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Spreadsheets · head to head

Quadratic vs scikit-learn

Quadratic logo

Quadratic

Spreadsheets

Infinite-canvas spreadsheet that runs Python, SQL and formulas in the same grid

From
Free
Rated
-
Full reviewWebsite
scikit-learn logo

scikit-learn

Machine Learning

Machine learning in Python

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Quadratic aI usage is metered as a dollar allowance rather than a flat entitlement, so a team that adopts the agent enthusiastically will exhaust 20 USD per user per month quickly and the true cost per seat becomes unpredictable.; scikit-learn no GPU acceleration by default; limited optional GPU support requires external arrays
  • They diverge on capability: Quadratic covers Multi-language cells, scikit-learn covers Classification algorithms.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Quadratic and scikit-learn differ
AttributeQuadraticscikit-learn
Pricing modelPer user per monthUnknown
PlatformsWeb, macOS, Windows, LinuxPython, Linux, macOS, Windows
CategorySpreadsheetsMachine Learning
FoundedUnknown2007

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 Quadratic

  • Multi-language cells
  • Infinite canvas
  • Direct database connections
  • AI agent in the sheet
  • WebAssembly engine
  • Python package support

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.

Quadratic

  • An analyst who wants a SQL result, a Python transformation and a set of manual assumptions visible in one file instead of three toolsnot scikit-learn
  • Ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cellnot scikit-learn
  • Sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebooknot scikit-learn
  • Prototyping a data pull against a warehouse before committing it to a scheduled pipelinenot scikit-learn

scikit-learn

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

Where each one falls short

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

Quadratic

  • AI usage is metered as a dollar allowance rather than a flat entitlement, so a team that adopts the agent enthusiastically will exhaust 20 USD per user per month quickly and the true cost per seat becomes unpredictable.
  • Self-hosting is available only on the Enterprise plan, so an organisation that cannot send data to the vendor cloud has to enter a sales negotiation rather than run a container.
  • Excel and Google Sheets compatibility is partial, so files with heavy conditional formatting, pivot tables or macros do not survive a round trip and have to be rebuilt.
  • The product is young and the connector list is short, covering the main relational databases and two warehouses, so anything else has to be reached through Python code you write and maintain yourself.
  • There is no real mobile editing experience, which matters more than it sounds for a spreadsheet because approvals and quick checks routinely happen on a phone.

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

Quadratic

Free
  • PersonalFree
    • Limited AI usage
    • Limited files and connections
    • Limited sharing
  • Pro$18/month
    • Billed annually
    • 20 USD of AI credits per month
    • Unlimited files
  • Business$36/month
    • Billed annually
    • 40 USD of AI credits per month
    • Advanced permissions
  • Enterprise$undefined/year
    • Quoted
    • Self-hosting option
    • Custom AI usage

scikit-learn

Free

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

Which should you pick?

Choose Quadratic if

  • You need multi-language cells.
  • You want to start without paying.
  • You work on Web, macOS, Windows, Linux.
  • You also want infinite canvas.
Quadratic 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 Quadratic or scikit-learn better?
Neither clearly leads. Quadratic 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, Quadratic or scikit-learn?
Quadratic starts at Free and scikit-learn at Free.
Does Quadratic or scikit-learn run on more platforms?
Quadratic runs on Web, macOS, Windows, Linux. scikit-learn runs on Python, Linux, macOS, Windows.
Can I use Quadratic for free?
Both have a free tier, so you can try either at no cost before committing.
What is Quadratic best used for?
Quadratic is most often used for an analyst who wants a sql result, a python transformation and a set of manual assumptions visible in one file instead of three tools, ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cell, sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebook, prototyping a data pull against a warehouse before committing it to a scheduled pipeline. Of those, an analyst who wants a sql result, a python transformation and a set of manual assumptions visible in one file instead of three tools and ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cell are not what scikit-learn is typically brought in for.
What can Quadratic do that scikit-learn cannot?
Quadratic covers Multi-language cells, Infinite canvas, Direct database connections, AI agent in the sheet. scikit-learn covers Classification algorithms, Regression models, Clustering methods, Dimensionality reduction.

Answered from the vendors’ own pages

Quadratic: Do I need to know Python to use it?

No, formulas work on their own, but the reason to choose Quadratic over Google Sheets is the code cells, so a team with no Python will not get the value.

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
Quadratic: What happens when the AI credits run out?

Prompting stops until the next monthly reset or until you move up a plan; the spreadsheet itself keeps working normally.

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
Quadratic: Can it replace a BI tool?

Not for scheduled distribution or governed metrics. It is for exploratory work by one person or a small team, not for dashboards a hundred people read.

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
Quadratic: Is my database query sent to the vendor?

Connections run through the vendor service on all plans except Enterprise self-hosting, which is the option to take if that is unacceptable.

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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