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Machine Learning · head to head

BigQuery ML vs Snowplow

BigQuery ML logo

BigQuery ML

Machine Learning

Machine learning in BigQuery using SQL

From
Free
Rated
-
Full reviewWebsite
Snowplow logo

Snowplow

Business Intelligence

Behavioural data pipeline you run in your own cloud, relicensed away from Apache 2.0 in 2024

From
On request
Rated
-
Full reviewWebsite

The short version

  • Only BigQuery ML has a free tier, so it costs nothing to try first.
  • Each has a real cost: BigQuery ML not available in BigQuery's Standard edition, so the cheapest tier cannot use it; Snowplow the core pipeline was relicensed from Apache 2.0 on 8 January 2024 to the Snowplow Limited Use Licence Agreement and a Confluent-derived community licence, so organisations that adopted it as permissively licensed software must now review their entitlement, buy a licence, or migrate.
  • They diverge on capability: BigQuery ML covers SQL-based ML, Snowplow covers Own-cloud deployment.
  • Prices and features above were last checked on 31 August 2026.

Where they differ

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

Attributes where BigQuery ML and Snowplow differ
AttributeBigQuery MLSnowplow
Starting priceFreeOn request
Pricing modelusage-basedquote
Free tierYesNo
PlatformsWebLinux, Web, Docker
CategoryMachine LearningBusiness Intelligence
Founded2008Unknown

Identical on both: 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 Snowplow

  • Own-cloud deployment
  • Schema enforcement
  • Warehouse loading
  • Enrichment
  • Trackers
  • Streaming output
  • Data models
  • Snowplow BDP

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

Snowplow

  • A data team that needs full-fidelity event data in its own warehouse to build attribution or machine learning features rather than to populate dashboardsnot BigQuery ML
  • A regulated business that cannot send behavioural data to a third-party analytics vendor and must keep collection inside its own cloud accountnot BigQuery ML
  • A product organisation tired of silently malformed events, which wants a schema contract enforced at collection timenot BigQuery ML
  • A company modelling customer behaviour across web, mobile and server events that needs them in one consistent structurenot 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

Snowplow

  • The core pipeline was relicensed from Apache 2.0 on 8 January 2024 to the Snowplow Limited Use Licence Agreement and a Confluent-derived community licence, so organisations that adopted it as permissively licensed software must now review their entitlement, buy a licence, or migrate.
  • You run the pipeline in your own cloud, which means the infrastructure bill, the on-call rota and the upgrade work are yours, and at high event volume that operational cost frequently exceeds what a hosted product would have charged.
  • Schema enforcement is the main benefit and the main friction, because every new event requires a schema to be authored and versioned, and teams without discipline around that end up blocked on their own governance process.
  • There is no analysis layer: Snowplow delivers data to your warehouse and nothing else, so you still need modelling, a BI tool and the people to run them before anyone sees a number.
  • The licence change fractured the community, spawning an Apache 2.0 fork, which means community contributions and third-party tooling are now split across two codebases with uncertain long-term maintenance.

Pricing, plan by plan

BigQuery ML

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

Snowplow

On request
  • Snowplow BDP$undefined/year
    • Commercial pricing quoted by event volume and deployment
    • Core pipeline components under the Snowplow Limited Use Licence Agreement, not Apache 2.0
    • Trackers, analytics SDKs and Iglu SDKs remain Apache 2.0

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 Snowplow if

  • You need own-cloud deployment.
  • You work on Linux, Web, Docker.
  • You also want schema enforcement.
Snowplow review

Questions people ask

Is BigQuery ML or Snowplow better?
Neither clearly leads. BigQuery ML starts at Free and Snowplow at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, BigQuery ML or Snowplow?
BigQuery ML has a free tier; the other does not. Paid plans start at Free for BigQuery ML and On request for Snowplow.
Does BigQuery ML or Snowplow run on more platforms?
BigQuery ML runs on Web. Snowplow runs on Linux, Web, Docker.
Can I use BigQuery ML for free?
Yes. BigQuery ML has a free tier, so you can try it without paying. Snowplow starts at On request.
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 Snowplow is typically brought in for.
What can BigQuery ML do that Snowplow cannot?
BigQuery ML covers SQL-based ML, AutoML Tables, Model export, Prediction functions. Snowplow covers Own-cloud deployment, Schema enforcement, Warehouse loading, Enrichment.

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
Snowplow: Is Snowplow still open source?

Not in the permissive sense. On 8 January 2024 the core pipeline moved from Apache 2.0 to the Snowplow Limited Use Licence Agreement, with version 1.1 following in December 2024, alongside a community licence based on the Confluent Community Licence. Trackers and analytics SDKs remain Apache 2.0.

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
Snowplow: Can we still run it for free in production?

Free use of the relicensed core components is materially constrained and commercial use generally requires an agreement. Read the current licence text against your intended use rather than relying on older documentation.

Snowplow: Is there an Apache 2.0 alternative?

Yes, a fork called OpenSnowcat was created in response to the relicensing and continues under Apache 2.0.

Snowplow: What does Snowplow BDP cost?

Not published. It is quoted by event volume and deployment, and your own cloud infrastructure costs are separate and additional.

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