDnot Apache Spark MLlib
  • Detecting vulnerable open-source dependenciesnot Apache Spark MLlib
  • Finding hardcoded secrets before code shipsnot Apache Spark MLlib
  • Enforcing custom code standards with rule setsnot Apache Spark MLlib
  • Prioritizing findings with AI-assisted triagenot 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 Semgrep
  • Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Semgrep
  • Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Semgrep
  • Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot Semgrep

Where each one falls short

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

Semgrep

  • Free tier caps out at 10 contributors and 10 repositories.
  • Secrets scanning is priced as a separate module ($15/contributor) from Code and Supply Chain.
  • Self-managed repositories and custom CI/CD require the Enterprise tier.
  • AI credits are limited per tier and additional usage requires upgrading.

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

Semgrep

Free
  • FreeFree
    • Up to 10 contributors
    • Code and Supply Chain scanning
    • 60 AI credits total
  • Teams$30/month
    • Code, Supply Chain, or Secrets scanning per contributor
    • Pro rules
    • AI-powered triage and remediation
  • Enterprise$undefined/month
    • On-prem support
    • Custom CI/CD
    • 50 AI credits per developer/month

Apache Spark MLlib

Free

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

Which should you pick?

Choose Semgrep if

  • You need static code scanning.
  • You want to start without paying.
  • You work on web, api, linux, mac, windows.
  • You also want supply chain scanning.
Semgrep 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 Semgrep or Apache Spark MLlib better?
Neither clearly leads. Semgrep 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, Semgrep or Apache Spark MLlib?
Semgrep starts at Free and Apache Spark MLlib at Free.
Does Semgrep or Apache Spark MLlib run on more platforms?
Semgrep runs on web, api, linux, mac, windows. Apache Spark MLlib runs on Linux, macOS, Windows.
Can I use Semgrep for free?
Both have a free tier, so you can try either at no cost before committing.
What is Semgrep best used for?
Semgrep is most often used for scanning code for security vulnerabilities in ci/cd, detecting vulnerable open-source dependencies, finding hardcoded secrets before code ships, enforcing custom code standards with rule sets. Of those, scanning code for security vulnerabilities in ci/cd and detecting vulnerable open-source dependencies are not what Apache Spark MLlib is typically brought in for.
What can Semgrep do that Apache Spark MLlib cannot?
Semgrep covers Static code scanning, Supply chain scanning, Secrets detection, Cross-file analysis. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.

Answered from the vendors’ own pages

Semgrep: What does Semgrep cost?

The Free edition covers up to 10 contributors; Teams starts at $30/contributor/month for Code scanning (Supply Chain also $30, Secrets $15); Enterprise is custom-priced.

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.

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

Yes, the Free edition supports up to 10 contributors and 10 repositories with Code and Supply Chain scanning plus 60 AI credits total.

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.

Semgrep: How is usage metered?

Pricing is per contributor, defined as someone who made at least one commit to a scanned private repository in the past 90 days.

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.

Semgrep: Is there special pricing for startups?

Yes, Semgrep offers special startup pricing upon request for early-stage companies.

Source
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.

Share

Related pages

More on Semgrep

More on Apache Spark MLlib

Keep looking

Other head to heads