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Machine Learning · head to head

Amazon Redshift ML vs Replicate

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
Replicate logo

Replicate

AI

Run AI models in the cloud

From
Free
Free plan
Yes
Full reviewWebsite

The short version

  • Each has a real cost: 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.; Replicate private model deployments are billed for all the time instances are online, including setup and idle time, not only for processing
  • They diverge on capability: Amazon Redshift ML covers CREATE MODEL in SQL, Replicate covers Model hosting.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where Amazon Redshift ML and Replicate differ
AttributeAmazon Redshift MLReplicate
PlatformsWebApi, Cloud
CategoryMachine LearningAI
Founded20062019

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 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

Only in Replicate

  • Model hosting
  • Simple API
  • Auto-scaling
  • Custom models
  • REST API
  • Python client
  • JavaScript client
  • Api support

What people use each for

The jobs each tool is most often brought in to do.

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 Replicate
  • Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Replicate
  • Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Replicate
  • Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Replicate

Replicate

  • Running open source machine learning models through a hosted API without managing GPUsnot Amazon Redshift ML
  • Deploying and serving a custom or fine tuned model on rented GPU hardwarenot Amazon Redshift ML
  • Per second billed batch image, video and language model inferencenot Amazon Redshift ML

Where each one falls short

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

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.

Replicate

  • Private model deployments are billed for all the time instances are online, including setup and idle time, not only for processing
  • Multi-GPU A100, H100, H200 and L40S capacity beyond the listed configurations is only available with a committed spend contract
  • The pricing page publishes no free tier allowance

Pricing, plan by plan

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Replicate

Free
  • Pay-as-you-go$null/usage
    • Billed by execution time for public models
    • CPU Small: $0.000025/second ($0.09/hour)
    • 8x Nvidia A100 GPUs: $0.0112/second ($40.32/hour)
  • Enterprise$null/custom
    • Dedicated account manager
    • Priority support
    • Higher GPU limits

Which should you pick?

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

Choose Replicate if

  • You need model hosting.
  • You want to start without paying.
  • You work on Api, Cloud.
  • You also want simple api.
Replicate review

Questions people ask

Is Amazon Redshift ML or Replicate better?
Neither clearly leads. Amazon Redshift ML starts at Free and Replicate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Amazon Redshift ML or Replicate?
Amazon Redshift ML starts at Free and Replicate at Free.
Does Amazon Redshift ML or Replicate run on more platforms?
Amazon Redshift ML runs on Web. Replicate runs on Api, Cloud.
Can I use Amazon Redshift ML for free?
Both have a free tier, so you can try either at no cost before committing.
What is Amazon Redshift ML best used for?
Amazon Redshift ML is most often used for adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model, letting an analytics team test whether a predictive column has any business value before asking for data science headcount, scoring rows inside a sql pipeline where moving data out to a separate service would add fragility for little benefit, organisations committed to aws whose main constraint is a data science backlog rather than modelling sophistication. Of those, adding a churn or propensity score to an existing dashboard where the data is already in redshift and nobody needs a bespoke model and letting an analytics team test whether a predictive column has any business value before asking for data science headcount are not what Replicate is typically brought in for.
What can Amazon Redshift ML do that Replicate cannot?
Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model. Replicate covers Model hosting, Simple API, Auto-scaling, Custom models.

Answered from the vendors’ own pages

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.

Replicate: How much does Replicate cost?

Replicate uses pay-as-you-go pricing based on model execution time and compute type. Costs range from $0.09/hour for CPU (Small) to $40.32/hour for 8x Nvidia A100 GPUs. Some models charge per input/output tokens instead of time.

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.

Replicate: Does Replicate offer a free tier?

Yes, Replicate is free to start with pay-as-you-go pricing. There are no subscription tiers or minimum commitments; you pay only for what you use.

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.

Replicate: What is the difference between public and private models?

Public models are billed by execution time. Private models are billed for all instance uptime including setup, idle, and active processing time, except for fast-booting fine-tunes which are billed only during active processing.

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 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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