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Spreadsheet & Data · head to head

Redash vs scikit-learn

Redash logo

Redash

Spreadsheet & Data

Connect and visualize your data

From
Free
Rated
-
Full reviewWebsite
S

scikit-learn

Machine Learning & Data Science

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Redash a basic self-hosted deployment needs a minimum of 4GB of RAM, and more RAM and CPU as background workers and API processes grow; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: Redash covers SQL Query Editor, scikit-learn covers Classification algorithms.

Where they differ

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

Attributes where Redash and scikit-learn differ
AttributeRedashscikit-learn
Pricing modelfreemiumUnknown
PlatformsWeb, Self-hosted, CloudPython, Linux, macOS, Windows
CategorySpreadsheet & DataMachine Learning & Data Science
Founded20132007

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 Redash

  • SQL Query Editor
  • Multiple Data Sources
  • Visualizations
  • Dashboards
  • Alerts
  • PostgreSQL
  • MySQL
  • BigQuery

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.

Redash

  • Self-hosted SQL query editor and dashboarding over existing databasesnot scikit-learn
  • Sharing scheduled query results with a team without buying a BI licencenot scikit-learn

scikit-learn

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

Where each one falls short

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

Redash

  • A basic self-hosted deployment needs a minimum of 4GB of RAM, and more RAM and CPU as background workers and API processes grow
  • The official Docker images were not updated for V10, so the documented route is to deploy a V8 instance and then upgrade it
  • Anyone not using a provided cloud image has to configure the environment variables and secrets by hand

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

Redash

Free
  • Open SourceFree
    • Full Features
    • Self-hosted
    • Community Support
  • Cloud$49/month
    • Managed Hosting
    • Automatic Updates
    • Support

scikit-learn

Free

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

Which should you pick?

Choose Redash if

  • You need sql query editor.
  • You want to start without paying.
  • You work on Web, Self-hosted, Cloud.
  • You also want multiple data sources.
Redash review

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

Questions people ask

Is Redash or scikit-learn better?
Neither clearly leads. Redash starts at Free and scikit-learn at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Redash or scikit-learn?
Redash starts at Free and scikit-learn at Free.
Does Redash or scikit-learn run on more platforms?
Redash runs on Web, Self-hosted, Cloud. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use Redash for free?
Both have a free tier, so you can try either at no cost before committing.
What is Redash best used for?
Redash is most often used for self-hosted sql query editor and dashboarding over existing databases, sharing scheduled query results with a team without buying a bi licence. Of those, self-hosted sql query editor and dashboarding over existing databases and sharing scheduled query results with a team without buying a bi licence are not what scikit-learn is typically brought in for.
What can Redash do that scikit-learn cannot?
Redash covers SQL Query Editor, Multiple Data Sources, Visualizations, Dashboards. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.

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
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
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
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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