Software · head to head
BigQuery ML vs KNIME
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- They diverge on capability: BigQuery ML covers SQL-based ML, KNIME covers Visual workflows.
Where they differ
Only the attributes on which BigQuery ML and KNIME actually diverge.
| Attribute | BigQuery ML | KNIME |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2008 | 2004 |
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 KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- Python
- R
- Spark
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 KNIME
- Linear and logistic regression on warehouse datanot KNIME
- K-means clustering and matrix factorisation for recommendationsnot KNIME
- Time series forecasting with ARIMA_PLUSnot KNIME
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot KNIME
KNIME
- Building data pipelines and analytics workflows visually rather than in codenot BigQuery ML
- Connecting and blending data across many sources for analysisnot 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
KNIME
- The free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub
- The free AI assistant is limited to 20 interactions a month
- Paid workflow runtime is metered in credits, with 120 included on Pro and overage at $0.025 per vCore minute
- The Team plan at $99 a month includes 3 members, with additional seats at $49 a month each
- Business Hub pricing is on request, and its tiers are capped at 4, 8 and 16 vCores with 5, 5 and 20 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
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
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 KNIME if
- You need visual workflows.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data preprocessing.
Questions people ask
- Is BigQuery ML or KNIME better?
- Neither clearly leads. BigQuery ML starts at Free and KNIME at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or KNIME?
- BigQuery ML starts at Free and KNIME at Free.
- Does BigQuery ML or KNIME run on more platforms?
- BigQuery ML runs on Web. KNIME runs on 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 KNIME is typically brought in for.
- What can BigQuery ML do that KNIME cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. Both handle TensorFlow.
Related pages
More on BigQuery ML
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