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AI · head to head

Galileo vs Amazon Redshift ML

Galileo logo

Galileo

AI

Evaluation and observability platform for GenAI applications and agents

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: Galileo the free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.; 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: Galileo covers Pre-built evaluations, 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 Galileo and Amazon Redshift ML actually diverge.

Attributes where Galileo and Amazon Redshift ML differ
AttributeGalileoAmazon Redshift ML
Pricing modelfreemiumusage-based
Platformsweb, 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 Galileo

  • Pre-built evaluations
  • Ground truth capture
  • Luna models
  • Agent behavior analysis
  • Production guardrails
  • Flexible deployment

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.

Galileo

  • Evaluating RAG and agent applications before production releasenot Amazon Redshift ML
  • Monitoring live GenAI applications for failures and driftnot Amazon Redshift ML
  • Applying real-time guardrails without custom integration worknot Amazon Redshift ML
  • Reducing evaluation costs using distilled Luna judge modelsnot 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 Galileo
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Galileo
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Galileo
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Galileo

Where each one falls short

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

Galileo

  • The free plan is limited to 5,000 traces per month, which is quickly outgrown by production workloads.
  • Real-time guardrails and unlimited trace capacity are reserved for the custom-priced Enterprise tier.
  • Pro plan pricing scales with trace volume, so costs can grow unpredictably as usage increases.
  • On-premises deployment requires an Enterprise contract rather than being available self-serve.

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

Galileo

Free
  • FreeFree
    • 5,000 traces/month
    • Unlimited users
    • Unlimited custom evaluations
  • Pro$100/month
    • 50,000 traces/month
    • Standard role-based access control
    • Advanced analytics and insights
  • Enterprise$undefined/mo
    • Unlimited trace capacity
    • Custom rate limits
    • Hosted, VPC, or on-prem deployment

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 Galileo if

  • You need pre-built evaluations.
  • You want to start without paying.
  • You work on web, api.
  • You also want ground truth capture.
Galileo 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 Galileo or Amazon Redshift ML better?
Neither clearly leads. Galileo 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, Galileo or Amazon Redshift ML?
Galileo starts at Free and Amazon Redshift ML at Free.
Does Galileo or Amazon Redshift ML run on more platforms?
Galileo runs on web, api. Amazon Redshift ML runs on Web.
Can I use Galileo for free?
Both have a free tier, so you can try either at no cost before committing.
What is Galileo best used for?
Galileo is most often used for evaluating rag and agent applications before production release, monitoring live genai applications for failures and drift, applying real-time guardrails without custom integration work, reducing evaluation costs using distilled luna judge models. Of those, evaluating rag and agent applications before production release and monitoring live genai applications for failures and drift are not what Amazon Redshift ML is typically brought in for.
What can Galileo do that Amazon Redshift ML cannot?
Galileo covers Pre-built evaluations, Ground truth capture, Luna models, Agent behavior analysis. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Galileo: What does Galileo cost?

Galileo offers a free plan, a Pro plan at $100/month billed yearly (with a 33% annual discount), and a custom-priced Enterprise plan for unlimited trace capacity.

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.

Galileo: Is there a free plan, and what are its limits?

The Free plan includes 5,000 traces per month with unlimited users and unlimited custom evaluations, aimed at developers and small teams experimenting with GenAI.

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.

Galileo: How is usage metered?

Galileo's pricing scales based on the number of traces processed each month, with Free capped at 5,000, Pro at 50,000, and Enterprise offering unlimited trace capacity.

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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