Snowplow

Named in 9 judge labels across 3 categories by 4 of 6 models in the September 2026 Edition. 0 first choices, 1 negative label. Every number here is derived from the raw labels under vendor table v2026-09-08.5.

Standing

9 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Snowplow was named 9 times in Warehouse & ETL and 2 other categories, where Hightouch led with 40%. The labels and the evidence are below, counted exactly.

Standing by category

Every category where a model named Snowplow. 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	0%	10 of 62	0%	4	under 10 labels · led by Hightouch at 40%
Customer data platforms	GTM data and infrastructure	0%	10 of 35	0%	3	under 10 labels · led by Hightouch at 42%
Product analytics	GTM data and infrastructure	0%	20 of 32	50%	2	under 10 labels · led by Amplitude at 38%

By model

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

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

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Powerful behavioral data collection, self-hostable. Strong for data teams comfortable managing their own pipeline.” Claude Opus 4.8 · CDP · budget prompt · alternative

“pushes hard toward self-hosted or warehouse-native (PostHog self-hosted, or Snowplow + your own warehouse)” Claude Opus 5 · Product analytics · negative prompt · alternative

“RudderStack or Snowplow give you warehouse-first event capture” Claude Opus 5 · Warehouse & ETL · paraphrase prompt · alternative

“Excellent for companies with strict data governance needs.” Gemini 3.1 Pro · CDP · budget prompt · alternative

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.

“More infrastructure than turnkey product analytics; usually requires analysts, data engineers, and a visualization layer” GPT-5.6 Sol · Product analytics · comparative prompt · soft negative

Names read as Snowplow

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

Snowplow (Community Edition) 1

Is this your product?

Claim this page

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 Snowplow, 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.