Softwr

Machine Learning · head to head

Hugging Face vs Amazon Redshift ML

Hugging Face logo

Hugging Face

Machine Learning

The AI community building the future

From
Free
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

  • Each has a real cost: Hugging Face model discovery across 3 million models lacks robust filtering and sorting by quality metrics; 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: Hugging Face covers Model hub, 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 Hugging Face and Amazon Redshift ML actually diverge.

Attributes where Hugging Face and Amazon Redshift ML differ
AttributeHugging FaceAmazon Redshift ML
Pricing modelUnknownusage-based
PlatformsWeb, APIWeb
Founded20162006

Identical on both: starting price (Free), free tier (Yes), 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 Hugging Face

  • Model hub
  • Datasets
  • Spaces
  • Transformers library
  • GitHub
  • Cloud providers
  • MLOps tools
  • Web 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.

Hugging Face

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

Where each one falls short

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

Hugging Face

  • Model discovery across 3 million models lacks robust filtering and sorting by quality metrics
  • Community-driven content means variable model quality and documentation
  • Private models and datasets require Pro subscription
  • Enterprise support and SLAs require custom arrangements

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

Hugging Face

Free

No published plan breakdown. See the Hugging Face 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 Hugging Face if

  • You need model hub.
  • You want to start without paying.
  • You work on Web, API.
  • You also want datasets.
Hugging Face 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 Hugging Face or Amazon Redshift ML better?
Neither clearly leads. Hugging Face 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, Hugging Face or Amazon Redshift ML?
Hugging Face starts at Free and Amazon Redshift ML at Free.
Does Hugging Face or Amazon Redshift ML run on more platforms?
Hugging Face runs on Web, API. Amazon Redshift ML runs on Web.
Can I use Hugging Face for free?
Both have a free tier, so you can try either at no cost before committing.
What is Hugging Face best used for?
Hugging Face is most often used for ai tools management, workflow automation, reporting. Of those, ai tools management and workflow automation are not what Amazon Redshift ML is typically brought in for.
What can Hugging Face do that Amazon Redshift ML cannot?
Hugging Face covers Model hub, Datasets, Spaces, Transformers library. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.

Answered from the vendors’ own pages

Hugging Face: Is Hugging Face free to use?

Yes. Hugging Face allows users to host and collaborate on unlimited public models, datasets, and applications at no cost. Models can be accessed and used freely from the Hub.

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.

Hugging Face: How many models are available on Hugging Face?

Hugging Face Hub currently hosts nearly 3 million machine learning models across various tasks including text generation, image processing, and video generation.

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.

Hugging Face: What is the Hugging Face Inference API?

Hugging Face provides access to 45,000+ models from leading AI providers through a single unified API with no service fees, simplifying access to diverse models.

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.

Hugging Face: What content types does Hugging Face support?

Hugging Face supports text, image, video, audio, and 3D content models, allowing collaboration across multiple modalities and use cases.

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.

Hugging Face: What is the transformers library?

Transformers is a Hugging Face library built for natural language processing applications, providing pre-built models and utilities for NLP tasks.

Source
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 Hugging Face

More on Amazon Redshift ML

Keep looking

Other head to heads