BigQuery

Named in 35 judge labels across 1 category by 6 of 6 models in the September 2026 Edition. 11 first choices, 3 negative labels. Every number here is derived from the raw labels under vendor table v2026-09-08.5.

Best standing

26% in Warehouse & ETL

Rank 2 of 62 products named in data warehouse and reverse etl for marketing, accepted challenger. 2 of 6 models made it the first choice on the direct prompt; 9% of its 35 labels there were negative.

Standing by category

Every category where a model named BigQuery. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Vertical	First choices	Rank	Negative rate	Labels	Quadrant
Data warehouse and reverse ETL for marketing	GTM data and infrastructure	26%	2 of 62	9%	35	accepted challenger

By model

How each model treated BigQuery across every prompt where it was named. Six models, six prompts per category.

Model	First choice	Alternative	Mention	Negative	Labels
Claude Opus 5	1	3	1	1	6
Claude Opus 4.8	2	2	1	1	6
GPT-6 Astra	2	3	0	1	6
GPT-5.6 Sol	4	2	0	0	6
Gemini 3.1 Pro	3	3	0	0	6
Perplexity Sonar Pro	3	1	1	0	5

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Google BigQuery is often the easiest starting point for marketing teams because it is serverless, scales automatically” Perplexity Sonar Pro · Warehouse & ETL · comparative prompt · first choice

“BigQuery + dbt Core + RudderStack Free, with Airbyte only for sources that lack a native BigQuery connector.” GPT-5.6 Sol · Warehouse & ETL · budget prompt · first choice

“Verdict for tight budgets: Start with BigQuery for its pay-per-use model and near-zero cost at low volumes.” Claude Opus 4.8 · Warehouse & ETL · budget prompt · first choice

“My default pick is BigQuery + Hightouch, with native exports or Fivetran’s free plan for ingestion.” GPT-6 Astra · Warehouse & ETL · budget prompt · first choice

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“pay-per-query costs spike when analysts write inefficient full-table scans — one bad query can cost ~$50” Claude Opus 5 · Warehouse & ETL · negative prompt · soft negative

“Be cautious with frequent, broad audience queries under on-demand pricing.” GPT-6 Astra · Warehouse & ETL · negative prompt · soft negative

“BigQuery — watch the query-based cost model” Claude Opus 4.8 · Warehouse & ETL · negative prompt · soft negative

Names read as BigQuery

What the judge wrote, as written, with how often. The vendor table decides that these count as BigQuery; a claim can dispute any of them.

Google BigQuery 13BigQuery (Google Cloud) 1

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Claiming is free and changes nothing in the data. A claimed page gets a verified contact who is told when each edition publishes; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as BigQuery, applied by version and listed in the change log; and a logo and one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or quadrants, any preview, or any say over which quotes appear. Sponsorship is separate: a sponsor funds categories or buyer dimensions and is named on what it funded, and the rules are the same for every sponsor.