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BigQuery ML vs DuckDB

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
DuckDB logo

DuckDB

Databases

MIT-licensed analytical SQL database that runs inside your process, with no server, no dependencies and one writer at a time.

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; DuckDB a database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
  • They diverge on capability: BigQuery ML covers SQL-based ML, DuckDB covers In-process execution.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which BigQuery ML and DuckDB actually diverge.

Attributes where BigQuery ML and DuckDB differ
AttributeBigQuery MLDuckDB
Pricing modelusage-basedopen-source
PlatformsWebLinux, macOS, Windows, WebAssembly
CategoryMachine LearningDatabases
Founded20082019

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 DuckDB

  • In-process execution
  • Vectorised columnar engine
  • Direct file querying
  • Zero dependencies
  • Larger-than-memory queries
  • MIT licence
  • Postgres-flavoured SQL
  • Extension ecosystem

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 DuckDB
  • Linear and logistic regression on warehouse datanot DuckDB
  • K-means clustering and matrix factorisation for recommendationsnot DuckDB
  • Time series forecasting with ARIMA_PLUSnot DuckDB
  • Running imported ONNX, TensorFlow or XGBoost models against BigQuery datanot DuckDB

DuckDB

  • Transformation steps in a data pipeline that would otherwise need Spark, replaced by SQL over Parquet in a single processnot BigQuery ML
  • Analytical queries embedded in an application or a dashboard where shipping a database server alongside it is not acceptablenot BigQuery ML
  • Local exploration of files that are too large for a pandas dataframe but far too small to justify a warehousenot BigQuery ML
  • Continuous integration and testing of analytical SQL, where a real engine can run in the test process without provisioning anythingnot 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

DuckDB

  • A database file accepts one read-write process at a time; other processes must open it read-only and will not see subsequent writes, so DuckDB cannot be the shared database behind several services.
  • There is no network protocol, authentication or user management, so exposing it to remote clients means writing and securing your own service around it.
  • It is built for scans and aggregations, not for many small transactions, so a workload of high-frequency single-row inserts and updates performs badly compared with SQLite or Postgres.
  • Storage files are backwards compatible but not forwards compatible, so a file written by a newer version cannot be read by an older one and every consumer of a shared file must be upgraded together.
  • Query memory settings matter: some operations still need to hold significant state, so an under-configured memory limit turns a large join or a high-cardinality aggregation into a spill-heavy query or an out-of-memory failure rather than a slow success.

Pricing, plan by plan

BigQuery ML

Free
  • Free TierFree
    • 10GB storage
    • 1TB queries
  • On-Demand$5/TB
    • Pay per TB scanned
    • ML training costs

DuckDB

Free

No published plan breakdown. See the DuckDB 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.
BigQuery ML review

Choose DuckDB if

  • You need in-process execution.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, WebAssembly.
  • You also want vectorised columnar engine.
DuckDB review

Questions people ask

Is BigQuery ML or DuckDB better?
Neither clearly leads. BigQuery ML starts at Free and DuckDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or DuckDB?
BigQuery ML starts at Free and DuckDB at Free.
Does BigQuery ML or DuckDB run on more platforms?
BigQuery ML runs on Web. DuckDB runs on Linux, macOS, Windows, WebAssembly.
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 DuckDB is typically brought in for.
What can BigQuery ML do that DuckDB cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. DuckDB covers In-process execution, Vectorised columnar engine, Direct file querying, Zero dependencies.

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.

Source
DuckDB: Can multiple applications share one DuckDB database?

Not for writing. One process holds the database read-write; others may attach read-only and will not see later writes. Shared multi-writer access needs a different database or a table format with a catalogue.

BigQuery 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.

Source
DuckDB: Is it a replacement for a data warehouse?

For single-node analytical workloads up to a few hundred gigabytes it very often is. It is not a replacement when many concurrent users need a shared, governed, always-on service.

DuckDB: Do I have to load data into it?

No. It queries Parquet, CSV, JSON and Arrow in place, including on object storage. Its own storage format is optional and mainly useful when you want indexes, constraints and faster repeated access.

DuckDB: What is MotherDuck's relationship to it?

MotherDuck is a separate company offering a managed and hybrid service built on the DuckDB engine. DuckDB itself remains MIT-licensed and independent of it, with the IP held by the DuckDB Foundation.

DuckDB: Is it suitable for OLTP?

No. It is designed for analytical scans. For transactional workloads with frequent small writes, SQLite or Postgres is the right tool.

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