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Machine Learning & Data Science · head to head

Hugging Face vs BigQuery ML

Hugging Face logo

Hugging Face

Machine Learning & Data Science

The AI community building the future

From
Free
Rated
-
Full reviewWebsite
BigQuery ML logo

BigQuery ML

Machine Learning & Data Science

Machine learning in BigQuery using SQL

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; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
  • They diverge on capability: Hugging Face covers Model hub, BigQuery ML covers SQL-based ML.

Where they differ

Only the attributes on which Hugging Face and BigQuery ML actually diverge.

Attributes where Hugging Face and BigQuery ML differ
AttributeHugging FaceBigQuery ML
Pricing modelUnknownusage-based
PlatformsWeb, APIWeb
Founded20162008

Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).

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
  • Api support

Only in BigQuery ML

  • SQL-based ML
  • AutoML Tables
  • Model export
  • Prediction functions
  • Feature preprocessing
  • BigQuery
  • Vertex AI
  • TensorFlow

Both cover

  • Web support

What people use each for

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

Hugging Face

  • ai tools managementnot BigQuery ML
  • Workflow automationnot BigQuery ML
  • Reportingnot BigQuery ML

BigQuery ML

  • Training models in SQL without exporting datanot Hugging Face
  • Linear and logistic regression on warehouse datanot Hugging Face
  • K-means clustering and matrix factorisation for recommendationsnot Hugging Face
  • Time series forecasting with ARIMA_PLUSnot Hugging Face
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot 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

BigQuery ML

  • Not available in BigQuery's Standard edition, so the cheapest tier cannot use it
  • Billed through BigQuery compute and storage rather than as its own product, so training cost tracks data scanned
  • Remote models incur extra Agent Platform charges on top
  • Externally trained model types such as boosted trees and AutoML run through Agent Platform rather than inside BigQuery

Pricing, plan by plan

Hugging Face

Free

No published plan breakdown. See the Hugging Face review.

BigQuery ML

Free
  • Free TierFree
    • 10GB storage
    • 1TB queries
  • On-Demand$5/TB
    • Pay per TB scanned
    • ML training costs

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 BigQuery ML if

  • You need sql-based ml.
  • You want to start without paying.
  • You also want automl tables.
BigQuery ML review

Questions people ask

Is Hugging Face or BigQuery ML better?
Neither clearly leads. Hugging Face starts at Free and BigQuery ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Hugging Face or BigQuery ML?
Hugging Face starts at Free and BigQuery ML at Free.
Does Hugging Face or BigQuery ML run on more platforms?
Hugging Face runs on Web, API. BigQuery 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 BigQuery ML is typically brought in for.
What can Hugging Face do that BigQuery ML cannot?
Hugging Face covers Model hub, Datasets, Spaces, Transformers library. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle Web support.

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
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
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
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
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
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