Machine Learning · head to head
BigQuery ML vs RapidMiner
RapidMiner
Machine Learning
Visual workflow data science platform, now sold by Altair as AI Studio
- 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; RapidMiner processes are stored as the product's own XML, so they cannot be meaningfully diffed, reviewed in a pull request or executed anywhere else, and a team's accumulated work is not portable in any practical sense.
- They diverge on capability: BigQuery ML covers SQL-based ML, RapidMiner covers Visual process canvas.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and RapidMiner actually diverge.
| Attribute | BigQuery ML | RapidMiner |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows, Web |
| Founded | 2008 | 2007 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 RapidMiner
- Visual process canvas
- Operator library
- Automatic modelling
- Python and R operators
- Validation operators
- Text and time series extensions
- AI Hub server
- Altair portfolio integration
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 RapidMiner
- Linear and logistic regression on warehouse datanot RapidMiner
- K-means clustering and matrix factorisation for recommendationsnot RapidMiner
- Time series forecasting with ARIMA_PLUSnot RapidMiner
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot RapidMiner
RapidMiner
- Modelling work in an engineering organisation where the analysis must be reviewable by people who do not codenot BigQuery ML
- Teaching data science concepts, where seeing the validation split as a visible connection is more instructive than reading a function callnot BigQuery ML
- Companies already holding Altair licences, where adding this draws on units already purchased rather than a new procurementnot BigQuery ML
- Business analysts building predictive workflows without a data science team to hand the problem tonot 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
RapidMiner
- Processes are stored as the product's own XML, so they cannot be meaningfully diffed, reviewed in a pull request or executed anywhere else, and a team's accumulated work is not portable in any practical sense.
- The operator library is the ceiling, and anything beyond it means dropping into an embedded Python or R operator, at which point the code sits inside a visual container that provides none of the version control, testing or debugging a normal repository would.
- Two changes of ownership in three years, Altair in 2022 and Siemens thereafter, have already moved the product's name, packaging and licensing, so a buyer is committing to a roadmap decided inside a much larger engineering software business.
- Licensing draws on Altair's shared units pool, so running heavy modelling work consumes capacity that other teams in the organisation were relying on for different products, which makes cost attribution and capacity planning awkward.
- Scheduling and deployment require AI Hub as a separate server product to install, license and operate, so a model built on the desktop is not in production until another purchase and another installation have been completed.
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
RapidMiner
Free- FreeFree
- 10,000 data rows
- 1 logical processor
- ProfessionalFree
- Unlimited data
- Full features
- 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 RapidMiner if
- You need visual process canvas.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want operator library.
Questions people ask
- Is BigQuery ML or RapidMiner better?
- Neither clearly leads. BigQuery ML starts at Free and RapidMiner at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or RapidMiner?
- BigQuery ML starts at Free and RapidMiner at Free.
- Does BigQuery ML or RapidMiner run on more platforms?
- BigQuery ML runs on Web. RapidMiner runs on Linux, Mac, Windows, Web.
- 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 RapidMiner is typically brought in for.
- What can BigQuery ML do that RapidMiner cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. RapidMiner covers Visual process canvas, Operator library, Automatic modelling, Python and R operators.
Answered from the vendors’ own pages
BigQuery ML: How much does Google Cloud BigQuery ML cost?
BigQuery ML pricing is not specified separately on Google Cloud's pricing page. It follows the same pay-as-you-go model as BigQuery, charging per terabyte of data scanned during analysis. Customers receive $300 in free credits and can use 20+ products free up to monthly limits.
SourceRapidMiner: Is it still called RapidMiner?
The desktop product is now Altair AI Studio and the server is Altair AI Hub. The RapidMiner name persists in documentation, community material and most search results, which makes finding current information harder than it should be.
BigQuery ML: Does Google Cloud offer a free trial?
Yes, new customers get $300 in free credits and all customers can use 20+ Google Cloud products free up to their monthly usage limits.
SourceRapidMiner: Is there a free version?
Altair has offered free and academic editions with usage limits, but the terms have moved with each ownership change, so check what is currently on offer rather than relying on what the free tier allowed a few years ago.
RapidMiner: Do I need to write code?
No, which is the point of it. You will write some once you hit the edge of the operator library, and at that stage the tool works against you rather than for you.
RapidMiner: Can I put a model into production?
Through AI Hub, which is a separate licensed server. The desktop tool builds and validates; it does not schedule or serve.
RapidMiner: How does licensing work?
Through Altair's units model, where a pool of purchased units is drawn on by whichever Altair products your organisation runs, rather than a per-seat licence specific to this product.
Related pages
More on BigQuery ML
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