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Databases · head to head

Google Cloud SQL vs Amazon Redshift ML

Google Cloud SQL logo

Google Cloud SQL

Databases

Fully managed relational database service for MySQL, PostgreSQL, and SQL Server

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: Google Cloud SQL locked into Google Cloud ecosystem with limited cross-cloud portability; 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: Google Cloud SQL covers High Availability, 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 Google Cloud SQL and Amazon Redshift ML actually diverge.

Attributes where Google Cloud SQL and Amazon Redshift ML differ
AttributeGoogle Cloud SQLAmazon Redshift ML
PlatformsGoogle Cloud PlatformWeb
CategoryDatabasesMachine Learning
Founded20082006

Identical on both: starting price (Free), pricing model (usage-based), 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 Google Cloud SQL

  • High Availability
  • Automated Backups
  • Point-in-time Recovery
  • Encryption
  • Regional/Zonal Instances
  • Read Replicas
  • Private IP
  • BigQuery

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.

Google Cloud SQL

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

Where each one falls short

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

Google Cloud SQL

  • Locked into Google Cloud ecosystem with limited cross-cloud portability
  • Pay-as-you-go pricing can become expensive with unpredictable workloads
  • Limited customization options compared to self-managed databases

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

Google Cloud SQL

Free
  • Free TierFree
    • db-f1-micro instance
    • 30GB storage
    • Limited usage
  • Standard$25/month
    • High availability
    • Automated backups
    • Point-in-time recovery

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 Google Cloud SQL if

  • You need high availability.
  • You want to start without paying.
  • You work on Google Cloud Platform.
  • You also want automated backups.
Google Cloud SQL 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 Google Cloud SQL or Amazon Redshift ML better?
Neither clearly leads. Google Cloud SQL 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, Google Cloud SQL or Amazon Redshift ML?
Google Cloud SQL starts at Free and Amazon Redshift ML at Free.
Does Google Cloud SQL or Amazon Redshift ML run on more platforms?
Google Cloud SQL runs on Google Cloud Platform. Amazon Redshift ML runs on Web.
Can I use Google Cloud SQL for free?
Both have a free tier, so you can try either at no cost before committing.
What is Google Cloud SQL best used for?
Google Cloud SQL 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 Google Cloud SQL do that Amazon Redshift ML cannot?
Google Cloud SQL covers High Availability, Automated Backups, Point-in-time Recovery, Encryption. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Google Cloud SQL: What database engines does Google Cloud SQL support?

Google Cloud SQL supports MySQL, PostgreSQL, and SQL Server. Users can choose their preferred engine when provisioning an instance and Google handles automated backups, replication, patching, and scaling.

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.

Google Cloud SQL: Does Google Cloud SQL have a free tier?

Google Cloud SQL does not have a free tier, though new users receive free trial credits from Google Cloud Platform. Pricing is based on compute resources (CPU and memory) and storage used, with options for committed use discounts.

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.

Google Cloud SQL: Can Google Cloud SQL scale automatically?

Yes. Cloud SQL automatically scales database storage and compute resources to handle increased workloads without manual intervention, and includes automated backups and high availability configurations.

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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