Databricks

Named in 17 judge labels across 2 categories by 5 of 6 models in the September 2026 Edition. 0 first choices, 7 negative labels. Every number here is derived from the raw labels under vendor table v2026-09-08.5.

Best standing

0% in Warehouse & ETL

Rank 44 of 62 products named in data warehouse and reverse etl for marketing, criticized challenger. 0 of 6 models made it the first choice on the direct prompt; 44% of its 16 labels there were negative.

Standing by category

Every category where a model named Databricks. 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%	44 of 62	44%	16	criticized challenger
Customer data platforms	GTM data and infrastructure	0%	27 of 35	0%	1	under 10 labels

By model

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

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

What the models said for it

Verbatim evidence the judge attached to positive labels.

“add Databricks or Redshift only when your existing cloud/data strategy strongly favors them” GPT-5.6 Sol · Warehouse & ETL · scale prompt · alternative

“I'd prioritize it when marketing can reuse an existing Databricks foundation” GPT-6 Astra · Warehouse & ETL · comparative prompt · alternative

“best fit when marketing analytics depends heavily on predictive models” GPT-5.6 Sol · Warehouse & ETL · comparative prompt · alternative

“Teams doing heavy ML — propensity scores, LTV models, MMM” Claude Opus 5 · Warehouse & ETL · comparative 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.

“I would not make Databricks the default unless the company has unusually large product-event volumes” GPT-5.6 Sol · Warehouse & ETL · direct prompt · soft negative

“Databricks can be massive overkill. It often requires highly specialized engineers to maintain” Gemini 3.1 Pro · Warehouse & ETL · negative prompt · soft negative

“Databricks is incredibly powerful but usually overkill for a 500-person company” Gemini 3.1 Pro · Warehouse & ETL · scale prompt · soft negative

“Databricks is excellent but overkill unless you have real ML/data-science needs” Claude Opus 5 · Warehouse & ETL · direct prompt · soft negative

Names read as Databricks

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

Databricks CustomerLake 1Databricks SQL 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 Databricks, 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.