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Immuta vs scikit-learn

Immuta logo

Immuta

Databases

Attribute-based access control and masking applied inside Snowflake, Databricks and BigQuery

From
On request
Free plan
No
Visit ImmutaFull review
scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Free plan
Yes
Visit scikit-learnFull review

The short version

  • scikit-learn is a free, open-source machine learning library for Python.
  • scikit-learn is free; Immuta prices on request.
  • Only Immuta lists attribute-based policy; only scikit-learn lists classification algorithms.
  • Where scikit-learn falls short: no GPU acceleration by default; limited optional GPU support requires external arrays.
  • Prices and features on this page were last checked on 31 August 2026.

Where they differ

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

Attributes where Immuta and scikit-learn differ
AttributeImmutascikit-learn
Starting priceOn requestFree
Free tierNoYes
PlatformsWeb, APIAPI, Linux, macOS, Windows
CategoryDatabasesMachine Learning

Not yet recorded: pricing model for scikit-learn, founded for Immuta.

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 Immuta

  • Attribute-based policy
  • Native enforcement
  • Dynamic masking
  • Row-level filtering
  • Purpose-based access
  • Sensitive data tagging
  • Audit logging
  • Multi-platform

Only in scikit-learn

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

What people use each for

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

Immuta

  • A bank whose Snowflake estate has grown to tens of thousands of roles that no one can review before an auditnot scikit-learn
  • A healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recordednot scikit-learn
  • A multinational applying different residency and access rules per jurisdiction to the same tables without duplicating datasetsnot scikit-learn
  • An organisation running both Snowflake and Databricks that wants one policy set rather than two divergent implementationsnot scikit-learn

scikit-learn

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

Where each one falls short

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

Immuta

  • Contracts commonly start around one hundred to two hundred thousand US dollars a year for mid-market deployments and exceed five hundred thousand at enterprise scale, which excludes most data teams without a regulatory mandate
  • Policy is only as good as the data classification underneath it, so an organisation with poorly tagged columns will spend months on classification before Immuta enforces anything useful
  • Native enforcement means capability varies by platform, and a feature available on Snowflake may be absent or behave differently on BigQuery, which undermines the promise of one policy set everywhere
  • Adding an access governance layer creates a new dependency in the path to data: a misconfigured policy silently returns fewer rows rather than erroring, and analysts can act on incomplete results without noticing
  • It governs cloud data platforms, so personal data in operational databases, files and SaaS applications sits outside its scope and needs separate controls, meaning Immuta is rarely the whole answer

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

Pricing, plan by plan

Immuta

On request
  • Immuta PlatformOn request
    • Attribute-based policy authoring
    • Native enforcement in supported data platforms
    • Dynamic masking and row-level security

scikit-learn

Free

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

Which should you pick?

Choose Immuta if

  • You need attribute-based policy.
  • You work on Web, API.
  • You also want native enforcement.
Immuta review

Choose scikit-learn if

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

Questions people ask

Is Immuta or scikit-learn better?
Neither clearly leads. Immuta starts at On request and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Immuta or scikit-learn?
scikit-learn has a free tier; the other does not. Paid plans start at On request for Immuta and Free for scikit-learn.
Does Immuta or scikit-learn run on more platforms?
Immuta runs on Web, API. scikit-learn runs on API, Linux, macOS, Windows.
Can I use scikit-learn for free?
Yes. scikit-learn has a free tier, so you can try it without paying. Immuta starts at On request.
What is Immuta best used for?
Immuta is most often used for a bank whose snowflake estate has grown to tens of thousands of roles that no one can review before an audit, a healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recorded, a multinational applying different residency and access rules per jurisdiction to the same tables without duplicating datasets, an organisation running both snowflake and databricks that wants one policy set rather than two divergent implementations. Of those, a bank whose snowflake estate has grown to tens of thousands of roles that no one can review before an audit and a healthcare analytics team that must let researchers query patient data with identifiers masked unless a specific purpose is recorded are not what scikit-learn is typically brought in for.
What can Immuta do that scikit-learn cannot?
Immuta covers Attribute-based policy, Native enforcement, Dynamic masking, Row-level filtering. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.

Answered from the vendors’ own pages

Immuta: Does Immuta sit in the query path?

No. It compiles policies into the data platform's own native controls, so queries run at normal speed through your existing tools.

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
Immuta: What does it cost?

Not published. Market data suggests roughly 100,000 to 200,000 US dollars a year for mid-market deployments and considerably more at enterprise scale.

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
Immuta: Is Immuta still independent?

Yes. It remains independently owned, unlike several competitors in data access governance that have been acquired.

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
Immuta: Does it work across more than one warehouse?

Yes, one policy set can target Snowflake, Databricks, BigQuery and Starburst, though enforcement capability varies by platform.

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
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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