Machine Learning & Data Science · head to head
Ollama vs BigQuery ML
Ollama
Machine Learning & Data Science
Open-source tool for running LLMs locally on desktop and servers
- From
- Free
- Rated
- -
BigQuery ML
Machine Learning & Data Science
Machine learning in BigQuery using SQL
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Ollama requires user to provide computational hardware; no free cloud compute; models may not fit in available RAM on typical machines; BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it
Where they differ
Only the attributes on which Ollama and BigQuery ML actually diverge.
| Attribute | Ollama | BigQuery ML |
|---|---|---|
| Pricing model | open-source | usage-based |
| Platforms | macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted) | Web |
| Founded | Unknown | 2008 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning & Data Science).
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 Ollama
Nothing recorded that BigQuery ML does not also cover.
Only in BigQuery ML
- SQL-based ML
- AutoML Tables
- Model export
- Prediction functions
- Feature preprocessing
- BigQuery
- Vertex AI
- TensorFlow
What people use each for
The jobs each tool is most often brought in to do.
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
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Ollama
FreeNo published plan breakdown. See the Ollama review.
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 Ollama if
- You want to start without paying.
- You work on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted).
Choose BigQuery ML if
- You need sql-based ml.
- You want to start without paying.
- You also want automl tables.
Questions people ask
- Is Ollama or BigQuery ML better?
- Neither clearly leads. Ollama 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, Ollama or BigQuery ML?
- Ollama starts at Free and BigQuery ML at Free.
- Does Ollama or BigQuery ML run on more platforms?
- Ollama runs on macOS, Windows, Linux, Cloud (AWS, Google Cloud, Azure, self-hosted). BigQuery ML runs on Web.
- Can I use Ollama for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Ollama best used for?
- Ollama is most often used for local development and testing without api costs or rate limits, privacy-sensitive applications requiring data to remain on-device, cost-sensitive deployments where computational resources are already available, fully offline environments or air-gapped networks. Of those, local development and testing without api costs or rate limits and privacy-sensitive applications requiring data to remain on-device are not what BigQuery ML is typically brought in for.
- What can Ollama do that BigQuery ML cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions.
Related pages
More on BigQuery ML
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- BigQuery ML vs Google Vertex AI
- BigQuery ML vs Azure Machine Learning
- BigQuery ML vs DataRobot
- BigQuery ML vs Snowflake
- BigQuery ML vs TensorFlow
- BigQuery ML vs Comet ML
- BigQuery ML vs Keras
- BigQuery ML vs MLflow
- BigQuery ML vs Jupyter
- BigQuery ML vs PyTorch
- BigQuery ML vs scikit-learn
- BigQuery ML vs Apache Spark MLlib
- BigQuery ML vs Weights & Biases
- BigQuery ML vs Alteryx
- BigQuery ML vs Anaconda
- BigQuery ML vs Databricks
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