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Machine Learning · head to head

IBM SPSS vs Amazon Redshift ML

IBM SPSS logo

IBM SPSS

Machine Learning

Statistical analysis software for data science

From
Free
Rated
-
Full reviewWebsite
Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning

SQL statements in Redshift that train models on SageMaker and return them as functions

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; Amazon Redshift ML training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • They diverge on capability: IBM SPSS covers Statistical analysis, Amazon Redshift ML covers CREATE MODEL in SQL.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which IBM SPSS and Amazon Redshift ML actually diverge.

Attributes where IBM SPSS and Amazon Redshift ML differ
AttributeIBM SPSSAmazon Redshift ML
Pricing modelsubscriptionusage-based
PlatformsLinux, Mac, WindowsWeb
Founded19112006

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 IBM SPSS

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

Only in Amazon Redshift ML

  • CREATE MODEL in SQL
  • Automatic model selection
  • Local inference
  • Bring your own model
  • Algorithm selection
  • Cost ceiling controls
  • Existing warehouse security
  • Batch and interactive scoring

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 Amazon Redshift ML
  • Predictive modelling and forecasting without writing codenot Amazon Redshift ML

Amazon Redshift ML

  • Adding a churn or propensity score to an existing dashboard where the data is already in Redshift and nobody needs a bespoke modelnot IBM SPSS
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot IBM SPSS
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot IBM SPSS
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot 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

Amazon Redshift ML

  • Training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
  • Autopilot searches many candidate models by default and the duration and cost of CREATE MODEL scale with the data size and the MAX_CELLS setting, so an unconstrained statement against a large table is an expensive accident rather than an experiment.
  • Local inference runs on the Redshift cluster itself, so scoring millions of rows competes for the resources the warehouse exists to provide, and the remote inference alternative adds a per-batch network call plus an hourly SageMaker endpoint charge that persists whether or not anyone queries it.
  • The supported problem types are limited to what the exposed algorithms cover, so anything involving text, images, sequences, a custom loss function or a bespoke evaluation metric is out of scope and has to be built conventionally.
  • There is no retraining schedule, drift detection or model registry, so a model created by a statement stays exactly as trained until somebody remembers to recreate it, and nothing in the warehouse will report that its accuracy has decayed.

Pricing, plan by plan

IBM SPSS

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

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

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 Amazon Redshift ML if

  • You need create model in sql.
  • You want to start without paying.
  • You also want automatic model selection.
Amazon Redshift ML review

Questions people ask

Is IBM SPSS or Amazon Redshift ML better?
Neither clearly leads. IBM SPSS starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, IBM SPSS or Amazon Redshift ML?
IBM SPSS starts at Free and Amazon Redshift ML at Free.
Does IBM SPSS or Amazon Redshift ML run on more platforms?
IBM SPSS runs on Linux, Mac, Windows. Amazon Redshift ML runs on Web.
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 Amazon Redshift ML is typically brought in for.
What can IBM SPSS do that Amazon Redshift ML cannot?
IBM SPSS covers Statistical analysis, Predictive modeling, Data visualization, Survey analysis. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Amazon Redshift ML: Does it require SageMaker?

Yes. Redshift ML is an interface; the training happens in SageMaker and needs an IAM role and an S3 bucket for the intermediate data.

Amazon Redshift ML: Is there an extra charge?

The SQL interface is part of Redshift, but the training runs as a SageMaker job charged at SageMaker rates, and a remote inference endpoint is billed for as long as it exists.

Amazon Redshift ML: What kinds of model can it build?

Regression, binary and multiclass classification through the automatic path, plus direct use of XGBoost, linear learner, multilayer perceptron and K-means. Anything beyond structured tabular prediction is out of scope.

Amazon Redshift ML: Can I use a model I trained myself?

Yes, through the bring-your-own-model path, either compiled into the cluster for local inference or called as a remote SageMaker endpoint.

Amazon Redshift ML: Does it retrain automatically?

No. Retraining means running CREATE MODEL again, on a schedule you build yourself, and nothing in the product monitors whether it is needed.

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