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AI · head to head

Amazon Q Developer vs Amazon Redshift ML

Amazon Q Developer logo

Amazon Q Developer

AI

Generative AI assistant for coding, testing, and AWS development

From
Free
Free plan
Yes
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
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: Amazon Q Developer deepest value and AWS-specific expertise is tied to the AWS ecosystem, less compelling for non-AWS shops; 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 Q Developer covers Code generation, 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 Q Developer and Amazon Redshift ML actually diverge.

Attributes where Amazon Q Developer and Amazon Redshift ML differ
AttributeAmazon Q DeveloperAmazon Redshift ML
Pricing modelfreemiumusage-based
Platformsweb, windows, mac, linux, apiWeb
CategoryAIMachine Learning
FoundedUnknown2006

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 Amazon Q Developer

  • Code generation
  • Agentic task automation
  • Security scanning
  • Application transformation
  • AWS expertise
  • Private code customization
  • Admin governance dashboard
  • IP indemnity protection

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 Q Developer

  • AI-assisted code completion in an IDEnot Amazon Redshift ML
  • Automating Java and .NET application upgradesnot Amazon Redshift ML
  • Scanning codebases for security vulnerabilitiesnot Amazon Redshift ML
  • Getting AWS architecture guidance while codingnot Amazon Redshift ML
  • Automating feature implementation via agentsnot 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 Q Developer
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Amazon Q Developer
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Amazon Q Developer
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Amazon Q Developer

Where each one falls short

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

Amazon Q Developer

  • Deepest value and AWS-specific expertise is tied to the AWS ecosystem, less compelling for non-AWS shops
  • Free tier caps agentic requests at 50 per month, which can be exhausted quickly on larger tasks
  • Additional transformation usage beyond plan limits is billed per line of code, adding unpredictable costs
  • GitHub and Eclipse integrations are still in preview, indicating incomplete platform coverage

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 Q Developer

Free
  • FreeFree
    • 50 agentic requests per month
    • Access to latest Claude models
    • IDE and CLI use
  • Pro$19/month
    • Expanded agentic request limits
    • 4,000 lines of code per month per user for transformation, pooled at account level
    • Admin dashboard with user and policy management

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 Q Developer if

  • You need code generation.
  • You want to start without paying.
  • You work on web, windows, mac, linux, api.
  • You also want agentic task automation.
Amazon Q Developer 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 Q Developer or Amazon Redshift ML better?
Neither clearly leads. Amazon Q Developer 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 Q Developer or Amazon Redshift ML?
Amazon Q Developer starts at Free and Amazon Redshift ML at Free.
Does Amazon Q Developer or Amazon Redshift ML run on more platforms?
Amazon Q Developer runs on web, windows, mac, linux, api. Amazon Redshift ML runs on Web.
Can I use Amazon Q Developer for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Q Developer best used for?
Amazon Q Developer is most often used for ai-assisted code completion in an ide, automating java and .net application upgrades, scanning codebases for security vulnerabilities, getting aws architecture guidance while coding. Of those, ai-assisted code completion in an ide and automating java and .net application upgrades are not what Amazon Redshift ML is typically brought in for.
What can Amazon Q Developer do that Amazon Redshift ML cannot?
Amazon Q Developer covers Code generation, Agentic task automation, Security scanning, Application transformation. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Amazon Q Developer: What does Amazon Q Developer cost?

There is a free tier with no cost, and a Pro tier priced at $19 per user per month that adds expanded agentic request limits, admin controls, and IP indemnity.

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 Q Developer: What are the limits on the free plan?

The free tier includes 50 agentic requests per month, access to the latest Claude models in IDE and CLI, and 1,000 lines of code per month for Java transformation.

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 Q Developer: How is usage metered beyond plan limits?

Java and .NET transformation usage beyond the plan's included lines of code is billed as overage at $0.003 per line of code submitted.

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 Q Developer: Can I cancel my Amazon Q Developer Pro subscription anytime?

Subscriptions are billed monthly per user and prorated in the first month, but canceling before the end of a billing month still incurs the full month's subscription fee.

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 Q Developer: What counts as an agentic request?

Agentic requests include both question-and-answer chat interactions and autonomous coding tasks performed by Q Developer's agents, all drawn from the same monthly quota.

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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