Software · head to head
BigQuery ML vs Kubeflow
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- They diverge on capability: BigQuery ML covers SQL-based ML, Kubeflow covers ML pipelines.
Where they differ
Only the attributes on which BigQuery ML and Kubeflow actually diverge.
| Attribute | BigQuery ML | Kubeflow |
|---|---|---|
| Pricing model | usage-based | Unknown |
| Platforms | Web | Kubernetes |
| Founded | 2008 | 2017 |
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 Kubeflow
- ML pipelines
- Training operators
- Model serving
- Jupyter notebooks
- Hyperparameter tuning
- Kubernetes
- PyTorch
- XGBoost
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 Kubeflow
- Linear and logistic regression on warehouse datanot Kubeflow
- K-means clustering and matrix factorisation for recommendationsnot Kubeflow
- Time series forecasting with ARIMA_PLUSnot Kubeflow
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Kubeflow
Kubeflow
- Machine learningnot BigQuery ML
- Data analysisnot BigQuery ML
- Model trainingnot BigQuery ML
- Predictive analyticsnot 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
Kubeflow
- Complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- Resource-intensive infrastructure with minimal installs consuming significant CPU and memory
- Limited multi-tenancy support and multi-cloud setup leaves users largely on their own
- No native CI/CD integration, requiring custom glue code for versioning and automated deployments
- Debugging jobs and monitoring workloads often requires dropping down into raw Kubernetes commands
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
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 Kubeflow if
- You need ml pipelines.
- You want to start without paying.
- You work on Kubernetes.
- You also want training operators.
Questions people ask
- Is BigQuery ML or Kubeflow better?
- Neither clearly leads. BigQuery ML starts at Free and Kubeflow at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Kubeflow?
- BigQuery ML starts at Free and Kubeflow at Free.
- Does BigQuery ML or Kubeflow run on more platforms?
- BigQuery ML runs on Web. Kubeflow runs on Kubernetes.
- 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 Kubeflow is typically brought in for.
- What can BigQuery ML do that Kubeflow cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Both handle TensorFlow.
Answered from the vendors’ own pages
Kubeflow: Is Kubeflow free to use?
Yes, Kubeflow is free and open-source under Apache License 2.0. However, you pay for the underlying Kubernetes infrastructure, which typically costs $500 to $5,000 per month depending on scale and cloud provider.
SourceKubeflow: Do I need Kubernetes expertise to use Kubeflow?
Kubeflow requires significant Kubernetes and DevOps expertise. The installation deploys dozens of services and CRDs, often requiring manual configuration and troubleshooting. Data scientists typically need to convert scripts to containerized components.
SourceKubeflow: What platforms can Kubeflow run on?
Kubeflow runs on any Kubernetes-compliant cluster, including on-premise, AWS, Azure, Google Cloud, and hybrid environments. This multi-cloud portability is one of its key advantages over managed alternatives.
SourceKubeflow: How does Kubeflow compare to managed services like SageMaker?
Kubeflow offers multi-cloud portability and lower long-term costs but requires more operational overhead. SageMaker provides a fully managed experience with better UI and less infrastructure work, but creates vendor lock-in to AWS.
Source