Softwr

Machine Learning · head to head

Groq vs Amazon Redshift ML

Groq logo

Groq

Machine Learning

Fast inference provider using proprietary LPU hardware for low-latency serving

From
On request
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

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: Groq pricing is not published and is sold entirely by quote, making cost comparison difficult; 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.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Groq and Amazon Redshift ML actually diverge.

Attributes where Groq and Amazon Redshift ML differ
AttributeGroqAmazon Redshift ML
Starting priceOn requestFree
Pricing modelquoteusage-based
Free tierNoYes
PlatformsAPI, CloudWeb
FoundedUnknown2006

Identical on both: user rating (Not yet rated), category (Machine Learning).

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 Groq

Nothing recorded that Amazon Redshift ML does not also cover.

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.

Groq

  • Latency-sensitive applications requiring sub-second inference response timesnot Amazon Redshift ML
  • High-volume inference workloads where cost per inference matters at scalenot Amazon Redshift ML
  • Custom model deployment with performance guaranteesnot Amazon Redshift ML
  • Enterprise applications seeking inference-specific infrastructurenot 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 Groq
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Groq
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Groq
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Groq

Where each one falls short

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

Groq

  • Pricing is not published and is sold entirely by quote, making cost comparison difficult
  • Limited to open-weight models; no proprietary model access through the platform
  • Not widely integrated into third-party AI platforms compared to OpenAI or Anthropic

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

Groq

On request

No published plan breakdown. See the Groq review.

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 Groq if

  • You work on API, Cloud.
Groq 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 Groq or Amazon Redshift ML better?
Neither clearly leads. Groq starts at On request and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Groq or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at On request for Groq and Free for Amazon Redshift ML.
Does Groq or Amazon Redshift ML run on more platforms?
Groq runs on API, Cloud. Amazon Redshift ML runs on Web.
Can I use Amazon Redshift ML for free?
Yes. Amazon Redshift ML has a free tier, so you can try it without paying. Groq starts at On request.
What is Groq best used for?
Groq is most often used for latency-sensitive applications requiring sub-second inference response times, high-volume inference workloads where cost per inference matters at scale, custom model deployment with performance guarantees, enterprise applications seeking inference-specific infrastructure. Of those, latency-sensitive applications requiring sub-second inference response times and high-volume inference workloads where cost per inference matters at scale are not what Amazon Redshift ML is typically brought in for.
What can Groq do that Amazon Redshift ML cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Groq: Is Groq free or paid?

Pricing details are not published on the main website. To explore Groq's service and pricing, visit their console at console.groq.com/home.

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.

Groq: Does Groq offer a free tier or free credits?

Free tier availability is not documented on the public site. Check the Groq console for current free tier or trial options.

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 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.

Share

Related pages

More on Groq

More on Amazon Redshift ML

Keep looking

Other head to heads