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AI · head to head

CoreWeave vs Amazon Redshift ML

CoreWeave logo

CoreWeave

AI

Specialized cloud for GPU compute

From
$0.35/per-hour
Free plan
No
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

  • Only Amazon Redshift ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: CoreWeave gPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand; 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: CoreWeave covers NVIDIA H100/A100, 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 CoreWeave and Amazon Redshift ML actually diverge.

Attributes where CoreWeave and Amazon Redshift ML differ
AttributeCoreWeaveAmazon Redshift ML
Starting price$0.35/per-hourFree
Free tierNoYes
PlatformsCloudWeb
CategoryAIMachine Learning
Founded20172006

Identical on both: pricing model (usage-based), 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 CoreWeave

  • NVIDIA H100/A100
  • Kubernetes native
  • High bandwidth
  • Object storage
  • Kubernetes
  • Terraform
  • Cloud APIs
  • Cloud 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.

CoreWeave

  • Renting GPU compute for model training and inferencenot Amazon Redshift ML
  • Running large scale AI workloads without buying hardwarenot 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 CoreWeave
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot CoreWeave
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot CoreWeave
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot CoreWeave

Where each one falls short

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

CoreWeave

  • GPU nodes are sold as full 8 GPU instances rather than single cards, so the entry cost for an H100 node is $49.24 an hour on demand
  • Spot pricing is roughly 40% of on demand, at $19.71 an hour for the same H100 node, so predictable capacity carries a large premium
  • The newest hardware carries no published price and requires contacting sales
  • Discounts of up to 60% require committed usage agreements negotiated with sales
  • Only the GH200 is offered as a single GPU instance

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

CoreWeave

$0.35/per-hour
  • Standard$0.35/per-hour
    • Various GPU types
    • Kubernetes
  • EnterpriseFree
    • Dedicated clusters
    • Custom solutions

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 CoreWeave if

  • You need nvidia h100/a100.
  • You work on Cloud.
  • You also want kubernetes native.
CoreWeave 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 CoreWeave or Amazon Redshift ML better?
Neither clearly leads. CoreWeave starts at $0.35/per-hour and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, CoreWeave or Amazon Redshift ML?
Amazon Redshift ML has a free tier; the other does not. Paid plans start at $0.35/per-hour for CoreWeave and Free for Amazon Redshift ML.
Does CoreWeave or Amazon Redshift ML run on more platforms?
CoreWeave runs on 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. CoreWeave starts at $0.35/per-hour.
What is CoreWeave best used for?
CoreWeave is most often used for renting gpu compute for model training and inference, running large scale ai workloads without buying hardware. Of those, renting gpu compute for model training and inference and running large scale ai workloads without buying hardware are not what Amazon Redshift ML is typically brought in for.
What can CoreWeave do that Amazon Redshift ML cannot?
CoreWeave covers NVIDIA H100/A100, Kubernetes native, High bandwidth, Object storage. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

CoreWeave: How much does CoreWeave cost?

CoreWeave does not publish pricing on its website. The company uses a quote-based pricing model and directs customers to contact their sales team directly to discuss pricing options and customized solutions.

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.

CoreWeave: How can I get a quote from CoreWeave?

To obtain CoreWeave pricing, you must contact their sales team directly through the Contact Us option on their website. They will provide a customized quote based on your specific compute and infrastructure requirements.

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.

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