Software · head to head
Comet ML vs BigQuery ML
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: Comet ML covers Experiment tracking, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which Comet ML and BigQuery ML actually diverge.
| Attribute | Comet ML | BigQuery ML |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Web, Linux, Mac, Windows | Web |
| Founded | 2017 | 2008 |
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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- Keras
- scikit-learn
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- Cloud Storage
Both cover
- TensorFlow
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot BigQuery ML
- Monitoring and evaluating LLM applications with tracingnot BigQuery ML
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
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 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.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is Comet ML or BigQuery ML better?
- Neither clearly leads. Comet ML 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, Comet ML or BigQuery ML?
- Comet ML starts at Free and BigQuery ML at Free.
- Does Comet ML or BigQuery ML run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. BigQuery ML runs on Web.
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for tracking machine learning experiments, metrics and model versions, monitoring and evaluating llm applications with tracing. Of those, tracking machine learning experiments, metrics and model versions and monitoring and evaluating llm applications with tracing are not what BigQuery ML is typically brought in for.
- What can Comet ML do that BigQuery ML cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle TensorFlow, Web support.
Related pages
More on BigQuery ML
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