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Machine Learning · head to head

Python vs Quadratic

Python logo

Python

Machine Learning

The language nearly all machine learning code is written in

From
Free
Rated
-
Full reviewWebsite
Quadratic logo

Quadratic

Spreadsheets

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

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Python the global interpreter lock serialises bytecode execution within a process, so CPU-bound parallel work needs multiprocessing with its memory duplication and serialisation costs; the free-threaded build added in 3.13 is opt-in and much of the compiled ecosystem does not yet support it.; 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.
  • They diverge on capability: Python covers C extension interface, Quadratic covers Multi-language cells.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Python and Quadratic actually diverge.

Attributes where Python and Quadratic differ
AttributePythonQuadratic
Pricing modelopen-sourcePer user per month
PlatformsWindows, macOS, Linux, Android, iOSWeb, macOS, Windows, Linux
CategoryMachine LearningSpreadsheets
Founded1991Unknown

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 Python

  • C extension interface
  • Dynamic typing
  • Rich standard library
  • Interactive interpreter and notebooks
  • Package index
  • Virtual environments
  • Cross-platform
  • Free-threaded build

Only in Quadratic

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

What people use each for

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

Python

  • Training and evaluating models, where every mainstream framework offers Python as its primary interfacenot Quadratic
  • Data preparation and analysis with pandas, Polars or PySpark before anything is modellednot Quadratic
  • Gluing systems together, where the job is calling several services and libraries rather than computing anything heavynot Quadratic
  • Research code that has to be readable by people whose speciality is statistics or a scientific domain rather than software engineeringnot Quadratic

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 Python
  • Ad hoc modelling where part of the logic is genuinely code and part is a judgement call typed into a cellnot Python
  • Sharing a reproducible analysis with a colleague who will only ever open a spreadsheet, not a notebooknot Python
  • Prototyping a data pull against a warehouse before committing it to a scheduled pipelinenot Python

Where each one falls short

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

Python

  • The global interpreter lock serialises bytecode execution within a process, so CPU-bound parallel work needs multiprocessing with its memory duplication and serialisation costs; the free-threaded build added in 3.13 is opt-in and much of the compiled ecosystem does not yet support it.
  • Dependency resolution is the standing cost of the ecosystem: a project pinning a CUDA-linked framework, a NumPy major version and a dozen libraries that constrain both produces multi-gigabyte images and installs that break whenever one of those publishes a new major version.
  • Ecosystem-wide binary breaks propagate badly, because a library compiled against an older extension interface fails at import with a low-level error rather than a clear message, and a team with a frozen environment discovers it cannot add one package without rebuilding all of them.
  • Dynamic typing pushes whole categories of error to run time, which in machine learning means a shape mismatch or a None surfacing six hours into a training job rather than at a compile step, and type hints are optional, unenforced at run time and applied inconsistently across ML libraries.
  • Interpreter start-up and per-call overhead make it a poor host for low-latency serving of small models, where the wrapper can cost more time than the inference itself, which is why serving layers get rewritten in Go, Rust or C++ once traffic justifies the work.

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.

Pricing, plan by plan

Python

Free

No published plan breakdown. See the Python review.

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

Which should you pick?

Choose Python if

  • You need c extension interface.
  • You want to start without paying.
  • You work on Windows, macOS, Linux, Android, iOS.
  • You also want dynamic typing.
Python review

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

Questions people ask

Is Python or Quadratic better?
Neither clearly leads. Python starts at Free and Quadratic at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Python or Quadratic?
Python starts at Free and Quadratic at Free.
Does Python or Quadratic run on more platforms?
Python runs on Windows, macOS, Linux, Android, iOS. Quadratic runs on Web, macOS, Windows, Linux.
Can I use Python for free?
Both have a free tier, so you can try either at no cost before committing.
What is Python best used for?
Python is most often used for training and evaluating models, where every mainstream framework offers python as its primary interface, data preparation and analysis with pandas, polars or pyspark before anything is modelled, gluing systems together, where the job is calling several services and libraries rather than computing anything heavy, research code that has to be readable by people whose speciality is statistics or a scientific domain rather than software engineering. Of those, training and evaluating models, where every mainstream framework offers python as its primary interface and data preparation and analysis with pandas, polars or pyspark before anything is modelled are not what Quadratic is typically brought in for.
What can Python do that Quadratic cannot?
Python covers C extension interface, Dynamic typing, Rich standard library, Interactive interpreter and notebooks. Quadratic covers Multi-language cells, Infinite canvas, Direct database connections, AI agent in the sheet.

Answered from the vendors’ own pages

Python: Which version should I use for machine learning?

Usually one release behind the newest. Compiled ML wheels lag the interpreter by months, and being first to a new version mostly buys you a broken environment.

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.

Python: Is Python too slow for machine learning?

The numerical work is not in Python. It matters for data preprocessing loops written in pure Python and for serving small models at high request rates, and in both cases the answer is to move that specific part into a vectorised library or a compiled extension.

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.

Python: pip or conda?

pip with virtual environments, or uv, is simpler and now covers most cases. Conda still earns its place when you need non-Python system libraries, particular CUDA builds or a scientific stack pinned as a set.

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.

Python: Do I need to know C to work in machine learning?

No, but you need to know that the libraries are C underneath, because that explains why an error message is unreadable, why a wheel will not install and why one line of pandas is a thousand times faster than the loop it replaced.

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.

Python: Is the global interpreter lock being removed?

A free-threaded build exists from 3.13 onward as an opt-in variant. It is not the default, and the compiled libraries that matter for machine learning are still working through support for it.

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