Machine Learning · head to head
BigQuery ML vs LangChain
LangChain
Machine Learning
Build applications with LLMs through composability
- 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; LangChain the free Developer plan of LangSmith is limited to 1 seat
- They diverge on capability: BigQuery ML covers SQL-based ML, LangChain covers Chains and agents.
Where they differ
Only the attributes on which BigQuery ML and LangChain actually diverge.
| Attribute | BigQuery ML | LangChain |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows |
| Founded | 2008 | 2022 |
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 LangChain
- Chains and agents
- Retrieval-augmented generation
- Memory management
- Tool integration
- Prompt templates
- OpenAI
- Anthropic
- Hugging Face
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 LangChain
- Linear and logistic regression on warehouse datanot LangChain
- K-means clustering and matrix factorisation for recommendationsnot LangChain
- Time series forecasting with ARIMA_PLUSnot LangChain
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot LangChain
LangChain
- Building LLM applications and agents in Python or JavaScriptnot BigQuery ML
- Tracing and debugging LLM chains and agent runsnot BigQuery ML
- Evaluating prompt and model changes against datasetsnot 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
LangChain
- The free Developer plan of LangSmith is limited to 1 seat
- Base traces are retained for 14 days only; 400 day retention costs extra
- Included traces are capped at 5,000 per month on Developer and 10,000 per month on Plus, with everything beyond billed pay as you go
- Self hosted and hybrid deployment of LangSmith is Enterprise only
- Custom SSO, RBAC and ABAC are Enterprise only
- A support SLA is Enterprise only
- Enterprise pricing is by quote with no published rate
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
LangChain
Free- Open SourceFree
- Full framework
- Community support
- LangSmith$39/month
- Debugging
- Monitoring
- Testing
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 LangChain if
- You need chains and agents.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want retrieval-augmented generation.
Questions people ask
- Is BigQuery ML or LangChain better?
- Neither clearly leads. BigQuery ML starts at Free and LangChain at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or LangChain?
- BigQuery ML starts at Free and LangChain at Free.
- Does BigQuery ML or LangChain run on more platforms?
- BigQuery ML runs on Web. LangChain runs on Linux, Mac, Windows.
- 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 LangChain is typically brought in for.
- What can BigQuery ML do that LangChain cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. LangChain covers Chains and agents, Retrieval-augmented generation, Memory management, Tool integration.
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.
SourceLangChain: Does LangChain charge for its services?
LangChain's main website does not display pricing. However, LangSmith (a related platform) offers both free and paid plans. Visit the dedicated pricing page or contact LangChain for details.
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.
SourceLangChain: How can I learn about LangChain pricing?
Click on the Pricing link in navigation or use the Try LangSmith or Get a demo options to explore pricing for LangChain's commercial offerings.
SourceRelated 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 MLflow
- BigQuery ML vs Snowflake
- BigQuery ML vs Comet ML
- BigQuery ML vs Jupyter
- BigQuery ML vs Pinecone
- BigQuery ML vs Python
- BigQuery ML vs PyTorch
- BigQuery ML vs scikit-learn
- BigQuery ML vs Apache Spark MLlib
- BigQuery ML vs Weaviate
- BigQuery ML vs Weights & Biases
- BigQuery ML vs Alteryx
- BigQuery ML vs Anaconda
- BigQuery ML vs Dataiku
- BigQuery ML vs Google Vertex AI
- BigQuery ML vs TensorFlow
- LangChain vs AWS SageMaker
- LangChain vs Azure Machine Learning
- LangChain vs DataRobot
- LangChain vs MLflow
- LangChain vs Snowflake
- LangChain vs Comet ML
- LangChain vs Jupyter
- LangChain vs Pinecone
- LangChain vs Python
- LangChain vs PyTorch
- LangChain vs scikit-learn
- LangChain vs Apache Spark MLlib
- LangChain vs Weaviate
- LangChain vs Weights & Biases
- LangChain vs Alteryx
- LangChain vs Anaconda
- LangChain vs Dataiku
- LangChain vs Google Vertex AI
- LangChain vs TensorFlow