Software · head to head
BigQuery ML vs Comet ML
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- 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; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: BigQuery ML covers SQL-based ML, Comet ML covers Experiment tracking.
Where they differ
Only the attributes on which BigQuery ML and Comet ML actually diverge.
| Attribute | BigQuery ML | Comet ML |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Web, Linux, Mac, Windows |
| Founded | 2008 | 2017 |
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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- Keras
- scikit-learn
Both cover
- TensorFlow
- Web support
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 Comet ML
- Linear and logistic regression on warehouse datanot Comet ML
- K-means clustering and matrix factorisation for recommendationsnot Comet ML
- Time series forecasting with ARIMA_PLUSnot Comet ML
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot BigQuery ML
- Monitoring and evaluating LLM applications with tracingnot 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
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
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 Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Questions people ask
- Is BigQuery ML or Comet ML better?
- Neither clearly leads. BigQuery ML starts at Free and Comet ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Comet ML?
- BigQuery ML starts at Free and Comet ML at Free.
- Does BigQuery ML or Comet ML run on more platforms?
- BigQuery ML runs on Web. Comet ML runs on Web, Linux, Mac, Windows.
- 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 Comet ML is typically brought in for.
- What can BigQuery ML do that Comet ML cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Both handle TensorFlow, Web support.
Related pages
More on BigQuery ML
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