Machine Learning · head to head
scikit-learn vs Syft
Syft
Cybersecurity
Generates a software bill of materials from images, filesystems and archives
- From
- Free
- Rated
- -
The short version
- Each has a real cost: scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays; Syft lockfile parsing can drop packages silently. An open issue filed in August 2026 reports the yarn v1 cataloguer returning 118 of 745 packages with no error raised, which means a complete bill of materials and an 84 percent incomplete one look identical to the caller.
- They diverge on capability: scikit-learn covers Classification algorithms, Syft covers Multi-format output.
- Prices and features above were last checked on 31 August 2026.
Where they differ
Only the attributes on which scikit-learn and Syft actually diverge.
| Attribute | scikit-learn | Syft |
|---|---|---|
| Pricing model | Unknown | Open source, no licence fee |
| Platforms | Python, Linux, macOS, Windows | macOS, Linux, Windows, Docker |
| Category | Machine Learning | Cybersecurity |
| Founded | 2007 | Unknown |
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 Syft
- Multi-format output
- Broad ecosystem coverage
- Binary classifiers
- In-toto attestations
- Library and CLI
- Pairs with Grype
What people use each for
The jobs each tool is most often brought in to do.
scikit-learn
- Machine learningnot Syft
- Data analysisnot Syft
- Model trainingnot Syft
- Predictive analyticsnot Syft
Syft
- Producing a bill of materials for a customer or regulator that requires onenot scikit-learn
- Feeding an inventory into a vulnerability scanner rather than scanning images directlynot scikit-learn
- Recording what shipped in a build so a future disclosure can be answered quicklynot scikit-learn
- Public sector work where an SBOM is a contractual deliverablenot 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
Syft
- Lockfile parsing can drop packages silently. An open issue filed in August 2026 reports the yarn v1 cataloguer returning 118 of 745 packages with no error raised, which means a complete bill of materials and an 84 percent incomplete one look identical to the caller.
- Fidelity varies sharply by ecosystem. Conan for C and C++, Haskell and Terraform get cataloguer support with no licence data, no dependency relationships and no file ownership, so a C and C++ shop gets the least from it.
- Binary classification yields no licence or dependency metadata, and vendored or statically linked code is exactly where supply chain risk hides, so the blind spot and the risk overlap.
- Incorrect CPE values and CPE collisions are recorded as open issues, and since Grype matches on CPE and PURL, an inventory error becomes a false negative in the security report downstream.
- An inventory is not a risk assessment. Even a perfect bill of materials says a vulnerable version is present, never that the vulnerable function is called, and the triage burden lands entirely on the reader.
Pricing, plan by plan
scikit-learn
FreeNo published plan breakdown. See the scikit-learn review.
Syft
Free- SyftFree
- Apache-2.0
- No usage limits
- Community support
- Anchore Enterprise$undefined/year
- Policy enforcement and reporting
- Federal and commercial tiers
- Pricing not published, quoted on request
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.
Choose Syft if
- You need multi-format output.
- You want to start without paying.
- You work on macOS, Linux, Windows, Docker.
- You also want broad ecosystem coverage.
Questions people ask
- Is scikit-learn or Syft better?
- Neither clearly leads. scikit-learn starts at Free and Syft at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, scikit-learn or Syft?
- scikit-learn starts at Free and Syft at Free.
- Does scikit-learn or Syft run on more platforms?
- scikit-learn runs on Python, Linux, macOS, Windows. Syft runs on macOS, Linux, Windows, Docker.
- 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 Syft is typically brought in for.
- What can scikit-learn do that Syft cannot?
- scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction. Syft covers Multi-format output, Broad ecosystem coverage, Binary classifiers, In-toto attestations.
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.
SourceSyft: Does Syft find vulnerabilities?
No. It produces an inventory. Grype, from the same company, matches that inventory against vulnerability feeds. They are separate tools and the distinction is frequently lost.
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.
SourceSyft: Does anything in the Anchore stack do reachability analysis?
No. Neither Syft, Grype nor the commercial Anchore platform performs call graph or reachability analysis, so none of them tells you whether a vulnerable code path is actually invoked.
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.
SourceSyft: Is it a CNCF or OpenSSF project?
No. It is single-vendor open source owned by Anchore, with no foundation governance. That is a different licence risk profile from Sigstore.
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.
SourceSyft: What does Anchore Enterprise cost?
Not published. The pricing page is contact-sales only, with named but unpriced commercial and federal tiers.
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.
SourceSyft: How do I know my SBOM is complete?
You largely cannot, which is the honest answer. Silent partial parsing is a known open defect, so a bill of materials used for compliance should be spot-checked against a known dependency list.
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.
SourceRelated pages
More on scikit-learn
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- Syft vs H2O.ai
- Syft vs Weka
- Syft vs BigQuery ML
- Syft vs Jupyter
- Syft vs Python
- Syft vs Anaconda
- Syft vs AWS SageMaker
- Syft vs ClearML
- Syft vs Cohere
- Syft vs Dask
- Syft vs Fal AI
- Syft vs Groq
- Syft vs TensorFlow
- Syft vs Google Vertex AI
- Syft vs Cosign
- Syft vs Sigstore
- Syft vs Trivy
- Syft vs Chainguard
- Syft vs Metasploit
- Syft vs Wireshark
- Syft vs Semgrep
- Syft vs Legit Security
- Syft vs OWASP ZAP
- Syft vs HashiCorp Vault
- Syft vs Bitwarden
- Syft vs Infisical
- Syft vs Tenable Nessus
- Syft vs Transmit Security
- Syft vs TrustArc
- Syft vs Varonis Data Security Platform
- Syft vs VMware Carbon Black