Software · head to head
Milvus vs BigQuery ML
Milvus
Software
Open-source vector database for scalable similarity search
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Milvus vector dimensions are capped at 32,768; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
- They diverge on capability: Milvus covers Billion-scale vectors, BigQuery ML covers SQL-based ML.
Where they differ
Only the attributes on which Milvus and BigQuery ML actually diverge.
| Attribute | Milvus | BigQuery ML |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Linux, Mac, Windows, Web | Web |
| Founded | 2017 | 2008 |
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 Milvus
- Billion-scale vectors
- Multiple index types
- GPU acceleration
- Hybrid search
- Data partitioning
- PyTorch
- Hugging Face
- LangChain
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- Cloud Storage
Both cover
- TensorFlow
- Web support
What people use each for
The jobs each tool is most often brought in to do.
Milvus
- Self hosting a vector database for semantic searchnot BigQuery ML
- Storing and querying embeddings for retrieval augmented generationnot BigQuery ML
- Similarity search over images, audio or text at scalenot BigQuery ML
BigQuery ML
- Training models in SQL without exporting datanot Milvus
- Linear and logistic regression on warehouse datanot Milvus
- K-means clustering and matrix factorisation for recommendationsnot Milvus
- Time series forecasting with ARIMA_PLUSnot Milvus
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Milvus
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Milvus
- Vector dimensions are capped at 32,768
- A collection is limited to 64 fields, 1,024 partitions and 16 shards
- Only 1 index is allowed per field
- Search returns at most 16,384 vectors as top-k, and nq is capped at 16,384
- Input and output per RPC is capped at 64 MB for insert, search and query
- VARCHAR values are limited to 65,535 characters
- Data loaded into query nodes cannot exceed 90% of available memory
- An instance supports at most 65,536 collections
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
Pricing, plan by plan
Milvus
Free- Open SourceFree
- Full features
- Self-hosted
- Community support
- Zilliz CloudFree
- Managed service
- Free tier available
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Which should you pick?
Choose Milvus if
- You need billion-scale vectors.
- You want to start without paying.
- You work on Linux, Mac, Windows, Web.
- You also want multiple index types.
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is Milvus or BigQuery ML better?
- Neither clearly leads. Milvus starts at Free and BigQuery ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Milvus or BigQuery ML?
- Milvus starts at Free and BigQuery ML at Free.
- Does Milvus or BigQuery ML run on more platforms?
- Milvus runs on Linux, Mac, Windows, Web. BigQuery ML runs on Web.
- Can I use Milvus for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Milvus best used for?
- Milvus is most often used for self hosting a vector database for semantic search, storing and querying embeddings for retrieval augmented generation, similarity search over images, audio or text at scale. Of those, self hosting a vector database for semantic search and storing and querying embeddings for retrieval augmented generation are not what BigQuery ML is typically brought in for.
- What can Milvus do that BigQuery ML cannot?
- Milvus covers Billion-scale vectors, Multiple index types, GPU acceleration, Hybrid search. BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Both handle TensorFlow, Web support.
Related pages
More on BigQuery ML
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