Software · head to head
KNIME vs BigQuery ML
The short version
- Each has a real cost: KNIME the free Analytics Platform runs locally only, so anything shared or scheduled requires a paid Hub; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: KNIME covers Visual workflows, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which KNIME and BigQuery ML actually diverge.
| Attribute | KNIME | BigQuery ML |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Linux, Mac, Windows | Web |
| Founded | 2004 | 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 KNIME
- Visual workflows
- Data preprocessing
- Machine learning
- Visualization
- Reporting
- Python
- R
- Spark
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- Cloud Storage
Both cover
- TensorFlow
What people use each for
The jobs each tool is most often brought in to do.
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
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
KNIME
Free- Analytics PlatformFree
- Visual workflows
- All nodes
- Community extensions
- ServerFree
- Team collaboration
- Workflow automation
- REST API
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 KNIME if
- You need visual workflows.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want data preprocessing.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is KNIME or BigQuery ML better?
- Neither clearly leads. KNIME 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, KNIME or BigQuery ML?
- KNIME starts at Free and BigQuery ML at Free.
- Does KNIME or BigQuery ML run on more platforms?
- KNIME runs on Linux, Mac, Windows. BigQuery ML runs on Web.
- Can I use KNIME for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is KNIME best used for?
- KNIME is most often used for building data pipelines and analytics workflows visually rather than in code, connecting and blending data across many sources for analysis. Of those, building data pipelines and analytics workflows visually rather than in code and connecting and blending data across many sources for analysis are not what BigQuery ML is typically brought in for.
- What can KNIME do that BigQuery ML cannot?
- KNIME covers Visual workflows, Data preprocessing, Machine learning, Visualization. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle TensorFlow.