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Machine Learning · head to head

scikit-learn vs Semgrep

scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite
Semgrep logo

Semgrep

Cybersecurity

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

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays; Semgrep free tier caps out at 10 contributors and 10 repositories.
  • They diverge on capability: scikit-learn covers Classification algorithms, Semgrep covers Static code scanning.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which scikit-learn and Semgrep actually diverge.

Attributes where scikit-learn and Semgrep differ
Attributescikit-learnSemgrep
Pricing modelUnknownfreemium
PlatformsPython, Linux, macOS, Windowsweb, api, linux, mac, windows
CategoryMachine LearningCybersecurity
Founded2007Unknown

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 scikit-learn

  • Classification algorithms
  • Regression models
  • Clustering methods
  • Dimensionality reduction
  • Model selection
  • NumPy
  • SciPy
  • Pandas

Only in Semgrep

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

What people use each for

The jobs each tool is most often brought in to do.

scikit-learn

  • Machine learningnot Semgrep
  • Data analysisnot Semgrep
  • Model trainingnot Semgrep
  • Predictive analyticsnot Semgrep

Semgrep

  • Scanning code for security vulnerabilities in CI/CDnot scikit-learn
  • Detecting vulnerable open-source dependenciesnot scikit-learn
  • Finding hardcoded secrets before code shipsnot scikit-learn
  • Enforcing custom code standards with rule setsnot scikit-learn
  • Prioritizing findings with AI-assisted triagenot scikit-learn

Where each one falls short

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

scikit-learn

  • No GPU acceleration by default; limited optional GPU support requires external arrays
  • Single-machine only; no built-in distributed computing across clusters
  • All datasets must fit entirely in RAM; no out-of-core learning
  • No production-grade deep learning; neural network support limited to basic multilayer perceptron
  • No reinforcement learning algorithms

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.

Pricing, plan by plan

scikit-learn

Free

No published plan breakdown. See the scikit-learn review.

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

Which should you pick?

Choose scikit-learn if

  • You need classification algorithms.
  • You want to start without paying.
  • You work on Python, Linux, macOS, Windows.
  • You also want regression models.
scikit-learn review

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

Questions people ask

Is scikit-learn or Semgrep better?
Neither clearly leads. scikit-learn starts at Free and Semgrep at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, scikit-learn or Semgrep?
scikit-learn starts at Free and Semgrep at Free.
Does scikit-learn or Semgrep run on more platforms?
scikit-learn runs on Python, Linux, macOS, Windows. Semgrep runs on web, api, linux, mac, windows.
Can I use scikit-learn for free?
Both have a free tier, so you can try either at no cost before committing.
What is scikit-learn best used for?
scikit-learn is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Semgrep is typically brought in for.
What can scikit-learn do that Semgrep cannot?
scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Semgrep covers Static code scanning, Supply chain scanning, Secrets detection, Cross-file analysis.

Answered from the vendors’ own pages

scikit-learn: Does scikit-learn support GPU acceleration?

Scikit-learn has no native GPU support by design to keep installation simple and cross-platform. Since 2023, a limited number of estimators can run on GPUs if input data is provided as PyTorch or CuPy arrays, but this requires additional setup.

Source
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
scikit-learn: Can scikit-learn handle datasets larger than RAM?

No. Scikit-learn is built on NumPy which requires all data to fit in memory, and NumPy operates on single-machine CPUs only. For very large datasets, consider Spark MLlib or distributed alternatives.

Source
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
scikit-learn: Is scikit-learn free to use commercially?

Yes. Scikit-learn is open source under the BSD license, which allows free commercial use, modification, and distribution.

Source
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
scikit-learn: What neural network capabilities does scikit-learn have?

Scikit-learn includes only a basic multilayer perceptron (MLPClassifier and MLPRegressor) for simple feedforward networks. For serious deep learning, use PyTorch, TensorFlow, or Keras instead.

Source
Semgrep: Is there special pricing for startups?

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

Source
scikit-learn: Does scikit-learn include natural language processing?

Scikit-learn has minimal NLP support limited to basic text feature extraction and vectorization. For comprehensive text processing, use spaCy or NLTK instead.

Source
scikit-learn: When was scikit-learn first released?

Scikit-learn's first public release was February 1, 2010, following its start as a Google Summer of Code project in 2007.

Source
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