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Databases · head to head

DataGrip vs Amazon Redshift ML

DataGrip logo

DataGrip

Databases

Cross-platform database IDE from JetBrains for SQL and NoSQL databases

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: DataGrip commercial use requires a paid subscription; the free tier is non-commercial only.; 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: DataGrip covers Intelligent SQL Completion, 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 DataGrip and Amazon Redshift ML actually diverge.

Attributes where DataGrip and Amazon Redshift ML differ
AttributeDataGripAmazon Redshift ML
Pricing modelsubscriptionusage-based
Platformswindows, mac, linuxWeb
CategoryDatabasesMachine Learning
Founded20002006

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 DataGrip

  • Intelligent SQL Completion
  • Schema Navigation
  • Data Editor
  • Version Control for Scripts
  • Multi-database Support

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.

DataGrip

  • Writing and running SQL queries across multiple database enginesnot Amazon Redshift ML
  • Browsing and editing schema and table data visuallynot Amazon Redshift ML
  • Version-controlling database migration scriptsnot Amazon Redshift ML
  • Standardizing database tooling across a JetBrains-based teamnot 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 DataGrip
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot DataGrip
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot DataGrip
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot DataGrip

Where each one falls short

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

DataGrip

  • Commercial use requires a paid subscription; the free tier is non-commercial only.
  • No built-in database administration features like backup scheduling found in dedicated DBA tools.
  • Heavier resource footprint than lightweight single-purpose SQL clients.
  • NoSQL support (e.g. MongoDB) is less mature than its relational database tooling.

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

DataGrip

Free
  • Free (non-commercial)Free
    • Personal, non-commercial use only
  • Individual - Year 1$99/year
    • Full DataGrip license
    • Free updates during subscription
  • Individual - Year 2+$79/year
    • Continuity discount from second year onward

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 DataGrip if

  • You need intelligent sql completion.
  • You want to start without paying.
  • You work on windows, mac, linux.
  • You also want schema navigation.
DataGrip 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 DataGrip or Amazon Redshift ML better?
Neither clearly leads. DataGrip 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, DataGrip or Amazon Redshift ML?
DataGrip starts at Free and Amazon Redshift ML at Free.
Does DataGrip or Amazon Redshift ML run on more platforms?
DataGrip runs on windows, mac, linux. Amazon Redshift ML runs on Web.
Can I use DataGrip for free?
Both have a free tier, so you can try either at no cost before committing.
What is DataGrip best used for?
DataGrip is most often used for writing and running sql queries across multiple database engines, browsing and editing schema and table data visually, version-controlling database migration scripts, standardizing database tooling across a jetbrains-based team. Of those, writing and running sql queries across multiple database engines and browsing and editing schema and table data visually are not what Amazon Redshift ML is typically brought in for.
What can DataGrip do that Amazon Redshift ML cannot?
DataGrip covers Intelligent SQL Completion, Schema Navigation, Data Editor, Version Control for Scripts. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

DataGrip: Is DataGrip free for personal use?

JetBrains introduced a free non-commercial license for DataGrip in October 2025, allowing personal use, while commercial use still requires a paid annual or monthly subscription.

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.

DataGrip: Who qualifies for the free non-commercial license?

Only individuals are eligible, for uses like learning, open-source work, or content creation. Anyone paid by an employer, including at a non-profit, must use a commercial license instead.

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.

DataGrip: How long does the free non-commercial license last?

It lasts one year and auto-renews if DataGrip was used at least once in the final six months; otherwise you can simply reapply for a new license.

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.

DataGrip: Does the free license have fewer features than the paid version?

No, it is a full-featured IDE identical to the paid version, though it requires anonymized telemetry sharing that cannot be opted out of under the non-commercial agreement.

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.

DataGrip: Can I use DataGrip offline with the free license?

No, activation requires logging into a JetBrains Account; offline activation codes are not available for the free non-commercial license.

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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