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Postgres vs Amazon Redshift ML

Postgres logo

Postgres

Technology

The world's most advanced open source 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: Postgres each major version is supported for only 5 years after its initial release, after which it is end-of-life; 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: Postgres 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 Postgres and Amazon Redshift ML actually diverge.

Attributes where Postgres and Amazon Redshift ML differ
AttributePostgresAmazon Redshift ML
Pricing modelopen-sourceusage-based
PlatformsLinux, Windows, Macos, DockerWeb
CategoryTechnologyMachine 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 Postgres

  • ACID compliance
  • Complex queries
  • Foreign keys
  • Triggers
  • Views
  • Stored procedures
  • JSON/JSONB support
  • Full-text search

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.

Postgres

  • Running a general purpose relational database for applicationsnot Amazon Redshift ML
  • Self-hosting an open source SQL database with no licence feenot Amazon Redshift ML
  • Workloads needing extensions, JSON and full text search in one enginenot 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 Postgres
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Postgres
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Postgres
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Postgres

Where each one falls short

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

Postgres

  • Each major version is supported for only 5 years after its initial release, after which it is end-of-life
  • Major version upgrades break on-disk compatibility and require a full dump and reload or the pg_upgrade tool
  • New major versions ship about once a year, so staying supported means a disruptive upgrade cycle
  • Minor releases contain only frequently-encountered bug fixes, low-risk fixes, security issues and data corruption fixes, so feature gaps are not addressed within a major version
  • There is no vendor SLA; commercial support must be bought separately from third party professional services listed by the project

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

Postgres

Free
  • Community EditionFree
    • Full database features
    • No limitations
    • Community 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 Postgres if

  • You need acid compliance.
  • You want to start without paying.
  • You work on Linux, Windows, Macos, Docker.
  • You also want complex queries.
Postgres 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 Postgres or Amazon Redshift ML better?
Neither clearly leads. Postgres 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, Postgres or Amazon Redshift ML?
Postgres starts at Free and Amazon Redshift ML at Free.
Does Postgres or Amazon Redshift ML run on more platforms?
Postgres runs on Linux, Windows, Macos, Docker. Amazon Redshift ML runs on Web.
Can I use Postgres for free?
Both have a free tier, so you can try either at no cost before committing.
What is Postgres best used for?
Postgres is most often used for running a general purpose relational database for applications, self-hosting an open source sql database with no licence fee, workloads needing extensions, json and full text search in one engine. Of those, running a general purpose relational database for applications and self-hosting an open source sql database with no licence fee are not what Amazon Redshift ML is typically brought in for.
What can Postgres do that Amazon Redshift ML cannot?
Postgres covers ACID compliance, Complex queries, Foreign keys, Triggers. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Postgres: How much does PostgreSQL cost to use?

PostgreSQL is completely free to download, install, and use. No licensing fees, subscription costs, or per-seat charges apply. The database is open source under the PostgreSQL License. Source: https://www.postgresql.org

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.

Postgres: Are there commercial PostgreSQL support options available?

Official PostgreSQL (the project) is free. Commercial PostgreSQL services such as hosting, professional support, training, and managed database services are offered by third-party vendors, not the PostgreSQL project itself. Source: https://www.postgresql.org

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.

Postgres: Do I need a license to use PostgreSQL commercially?

No. PostgreSQL is open source under the PostgreSQL License, which permits free commercial use without royalties, licensing fees, or support obligations. Source: https://www.postgresql.org

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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