Software · head to head
BigQuery ML vs Keras
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Keras limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
- They diverge on capability: BigQuery ML covers SQL-based ML, Keras covers Sequential and Functional API.
Where they differ
Only the attributes on which BigQuery ML and Keras actually diverge.
| Attribute | BigQuery ML | Keras |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Python, Google Colab, Jupyter |
| Founded | 2008 | 2015 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Unknown).
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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- Cloud Storage
Only in Keras
- Sequential and Functional API
- Pre-built neural network layers
- Model training and evaluation
- Transfer learning
- Model serialization
- JAX
- PyTorch
- Linux support
Both cover
- TensorFlow
What people use each for
The jobs each tool is most often brought in to do.
BigQuery ML
- Training models in SQL without exporting datanot Keras
- Linear and logistic regression on warehouse datanot Keras
- K-means clustering and matrix factorisation for recommendationsnot Keras
- Time series forecasting with ARIMA_PLUSnot Keras
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Keras
Keras
- Machine learningnot BigQuery ML
- Data analysisnot BigQuery ML
- Model trainingnot BigQuery ML
- Predictive analyticsnot BigQuery ML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
Keras
- Limited customization compared to TensorFlow; advanced users may find constraints in complex model designs
- Error messages can be vague and unhelpful, making debugging challenging
- Smaller ecosystem and fewer pre-trained models than TensorFlow or PyTorch
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Keras
Free- Open SourceFree
- High-level API
- Pre-built layers
- Model serialization
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Choose Keras if
- You need sequential and functional api.
- You want to start without paying.
- You work on Python, Google Colab, Jupyter.
- You also want pre-built neural network layers.
Questions people ask
- Is BigQuery ML or Keras better?
- Neither clearly leads. BigQuery ML starts at Free and Keras at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Keras?
- BigQuery ML starts at Free and Keras at Free.
- Does BigQuery ML or Keras run on more platforms?
- BigQuery ML runs on Web. Keras runs on Python, Google Colab, Jupyter.
- Can I use BigQuery ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BigQuery ML best used for?
- BigQuery ML is most often used for training models in sql without exporting data, linear and logistic regression on warehouse data, k-means clustering and matrix factorisation for recommendations, time series forecasting with arima_plus. Of those, training models in sql without exporting data and linear and logistic regression on warehouse data are not what Keras is typically brought in for.
- What can BigQuery ML do that Keras cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Keras covers Sequential and Functional API, Pre-built neural network layers, Model training and evaluation, Transfer learning. Both handle TensorFlow.
Answered from the vendors’ own pages
Keras: What is Keras?
Keras is a high-level deep learning API built on top of TensorFlow that simplifies building and training neural networks. Keras 3 supports multiple backends including TensorFlow, PyTorch, and JAX, making it backend-agnostic.
SourceKeras: What model architectures does Keras support?
Keras supports the Sequential model for linear stacks of layers, the Functional API for arbitrary graph architectures, and model subclassing for custom implementations. All approaches provide access to layers, optimizers, metrics, and callbacks.
SourceKeras: Can Keras models run on TPUs and GPUs?
Yes, Keras models can run on TPU Pods or large GPU clusters, be exported to run in browsers or on mobile devices, and be served via web APIs.
SourceKeras: Does Keras offer pre-trained models?
Yes, Keras provides pre-trained models through KerasHub and Keras Applications for common deep learning tasks like image classification, object detection, and NLP.
SourceKeras: Who should use Keras?
Keras is ideal for beginners and rapid prototyping due to its simplicity and user-friendly interface. Advanced users and production deployments may benefit more from lower-level frameworks like TensorFlow or PyTorch for greater customization.
SourceRelated pages
More on BigQuery ML
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