Machine Learning · head to head
BigQuery ML vs Dataiku
Dataiku
Machine Learning
Browser-based platform where visual data preparation and written code share one pipeline
- 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; Dataiku visual recipes are stored as Dataiku's own configuration and do not export as runnable SQL or Python, so a Flow with hundreds of visual steps has to be rebuilt from scratch if the organisation ever leaves, and that cost rises with every project added.
- They diverge on capability: BigQuery ML covers SQL-based ML, Dataiku covers Visual Flow.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and Dataiku actually diverge.
| Attribute | BigQuery ML | Dataiku |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows, Web |
| Founded | 2008 | 2013 |
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 Dataiku
- Visual Flow
- Visual recipes
- Code recipes and notebooks
- Computation pushdown
- Automated machine learning
- Scenarios
- Node topology
- Governance features
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 Dataiku
- Linear and logistic regression on warehouse datanot Dataiku
- K-means clustering and matrix factorisation for recommendationsnot Dataiku
- Time series forecasting with ARIMA_PLUSnot Dataiku
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Dataiku
Dataiku
- Organisations where analysts and data scientists must collaborate on the same pipeline rather than exchanging extractsnot BigQuery ML
- Regulated model risk environments needing documented lineage, sign-off and a record of how a production model was producednot BigQuery ML
- Pushing heavy transformations down into a cloud warehouse while keeping the pipeline definition in one reviewable placenot BigQuery ML
- Large enterprises replacing a sprawl of spreadsheets and unmanaged scripts with something a governance function will acceptnot 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
Dataiku
- Visual recipes are stored as Dataiku's own configuration and do not export as runnable SQL or Python, so a Flow with hundreds of visual steps has to be rebuilt from scratch if the organisation ever leaves, and that cost rises with every project added.
- Production requires separate automation and API nodes, each installed and licensed, so the figure quoted for building models is not the figure for running them.
- Licensing is per user across tiers, and the lower tiers are constrained enough that occasional contributors frequently end up needing a full seat, which makes a wide rollout cost more than the initial estimate suggested.
- A self-hosted installation needs a dedicated administrator for upgrades, connection management, permissions and node topology, so the licence is a fraction of the real cost of ownership.
- Computation pushes down to the warehouse or Spark cluster where it is billed by that provider, so a platform sold on making analysts self-sufficient can generate a large warehouse bill that nobody attributes back to it.
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Dataiku
Free- Free EditionFree
- Single user
- Core features
- EnterpriseFree
- Full platform
- Collaboration
- MLOps
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 Dataiku if
- You need visual flow.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want visual recipes.
Questions people ask
- Is BigQuery ML or Dataiku better?
- Neither clearly leads. BigQuery ML starts at Free and Dataiku at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Dataiku?
- BigQuery ML starts at Free and Dataiku at Free.
- Does BigQuery ML or Dataiku run on more platforms?
- BigQuery ML runs on Web. Dataiku 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 Dataiku is typically brought in for.
- What can BigQuery ML do that Dataiku cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Dataiku covers Visual Flow, Visual recipes, Code recipes and notebooks, Computation pushdown.
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.
SourceDataiku: Is there a free version?
There is a free edition with limits on users and features, adequate for evaluation and personal work. Anything a team runs in production is a negotiated commercial agreement.
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.
SourceDataiku: Do I have to write code to use it?
No. That is the premise. An analyst can build a complete pipeline through visual recipes, and a data scientist can write Python next to it in the same Flow.
Dataiku: Where does the computation actually run?
Wherever you connect it. Transformations are pushed down into the warehouse, database or Spark cluster where the data lives, which is efficient and also means the compute cost appears on that provider's bill rather than Dataiku's.
Dataiku: Can I export my work if we leave?
Code recipes are your code and leave with you. Visual recipes do not export as equivalent code, so the visual portion of a Flow has to be reimplemented, and that portion tends to be the majority in the projects where the platform succeeded best.
Dataiku: Self-hosted or cloud?
Both are offered. Self-hosting gives control over data residency and networking and requires an administrator; the managed cloud removes that work and moves the constraint to what the vendor's environment supports.
Related pages
More on BigQuery ML
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- Dataiku vs Amazon Redshift ML
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- Dataiku vs RapidMiner
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- Dataiku vs JMP
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