Machine Learning & Data Science · head to head
BigQuery ML vs TensorBoard
BigQuery ML
Machine Learning & Data Science
Machine learning in BigQuery using SQL
- From
- Free
- Rated
- -
T
TensorBoard
Machine Learning & Data Science
TensorFlow's visualization toolkit
- From
- Free
- Rated
- -
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; TensorBoard built and documented as a TensorFlow companion tool; the project's own site presents it as inspecting TensorFlow runs and graphs specifically, per tensorflow.org/tensorboard.
Where they differ
Only the attributes on which BigQuery ML and TensorBoard actually diverge.
| Attribute | BigQuery ML | TensorBoard |
|---|---|---|
| Pricing model | usage-based | open-source |
| Founded | 2008 | Unknown |
Identical on both: starting price (Free), free tier (Yes), platforms (Web), 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 BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
Only in TensorBoard
Nothing recorded that BigQuery ML does not also cover.
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 TensorBoard
- Linear and logistic regression on warehouse datanot TensorBoard
- K-means clustering and matrix factorisation for recommendationsnot TensorBoard
- Time series forecasting with ARIMA_PLUSnot TensorBoard
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot TensorBoard
TensorBoard
No use cases recorded yet. See the TensorBoard review.
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
TensorBoard
- Built and documented as a TensorFlow companion tool; the project's own site presents it as inspecting TensorFlow runs and graphs specifically, per tensorflow.org/tensorboard.
- Source is Apache-2.0 licensed on GitHub (github.com/tensorflow/tensorboard), so there is no vendor-hosted paid tier or support contract distinct from the open source project.
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
TensorBoard
FreeNo published plan breakdown. See the TensorBoard review.
Which should you pick?
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is BigQuery ML or TensorBoard better?
- Neither clearly leads. BigQuery ML starts at Free and TensorBoard at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or TensorBoard?
- BigQuery ML starts at Free and TensorBoard at Free.
- Does BigQuery ML or TensorBoard run on more platforms?
- Both run on Web, so platform support will not decide this one for you.
- 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 TensorBoard is typically brought in for.
- What can BigQuery ML do that TensorBoard cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions.
Related pages
More on BigQuery ML
More on TensorBoard
Other head to heads
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- BigQuery ML vs Google Vertex AI
- BigQuery ML vs Azure Machine Learning
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- BigQuery ML vs Dataiku
- TensorBoard vs AWS SageMaker
- TensorBoard vs Google Vertex AI
- TensorBoard vs Azure Machine Learning
- TensorBoard vs DataRobot
- TensorBoard vs Snowflake
- TensorBoard vs TensorFlow
- TensorBoard vs Comet ML
- TensorBoard vs Keras
- TensorBoard vs MLflow
- TensorBoard vs Jupyter
- TensorBoard vs PyTorch
- TensorBoard vs scikit-learn
- TensorBoard vs Apache Spark MLlib
- TensorBoard vs Weights & Biases
- TensorBoard vs Alteryx
- TensorBoard vs Anaconda
- TensorBoard vs Databricks
- TensorBoard vs Dataiku