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Databases · head to head

Amazon RDS vs Amazon Redshift ML

Amazon RDS logo

Amazon RDS

Databases

Set up, operate, and scale a relational database in the cloud

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: Amazon RDS no super-user access or direct host connectivity limits advanced customization; 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: Amazon RDS covers Multiple DB Engines, 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 Amazon RDS and Amazon Redshift ML actually diverge.

Attributes where Amazon RDS and Amazon Redshift ML differ
AttributeAmazon RDSAmazon Redshift ML
PlatformsAWS Cloud, Multi-AZ, Multi-regionWeb
CategoryDatabasesMachine Learning

Identical on both: starting price (Free), pricing model (usage-based), free tier (Yes), user rating (Not yet rated), founded (2006).

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 RDS

  • Multiple DB Engines
  • Automated Backups
  • Multi-AZ Deployment
  • Read Replicas
  • Encryption
  • Performance Insights
  • Automatic Scaling
  • MySQL

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.

Amazon RDS

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

Where each one falls short

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

Amazon RDS

  • No super-user access or direct host connectivity limits advanced customization
  • Pricing unpredictable and expensive compared to GCP alternatives with equivalent features
  • Limited access to system procedures and tables requiring advanced permissions
  • No Oracle RAC (Real Application Clusters) support for high-availability Oracle 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.
  • 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

Amazon RDS

Free
  • On-Demand Instances$undefined/per second
  • Reserved Instances$undefined/mo
  • Database Savings Plans$undefined/mo

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 Amazon RDS if

  • You need multiple db engines.
  • You want to start without paying.
  • You work on AWS Cloud, Multi-AZ, Multi-region.
  • You also want automated backups.
Amazon RDS 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 Amazon RDS or Amazon Redshift ML better?
Neither clearly leads. Amazon RDS 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, Amazon RDS or Amazon Redshift ML?
Amazon RDS starts at Free and Amazon Redshift ML at Free.
Does Amazon RDS or Amazon Redshift ML run on more platforms?
Amazon RDS runs on AWS Cloud, Multi-AZ, Multi-region. Amazon Redshift ML runs on Web.
Can I use Amazon RDS for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon RDS best used for?
Amazon RDS 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 Amazon RDS do that Amazon Redshift ML cannot?
Amazon RDS covers Multiple DB Engines, Automated Backups, Multi-AZ Deployment, Read Replicas. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Amazon RDS: What is included in the AWS Free Tier for RDS?

For signups before July 15, 2025: 750 hours per month of single-AZ database instance usage (12 months), 20 GB General Purpose SSD storage monthly, 20 GB automated backup storage monthly, available engines include MySQL, MariaDB, PostgreSQL, SQL Server Express Edition. For signups after July 15, 2025: choice between Free Plan or Paid Plan, $100 in credits plus up to $100 additional credits for activating foundational services, credits valid 12 months. Free Tier unavailable in AWS GovCloud (US) and China (Beijing) regions.

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.

Amazon RDS: How is data transfer priced in RDS?

Same Availability Zone (EC2 to RDS) is free. Multi-AZ replication is free. Cross-AZ within same region is 0.01 USD per GB in and out. Cross-region snapshots and backups follow standard data transfer charges.

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.

Amazon RDS: What database engines are supported by RDS?

Aurora, MySQL, PostgreSQL, MariaDB, Oracle, SQL Server, and IBM Db2. Pricing varies by engine.

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 RDS: What cost components are included in RDS monthly pricing?

DB instance hours (billed in 1-second increments, 10-minute minimum), storage per GB per month, I/O requests (Aurora and magnetic storage only), provisioned IOPS per month, backup storage, and data transfer fees.

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.

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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