Machine Learning · head to head
BigQuery ML vs Semantic Kernel
Semantic Kernel
Machine Learning
Model-agnostic SDK for AI orchestration
- 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; Semantic Kernel steep learning curve for advanced features
- They diverge on capability: BigQuery ML covers SQL-based ML, Semantic Kernel covers Multi-model support.
Where they differ
Only the attributes on which BigQuery ML and Semantic Kernel actually diverge.
| Attribute | BigQuery ML | Semantic Kernel |
|---|---|---|
| Pricing model | usage-based | Open source, no pricing |
| Platforms | Web | Python, .NET, Java |
| Founded | 2008 | Unknown |
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 Semantic Kernel
- Multi-model support
- Agent framework
- Multi-agent systems
- Plugin ecosystem
- Vector database integration
- Multimodal support
- Local model support
- Enterprise observability
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 Semantic Kernel
- Linear and logistic regression on warehouse datanot Semantic Kernel
- K-means clustering and matrix factorisation for recommendationsnot Semantic Kernel
- Time series forecasting with ARIMA_PLUSnot Semantic Kernel
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Semantic Kernel
Semantic Kernel
- Building enterprise AI applications with LLM integrationnot BigQuery ML
- Creating multi-agent systems for complex workflowsnot BigQuery ML
- Developing AI-powered chatbots and assistantsnot BigQuery ML
- Implementing RAG systems with vector databasesnot 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
Semantic Kernel
- Steep learning curve for advanced features
- Documentation focuses on Azure cloud services
- Configuration complexity for multi-model scenarios
- Requires understanding of AI/LLM concepts
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Semantic Kernel
Free- Open SourceFree
- MIT license
- Full framework access
- All language SDKs
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 Semantic Kernel if
- You need multi-model support.
- You want to start without paying.
- You work on Python, .NET, Java.
- You also want agent framework.
Questions people ask
- Is BigQuery ML or Semantic Kernel better?
- Neither clearly leads. BigQuery ML starts at Free and Semantic Kernel at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Semantic Kernel?
- BigQuery ML starts at Free and Semantic Kernel at Free.
- Does BigQuery ML or Semantic Kernel run on more platforms?
- BigQuery ML runs on Web. Semantic Kernel runs on Python, .NET, Java.
- 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 Semantic Kernel is typically brought in for.
- What can BigQuery ML do that Semantic Kernel cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Semantic Kernel covers Multi-model support, Agent framework, Multi-agent systems, Plugin ecosystem.
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.
SourceSemantic Kernel: What LLM providers does Semantic Kernel support?
Semantic Kernel supports OpenAI, Azure OpenAI, Hugging Face, Nvidia, and other providers through extensible model implementations.
SourceBigQuery 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.
SourceSemantic Kernel: Can I run Semantic Kernel locally?
Yes. Semantic Kernel supports local models through Ollama, LMStudio, and ONNX for complete data control and offline operation.
SourceSemantic Kernel: Is Semantic Kernel free?
Yes. Semantic Kernel is MIT-licensed open source and completely free. You only pay for external LLM APIs you use.
SourceRelated pages
More on BigQuery ML
More on Semantic Kernel
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