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Machine Learning · head to head

Python vs SAS

Python logo

Python

Machine Learning

The language nearly all machine learning code is written in

From
Free
Rated
-
Full reviewWebsite
SAS logo

SAS

Machine Learning

Analytics, AI and data management software

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.; SAS sAS publishes no rate, no minimum and no named cost driver; the how to buy page offers only a customized price quote based on your requirements and deployment preferences
  • They diverge on capability: Python covers C extension interface, SAS covers Statistical analysis.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Python and SAS actually diverge.

Attributes where Python and SAS differ
AttributePythonSAS
Pricing modelopen-sourcesubscription
PlatformsWindows, macOS, Linux, Android, iOSLinux, Windows, Web
Founded19911976

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).

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 SAS

  • Statistical analysis
  • Machine learning
  • Forecasting
  • Text analytics
  • Optimization
  • Python
  • R
  • Hadoop

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 SAS
  • Data preparation and analysis with pandas, Polars or PySpark before anything is modellednot SAS
  • Gluing systems together, where the job is calling several services and libraries rather than computing anything heavynot SAS
  • Research code that has to be readable by people whose speciality is statistics or a scientific domain rather than software engineeringnot SAS

SAS

  • Regulated statistical analysis and clinical reportingnot Python
  • Enterprise data management, visualization and decisioning on one licensed platformnot 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.

SAS

  • SAS publishes no rate, no minimum and no named cost driver; the how to buy page offers only a customized price quote based on your requirements and deployment preferences
  • Most new and existing customers are routed through authorized resellers rather than buying direct
  • Cloud marketplace purchases require choosing between pay as you go and bring your own licence, each with different licensing terms

Pricing, plan by plan

Python

Free

No published plan breakdown. See the Python review.

SAS

Free
  • SAS OnDemand for AcademicsFree
    • Academic use
    • Core SAS
  • SAS ViyaFree
    • Full platform
    • Cloud-native
    • AI/ML

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 SAS if

  • You need statistical analysis.
  • You want to start without paying.
  • You work on Linux, Windows, Web.
  • You also want machine learning.
SAS review

Questions people ask

Is Python or SAS better?
Neither clearly leads. Python starts at Free and SAS at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Python or SAS?
Python starts at Free and SAS at Free.
Does Python or SAS run on more platforms?
Python runs on Windows, macOS, Linux, Android, iOS. SAS runs on Linux, Windows, Web.
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 SAS is typically brought in for.
What can Python do that SAS cannot?
Python covers C extension interface, Dynamic typing, Rich standard library, Interactive interpreter and notebooks. SAS covers Statistical analysis, Machine learning, Forecasting, Text analytics.

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.

SAS: Does SAS offer a free trial?

Yes, SAS offers a free trial through a private trial environment for SAS Viya. Interested customers can request access by submitting a trial form on their website.

Source
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.

SAS: How does SAS price its software?

SAS does not publish standard pricing on its website. Instead, it uses a custom enterprise sales model where customers can choose between paying as-you-go or purchasing SAS Viya Enterprise. Specific pricing must be requested directly from their sales team.

Source
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.

SAS: What are my pricing options?

SAS offers flexible purchasing models including pay-as-you-go and enterprise licensing options. The company states they can help you find the environment that fits your needs, but specific terms must be discussed with sales.

Source
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.

SAS: How do I get a pricing quote?

You can request pricing through their website by using the quote request form, requesting a customized demo, or contacting their sales team directly.

Source
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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