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Machine Learning & Data Science · head to head

Keras vs SAS

Keras logo

Keras

Machine Learning & Data Science

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite
SAS logo

SAS

Machine Learning & Data Science

Analytics, AI and data management software

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs; 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: Keras covers Sequential and Functional API, SAS covers Statistical analysis.

Where they differ

Only the attributes on which Keras and SAS actually diverge.

Attributes where Keras and SAS differ
AttributeKerasSAS
Pricing modelopen-sourcesubscription
PlatformsPython, Google Colab, JupyterLinux, Windows, Web
Founded20151976

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

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 Keras

  • Sequential and Functional API
  • Pre-built neural network layers
  • Model training and evaluation
  • Transfer learning
  • Model serialization
  • TensorFlow
  • JAX
  • PyTorch

Only in SAS

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

Both cover

  • Linux support
  • Windows support

What people use each for

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

Keras

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

SAS

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

Where each one falls short

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

Keras

  • Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • Error messages can be vague and unhelpful, making debugging challenging
  • Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch

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

Keras

Free
  • Open SourceFree
    • High-level API
    • Pre-built layers
    • Model serialization

SAS

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

Which should you pick?

Choose Keras if

  • You need sequential and functional api.
  • You want to start without paying.
  • You work on Python, Google Colab, Jupyter.
  • You also want pre-built neural network layers.
Keras 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 Keras or SAS better?
Neither clearly leads. Keras 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, Keras or SAS?
Keras starts at Free and SAS at Free.
Does Keras or SAS run on more platforms?
Keras runs on Python, Google Colab, Jupyter. SAS runs on Linux, Windows, Web.
Can I use Keras for free?
Both have a free tier, so you can try either at no cost before committing.
What is Keras best used for?
Keras is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what SAS is typically brought in for.
What can Keras do that SAS cannot?
Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. SAS covers Statistical analysis, Machine learning, Forecasting, Text analytics. Both handle Linux support, Windows support.

Answered from the vendors’ own pages

Keras: What is Keras?

Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.

Source
Keras: What model architectures does Keras support?

Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.

Source
Keras: Can Keras models run on TPUs and GPUs?

Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.

Source
Keras: Does Keras offer pre-trained models?

Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.

Source
Keras: Who should use Keras?

Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.

Source
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