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Machine Learning · head to head

Palantir Foundry vs Amazon Redshift ML

Palantir Foundry logo

Palantir Foundry

Machine Learning

Operating system for modern enterprise

From
On request
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

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: Palantir Foundry custom pricing model with no public information makes budgeting difficult; 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: Palantir Foundry covers Data integration, 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 Palantir Foundry and Amazon Redshift ML actually diverge.

Attributes where Palantir Foundry and Amazon Redshift ML differ
AttributePalantir FoundryAmazon Redshift ML
Starting priceOn requestFree
Pricing modelsubscriptionusage-based
Free tierNoYes
Founded20032006

Identical on both: platforms (Web), 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 Palantir Foundry

  • Data integration
  • Ontology modeling
  • Pipeline builder
  • Operational analytics
  • Governance
  • Enterprise systems
  • Cloud platforms
  • IoT

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.

Palantir Foundry

  • Machine learningnot Amazon Redshift ML
  • Data analysisnot Amazon Redshift ML
  • Model trainingnot Amazon Redshift ML
  • Predictive analyticsnot 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 Palantir Foundry
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Palantir Foundry
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Palantir Foundry
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Palantir Foundry

Where each one falls short

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

Palantir Foundry

  • Custom pricing model with no public information makes budgeting difficult
  • Steep implementation and configuration requirements
  • Requires significant technical expertise to operate effectively
  • Long sales cycle typical for enterprise software

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

Palantir Foundry

On request
  • EnterpriseFree
    • Full platform
    • Custom deployment
    • Enterprise support

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 Palantir Foundry if

  • You need data integration.
  • You also want ontology modeling.
Palantir Foundry 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 Palantir Foundry or Amazon Redshift ML better?
Neither clearly leads. Palantir Foundry starts at On request and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Palantir Foundry or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at On request for Palantir Foundry and Free for Amazon Redshift ML.
Does Palantir Foundry or Amazon Redshift ML run on more platforms?
Both run on Web, so platform support will not decide this one for you.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Palantir Foundry starts at On request.
What is Palantir Foundry best used for?
Palantir Foundry is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Amazon Redshift ML is typically brought in for.
What can Palantir Foundry do that Amazon Redshift ML cannot?
Palantir Foundry covers Data integration, Ontology modeling, Pipeline builder, Operational analytics. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Palantir Foundry: What is Palantir Foundry designed for?

Palantir Foundry is an enterprise data integration and analytics platform supporting end-to-end data pipelines, covering ingestion, processing, pipeline building, monitoring, and creating analytics dashboards with both code and no-code tools.

Source
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.

Palantir Foundry: How much does Palantir Foundry cost?

Palantir Foundry uses custom pricing. No public list pricing is available. Enterprise customers and government agencies must contact Palantir directly for formal quotes and licensing terms.

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.

Palantir Foundry: Who uses Palantir Foundry?

Palantir Foundry serves enterprise and government organizations needing complex data integration, analytics, and operational intelligence across large-scale data environments.

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.

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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