Snowflake

Named in 32 judge labels across 1 category by 6 of 6 models in the September 2026 Edition. 10 first choices, 6 negative labels. Every number here is derived from the raw labels under vendor table v2026-09-08.5.

Best standing

23% in Warehouse & ETL

Rank 3 of 62 products named in data warehouse and reverse etl for marketing, accepted challenger. 6 of 6 models made it the first choice on the direct prompt; 19% of its 32 labels there were negative.

Standing by category

Every category where a model named Snowflake. 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	23%	3 of 62	19%	32	accepted challenger

By model

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

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

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Snowflake generally leads in ease of use and minimal administration... very approachable for data-savvy marketing analysts” Claude Opus 4.8 · Warehouse & ETL · comparative prompt · first choice

“Snowflake: (Top Recommendation for Ease of Use) Snowflake is the industry standard for the modern data stack.” Gemini 3.1 Pro · Warehouse & ETL · scale prompt · first choice

“the strongest default choice is Snowflake or BigQuery as the warehouse, paired with Hightouch for reverse ETL” Perplexity Sonar Pro · Warehouse & ETL · direct prompt · first choice

“My default shortlist is Snowflake + dbt + Hightouch, with Fivetran for ingestion.” GPT-6 Astra · Warehouse & ETL · direct 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.

“Avoid Snowflake, Fivetran and enterprise CDP packages at the beginning” GPT-5.6 Sol · Warehouse & ETL · budget prompt · hard negative

“More powerful for scaling but can get expensive if not carefully managed... for a small marketing team, the credits can add up.” Claude Opus 4.8 · Warehouse & ETL · budget prompt · soft negative

“The trap with Snowflake at low volume: Snowflake costs more because even a small warehouse spins up when you run queries” Claude Opus 5 · Warehouse & ETL · budget prompt · soft negative

“Snowflake and BigQuery are generally safe choices—but only with spending guardrails.” GPT-5.6 Sol · Warehouse & ETL · negative prompt · soft negative

Names read as Snowflake

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

Every label used the canonical name.

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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 Snowflake, 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.