Machine Learning · head to head
BigQuery ML vs Seldon
Seldon
Machine Learning
Kubernetes model serving whose current version is licensed under the Business Source Licence
- 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; Seldon seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- They diverge on capability: BigQuery ML covers SQL-based ML, Seldon covers Kubernetes custom resources.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and Seldon actually diverge.
| Attribute | BigQuery ML | Seldon |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux |
| Founded | 2008 | 2014 |
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 Seldon
- Kubernetes custom resources
- Inference graphs
- Traffic strategies
- Open Inference Protocol
- Alibi Explain
- Alibi Detect
- Kafka-backed pipelines in v2
- Commercial control plane
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 Seldon
- Linear and logistic regression on warehouse datanot Seldon
- K-means clustering and matrix factorisation for recommendationsnot Seldon
- Time series forecasting with ARIMA_PLUSnot Seldon
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Seldon
Seldon
- Serving an ensemble or a multi-stage inference path as one versioned deployment rather than as a chain of separate servicesnot BigQuery ML
- Running genuine production experiments where a share of live traffic goes to a candidate model and the results are comparednot BigQuery ML
- Regulated environments needing explanations and drift monitoring attached to the served model rather than bolted on laternot BigQuery ML
- Organisations with an established Kubernetes platform team who want serving expressed as manifests under existing deployment controlsnot 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
Seldon
- Seldon Core v2 is under the Business Source Licence rather than Apache 2.0, so production use requires a commercial agreement, and a team that evaluated it believing it was open source discovers the licence is the blocker exactly when the project is ready to ship.
- Core v1 remains Apache 2.0 but is in maintenance, so taking the free route means running software that receives no new development while the architecture it belongs to moves on without it.
- Version 2 is a different system rather than a newer release, with different custom resources, a scheduler component and a Kafka-based pipeline model, so migrating from v1 is a re-implementation of every deployment manifest rather than an upgrade.
- Kafka is a dependency for v2 pipelines, so an organisation that does not already operate it takes on a distributed log with its own storage, retention, rebalancing and failure modes purely in order to serve models.
- Everything assumes Kubernetes fluency and the failure modes are Kubernetes failure modes, custom resource version mismatches, an operator that will not reconcile, admission webhooks and resource limits terminating an inference pod mid-request, so it needs a platform engineer rather than a data scientist.
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
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 Seldon if
- You need kubernetes custom resources.
- You want to start without paying.
- You work on Linux.
- You also want inference graphs.
Questions people ask
- Is BigQuery ML or Seldon better?
- Neither clearly leads. BigQuery ML starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Seldon?
- BigQuery ML starts at Free and Seldon at Free.
- Does BigQuery ML or Seldon run on more platforms?
- BigQuery ML runs on Web. Seldon runs on Linux.
- 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 Seldon is typically brought in for.
- What can BigQuery ML do that Seldon cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Seldon covers Kubernetes custom resources, Inference graphs, Traffic strategies, Open Inference Protocol.
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.
SourceSeldon: Is Seldon open source?
Partly, and this is the thing to check before you build on it. Core v1 is Apache 2.0 but in maintenance. Core v2 was moved to the Business Source Licence in 2024, which allows evaluation but not unlicensed production use. Verify the current licence of each component you intend to run, including MLServer and the Alibi libraries.
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.
SourceSeldon: What is the difference between v1 and v2?
Architecture, not just version number. v2 introduces a scheduler, a different set of custom resources and Kafka-backed pipelines. Manifests, mental model and operations all change, so treat a move as a project.
Seldon: Do I need Kubernetes?
Yes. It is a Kubernetes-native system and there is no meaningful deployment without a cluster and someone competent to run it.
Seldon: What is MLServer?
Seldon's Python inference server implementing the Open Inference Protocol, usable inside Seldon deployments or on its own. Check its current licence alongside Core's, since the company has moved projects onto the Business Source Licence.
Seldon: Do I have to run Kafka?
For v2 pipelines, yes. If you only need single models served, that dependency is a large amount of infrastructure for the benefit, and a simpler serving layer may be the better answer.
Related pages
More on BigQuery ML
Other head to heads
- BigQuery ML vs AWS SageMaker
- BigQuery ML vs Azure Machine Learning
- BigQuery ML vs DataRobot
- BigQuery ML vs Databricks
- BigQuery ML vs SAS
- BigQuery ML vs scikit-learn
- BigQuery ML vs Snowflake
- BigQuery ML vs Weka
- BigQuery ML vs MATLAB
- BigQuery ML vs Palantir Foundry
- BigQuery ML vs Apache Spark MLlib
- BigQuery ML vs Hugging Face
- BigQuery ML vs Kubeflow
- BigQuery ML vs Langwatch
- BigQuery ML vs LlamaIndex
- BigQuery ML vs Milvus
- BigQuery ML vs Neptune.ai
- BigQuery ML vs Amazon Redshift ML
- BigQuery ML vs Google Vertex AI
- BigQuery ML vs BentoML
- BigQuery ML vs Pachyderm
- BigQuery ML vs MLflow
- BigQuery ML vs DVC
- BigQuery ML vs Weights & Biases
- BigQuery ML vs Comet ML
- BigQuery ML vs Dataiku
- BigQuery ML vs Anaconda
- BigQuery ML vs Domino Data Lab
- BigQuery ML vs H2O.ai
- Seldon vs AWS SageMaker
- Seldon vs Azure Machine Learning
- Seldon vs DataRobot
- Seldon vs Databricks
- Seldon vs SAS
- Seldon vs scikit-learn
- Seldon vs Snowflake
- Seldon vs Weka
- Seldon vs MATLAB
- Seldon vs Palantir Foundry
- Seldon vs Apache Spark MLlib
- Seldon vs Hugging Face
- Seldon vs Kubeflow
- Seldon vs Langwatch
- Seldon vs LlamaIndex
- Seldon vs Milvus
- Seldon vs Neptune.ai
- Seldon vs Amazon Redshift ML
- Seldon vs Google Vertex AI
- Seldon vs BentoML
- Seldon vs Pachyderm
- Seldon vs MLflow
- Seldon vs DVC
- Seldon vs Weights & Biases
- Seldon vs Comet ML
- Seldon vs Dataiku
- Seldon vs Anaconda
- Seldon vs Domino Data Lab
- Seldon vs H2O.ai