Machine Learning · head to head
BigQuery ML vs Postgres
The short version
- Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Postgres each major version is supported for only 5 years after its initial release, after which it is end-of-life
- They diverge on capability: BigQuery ML covers SQL-based ML, Postgres covers ACID compliance.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which BigQuery ML and Postgres actually diverge.
| Attribute | BigQuery ML | Postgres |
|---|---|---|
| Pricing model | usage-based | open-source |
| Platforms | Web | Linux, Windows, Macos, Docker |
| Category | Machine Learning | Technology |
| Founded | 2008 | 1996 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 Postgres
- ACID compliance
- Complex queries
- Foreign keys
- Triggers
- Views
- Stored procedures
- JSON/JSONB support
- Full-text search
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 Postgres
- Linear and logistic regression on warehouse datanot Postgres
- K-means clustering and matrix factorisation for recommendationsnot Postgres
- Time series forecasting with ARIMA_PLUSnot Postgres
- Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot Postgres
Postgres
- Running a general purpose relational database for applicationsnot BigQuery ML
- Self-hosting an open source SQL database with no licence feenot BigQuery ML
- Workloads needing extensions, JSON and full text search in one enginenot 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
Postgres
- Each major version is supported for only 5 years after its initial release, after which it is end-of-life
- Major version upgrades break on-disk compatibility and require a full dump and reload or the pg_upgrade tool
- New major versions ship about once a year, so staying supported means a disruptive upgrade cycle
- Minor releases contain only frequently-encountered bug fixes, low-risk fixes, security issues and data corruption fixes, so feature gaps are not addressed within a major version
- There is no vendor SLA; commercial support must be bought separately from third party professional services listed by the project
Pricing, plan by plan
BigQuery ML
Free- Free TierFree
- 10GB storage
- 1TB queries
- On-Demand$5/TB
- Pay per TB scanned
- ML training costs
Postgres
Free- Community EditionFree
- Full database features
- No limitations
- Community support
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 Postgres if
- You need acid compliance.
- You want to start without paying.
- You work on Linux, Windows, Macos, Docker.
- You also want complex queries.
Questions people ask
- Is BigQuery ML or Postgres better?
- Neither clearly leads. BigQuery ML starts at Free and Postgres at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, BigQuery ML or Postgres?
- BigQuery ML starts at Free and Postgres at Free.
- Does BigQuery ML or Postgres run on more platforms?
- BigQuery ML runs on Web. Postgres runs on Linux, Windows, Macos, Docker.
- 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 Postgres is typically brought in for.
- What can BigQuery ML do that Postgres cannot?
- BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Postgres covers ACID compliance, Complex queries, Foreign keys, Triggers.
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.
SourcePostgres: How much does PostgreSQL cost to use?
PostgreSQL is completely free to download, install, and use. No licensing fees, subscription costs, or per-seat charges apply. The database is open source under the PostgreSQL License. Source: https://www.postgresql.org
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.
SourcePostgres: Are there commercial PostgreSQL support options available?
Official PostgreSQL (the project) is free. Commercial PostgreSQL services such as hosting, professional support, training, and managed database services are offered by third-party vendors, not the PostgreSQL project itself. Source: https://www.postgresql.org
SourcePostgres: Do I need a license to use PostgreSQL commercially?
No. PostgreSQL is open source under the PostgreSQL License, which permits free commercial use without royalties, licensing fees, or support obligations. Source: https://www.postgresql.org
SourceRelated pages
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- Postgres vs Notion
- Postgres vs Close
- Postgres vs Coda
- Postgres vs Drift
- Postgres vs JetBrains IntelliJ IDEA
- Postgres vs LogRocket
- Postgres vs Neovim