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AI · head to head

Modal vs Amazon Redshift ML

Modal logo

Modal

AI

Cloud functions for AI and ML

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: Modal the Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute; 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: Modal covers Serverless GPUs, 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 Modal and Amazon Redshift ML actually diverge.

Attributes where Modal and Amazon Redshift ML differ
AttributeModalAmazon Redshift ML
PlatformsCloud, ApiWeb
CategoryAIMachine Learning
Founded20212006

Identical on both: starting price (Free), pricing model (usage-based), 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 Modal

  • Serverless GPUs
  • Python functions
  • Auto-scaling
  • Fast cold starts
  • Python SDK
  • GitHub Actions
  • Cloud storage
  • 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.

Modal

  • Running serverless GPU workloads for model inference and trainingnot Amazon Redshift ML
  • Executing Python functions on cloud compute without managing serversnot 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 Modal
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Modal
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Modal
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Modal

Where each one falls short

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

Modal

  • The Team plan carries a $250 monthly base fee and returns only $100 of that as free credits, so $150 is a flat charge before any compute
  • Compute is billed per second across separate GPU and CPU meters, so total cost depends on execution time rather than any fixed rate
  • The Starter plan's $30 monthly free credit is the only allowance below the paid base fee
  • Enterprise volume discounts are custom and unpublished

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

Modal

Free
  • StarterFree
    • 3 seats
    • 100 containers
    • 10 GPU concurrency
  • Team$250/month
    • Unlimited seats
    • 5,000 containers
    • 50 GPU concurrency
  • Enterprise$null/custom
    • Custom seats, containers, and GPU concurrency

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 Modal if

  • You need serverless gpus.
  • You want to start without paying.
  • You work on Cloud, Api.
  • You also want python functions.
Modal 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 Modal or Amazon Redshift ML better?
Neither clearly leads. Modal 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, Modal or Amazon Redshift ML?
Modal starts at Free and Amazon Redshift ML at Free.
Does Modal or Amazon Redshift ML run on more platforms?
Modal runs on Cloud, Api. Amazon Redshift ML runs on Web.
Can I use Modal for free?
Both have a free tier, so you can try either at no cost before committing.
What is Modal best used for?
Modal is most often used for running serverless gpu workloads for model inference and training, executing python functions on cloud compute without managing servers. Of those, running serverless gpu workloads for model inference and training and executing python functions on cloud compute without managing servers are not what Amazon Redshift ML is typically brought in for.
What can Modal do that Amazon Redshift ML cannot?
Modal covers Serverless GPUs, Python functions, Auto-scaling, Fast cold starts. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Modal: How much does Modal cost?

Modal uses pay-as-you-go pricing with Team plan at 250 USD/month base. Starter includes 30 USD/month free credits; Team includes 100 USD/month free credits. Compute charges per second for CPU cores, memory, and GPU instances.

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.

Modal: Is there a free tier?

Yes, Starter plan is free plus 30 USD/month in compute credits included monthly for new users.

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.

Modal: What are the seat limits?

Starter plan includes 3 seats; Team plan provides unlimited seats; Enterprise tier has custom seat allocations.

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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