Software · head to head
BigQuery ML vs Weaviate
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Weaviate the free tier caps at 100,000 objects, 1 GB of memory and a single collection
- They diverge on capability: BigQuery ML covers SQL-based ML, Weaviate covers Vector and keyword search.
Where they differ
Only the attributes on which BigQuery ML and Weaviate actually diverge.
| Attribute | BigQuery ML | Weaviate |
|---|---|---|
| Pricing model | usage-based | freemium |
| Platforms | Web | Linux, Mac, Windows, Web |
| Founded | 2008 | 2019 |
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
- TensorFlow
Only in Weaviate
- Vector and keyword search
- Built-in vectorizers
- GraphQL API
- Multi-tenancy
- Hybrid search
- OpenAI
- Hugging Face
- Cohere
Both cover
- Web support
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 Weaviate
- Linear and logistic regression on warehouse datanot Weaviate
- K-means clustering and matrix factorisation for recommendationsnot Weaviate
- Time series forecasting with ARIMA_PLUSnot Weaviate
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Weaviate
Weaviate
- Running a vector database for semantic and hybrid searchnot BigQuery ML
- Generating and storing embeddings alongside the objects they describenot 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
Weaviate
- The free tier caps at 100,000 objects, 1 GB of memory and a single collection
- Billing is per million vector dimensions rather than per record, so wider embeddings cost proportionally more for the same object count
- Premium is a prepaid contract starting at $400 a month rather than pay as you go
- Storage rates do not fall consistently with tier, and Premium Dedicated is $0.1505 per GiB against $0.12 on the cheaper Flex plan
- The Query Agent is metered separately, free to 1,000 requests a month and $30 a month plus overage beyond
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Weaviate
Free- Open SourceFree
- Full features
- Self-hosted
- ServerlessFree
- Managed service
- Auto-scaling
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 Weaviate if
- You need vector and keyword search.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want built-in vectorizers.
Questions people ask
- Is BigQuery ML or Weaviate better?
- Neither clearly leads. BigQuery ML starts at Free and Weaviate at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Weaviate?
- BigQuery ML starts at Free and Weaviate at Free.
- Does BigQuery ML or Weaviate run on more platforms?
- BigQuery ML runs on Web. Weaviate 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 Weaviate is typically brought in for.
- What can BigQuery ML do that Weaviate cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Weaviate covers Vector and keyword search, Built-in vectorizers, GraphQL API, Multi-tenancy. Both handle Web support.