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IBM SPSS vs Keras

IBM SPSS logo

IBM SPSS

Software

Statistical analysis software for data science

From
Free
Rated
-
Full reviewWebsite
Keras logo

Keras

Software

Deep learning API for humans

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: IBM SPSS add-on packages are priced separately from the base subscription, and the promotional 45% discount on them excludes renewals; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
  • They diverge on capability: IBM SPSS covers Statistical analysis, Keras covers Sequential and Functional API.

Where they differ

Only the attributes on which IBM SPSS and Keras actually diverge.

Attributes where IBM SPSS and Keras differ
AttributeIBM SPSSKeras
Pricing modelsubscriptionopen-source
PlatformsLinux, Mac, WindowsPython, Google Colab, Jupyter
Founded19112015

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

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 IBM SPSS

  • Statistical analysis
  • Predictive modeling
  • Data visualization
  • Survey analysis
  • Decision trees
  • Python
  • R
  • Excel

Only in Keras

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

Both cover

  • Linux support
  • Mac support
  • Windows support

What people use each for

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

IBM SPSS

  • Statistical testing and regression analysis for academic and market researchnot Keras
  • Predictive modelling and forecasting without writing codenot Keras

Keras

  • Machine learningnot IBM SPSS
  • Data analysisnot IBM SPSS
  • Model trainingnot IBM SPSS
  • Predictive analyticsnot IBM SPSS

Where each one falls short

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

IBM SPSS

  • Add-on packages are priced separately from the base subscription, and the promotional 45% discount on them excludes renewals
  • Subscription cost renews at the then current price at the end of the first year, so the advertised rate applies to the first term only
  • Prices shown are described by IBM as indicative, vary by country and exclude applicable taxes and duties
  • Extended access periods of 12 months or more are handled as tailored pricing rather than a published rate
  • Advanced statistics, custom tables, decision trees and forecasting are separate add-ons rather than part of the base product

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

Pricing, plan by plan

IBM SPSS

Free
  • TrialFree
    • 14-day trial
    • Full features
  • Base$99/month
    • Core statistics
    • Data management

Keras

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

Which should you pick?

Choose IBM SPSS if

  • You need statistical analysis.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want predictive modeling.
IBM SPSS review

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

Questions people ask

Is IBM SPSS or Keras better?
Neither clearly leads. IBM SPSS starts at Free and Keras at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, IBM SPSS or Keras?
IBM SPSS starts at Free and Keras at Free.
Does IBM SPSS or Keras run on more platforms?
IBM SPSS runs on Linux, Mac, Windows. Keras runs on Python, Google Colab, Jupyter.
Can I use IBM SPSS for free?
Both have a free tier, so you can try either at no cost before committing.
What is IBM SPSS best used for?
IBM SPSS is most often used for statistical testing and regression analysis for academic and market research, predictive modelling and forecasting without writing code. Of those, statistical testing and regression analysis for academic and market research and predictive modelling and forecasting without writing code are not what Keras is typically brought in for.
What can IBM SPSS do that Keras cannot?
IBM SPSS covers Statistical analysis, Predictive modeling, Data visualization, Survey analysis. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Both handle Linux support, Mac 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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