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Machine Learning · head to head

Amazon Redshift ML vs SAS

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
SAS logo

SAS

Machine Learning

Analytics, AI and data management software

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: 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.; 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: Amazon Redshift ML covers CREATE MODEL in SQL, SAS covers Statistical analysis.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Amazon Redshift ML and SAS differ
AttributeAmazon Redshift MLSAS
Pricing modelusage-basedsubscription
PlatformsWebLinux, Windows, Web
Founded20061976

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 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

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.

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 SAS
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot SAS
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot SAS
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot SAS

SAS

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

Where each one falls short

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

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.

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

Amazon Redshift ML

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

SAS

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

Which should you pick?

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

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 Amazon Redshift ML or SAS better?
Neither clearly leads. Amazon Redshift ML 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, Amazon Redshift ML or SAS?
Amazon Redshift ML starts at Free and SAS at Free.
Does Amazon Redshift ML or SAS run on more platforms?
Amazon Redshift ML runs on Web. SAS runs on Linux, Windows, Web.
Can I use Amazon Redshift ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Redshift ML best used for?
Amazon Redshift ML is most often used for adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model, letting an analytics team test whether a predictive column has any business value before asking for data science headcount, scoring rows inside a sql pipeline where moving data out to a separate service would add fragility for little benefit, organisations committed to aws whose main constraint is a data science backlog rather than modelling sophistication. Of those, adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model and letting an analytics team test whether a predictive column has any business value before asking for data science headcount are not what SAS is typically brought in for.
What can Amazon Redshift ML do that SAS cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. SAS covers Statistical analysis, Machine learning, Forecasting, Text analytics.

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.

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
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.

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
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.

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
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.

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
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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