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PostgreSQL vs Amazon Redshift ML

PostgreSQL logo

PostgreSQL

Databases

The world's most advanced open source relational database

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: PostgreSQL requires manual scaling across multiple machines for very large deployments; 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: PostgreSQL covers ACID Compliance, 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 PostgreSQL and Amazon Redshift ML actually diverge.

Attributes where PostgreSQL and Amazon Redshift ML differ
AttributePostgreSQLAmazon Redshift ML
Pricing modelUnknownusage-based
PlatformsLinux, Windows, macOS, BSD, UnixWeb
CategoryDatabasesMachine Learning
Founded19962006

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

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 PostgreSQL

  • ACID Compliance
  • JSON/JSONB Support
  • Full-text Search
  • Extensibility
  • Advanced Indexing
  • Partitioning
  • Replication
  • pgAdmin

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.

PostgreSQL

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

Where each one falls short

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

PostgreSQL

  • Requires manual scaling across multiple machines for very large deployments
  • Performance tuning requires deep knowledge of database internals
  • No built-in graphical admin interface; command-line tools are primary method

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

PostgreSQL

Free

No published plan breakdown. See the PostgreSQL review.

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 PostgreSQL if

  • You need acid compliance.
  • You want to start without paying.
  • You work on Linux, Windows, macOS, BSD, Unix.
  • You also want json/jsonb support.
PostgreSQL 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 PostgreSQL or Amazon Redshift ML better?
Neither clearly leads. PostgreSQL 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, PostgreSQL or Amazon Redshift ML?
PostgreSQL starts at Free and Amazon Redshift ML at Free.
Does PostgreSQL or Amazon Redshift ML run on more platforms?
PostgreSQL runs on Linux, Windows, macOS, BSD, Unix. Amazon Redshift ML runs on Web.
Can I use PostgreSQL for free?
Both have a free tier, so you can try either at no cost before committing.
What is PostgreSQL best used for?
PostgreSQL is most often used for transaction processing, data storage, application backend, reporting. Of those, transaction processing and data storage are not what Amazon Redshift ML is typically brought in for.
What can PostgreSQL do that Amazon Redshift ML cannot?
PostgreSQL covers ACID Compliance, JSON/JSONB Support, Full-text Search, Extensibility. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

PostgreSQL: Is PostgreSQL completely free?

Yes. PostgreSQL is completely free and open source with no licensing fees or restrictions on use.

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.

PostgreSQL: What platforms does PostgreSQL run on?

PostgreSQL runs on all major operating systems including Linux, Windows, macOS, BSD, and commercial Unix variants, and has been proven highly scalable managing terabytes to petabytes of data.

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.

PostgreSQL: What procedural languages are supported?

PostgreSQL supports stored functions and procedures in multiple languages including PL/pgSQL, Perl, Python, Tcl, Java, JavaScript, R, and Rust.

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.

PostgreSQL: What is ACID compliance in PostgreSQL?

PostgreSQL has been ACID-compliant since 2001, ensuring data integrity through atomicity, consistency, isolation, and durability guarantees for all transactions.

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.

PostgreSQL: Does PostgreSQL support JSON data?

Yes. PostgreSQL supports JSON and JSONB data types for storing and querying JSON documents, along with XML and other document formats.

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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