Software · head to head
BigQuery ML vs Ollama
Ollama
Software
Open-source tool for running LLMs locally on desktop and servers
- 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; Ollama requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines
Where they differ
Only the attributes on which BigQuery ML and Ollama actually diverge.
| Attribute | BigQuery ML | Ollama |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted) |
| Founded | 2008 | Unknown |
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 Ollama
Nothing recorded that BigQuery ML does not also cover.
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 Ollama
- Linear and logistic regression on warehouse datanot Ollama
- K-means clustering and matrix factorisation for recommendationsnot Ollama
- Time series forecasting with ARIMA_PLUSnot Ollama
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Ollama
Ollama
- Local development and testing without API costs or rate limitsnot BigQuery ML
- Privacy-sensitive applications requiring data to remain on-devicenot BigQuery ML
- Cost-sensitive deployments where computational resources are already availablenot BigQuery ML
- Fully offline environments or air-gapped networksnot 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
Ollama
- Requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines
- No hosted service option for inference; all computational burden falls to user
- Limited to open-weight models; cannot run proprietary models like GPT-4 or Claude locally
- Performance depends entirely on user's hardware; no SLAs or guarantees on speed
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Ollama
FreeNo published plan breakdown. See the Ollama review.
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 Ollama if
- You want to start without paying.
- You work on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
Questions people ask
- Is BigQuery ML or Ollama better?
- Neither clearly leads. BigQuery ML starts at Free and Ollama at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Ollama?
- BigQuery ML starts at Free and Ollama at Free.
- Does BigQuery ML or Ollama run on more platforms?
- BigQuery ML runs on Web. Ollama runs on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
- 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 Ollama is typically brought in for.
- What can BigQuery ML do that Ollama cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions.
Related pages
More on BigQuery ML
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