Two of fourteen models named Amazon Marketing Cloud first on the direct prompt; one named Databricks Clean Rooms. Amazon Marketing Cloud was named by twelve of the fourteen models and Databricks Clean Rooms by twelve and Amazon Marketing Cloud carries 24 labels and Databricks Clean Rooms 27, so the shares are not directly comparable.
By Amazon, Seattle, United States, founded 1994. Named in one category this edition.
By Databricks, San Francisco, United States, founded 2013. Named in one category this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the data clean rooms page.
Across every category in the October 2026 Edition, Amazon Marketing Cloud and Databricks Clean Rooms were named in the same answer twenty-two times, of the 62 answers naming Amazon Marketing Cloud and the 73 naming Databricks Clean Rooms. In those answers Databricks Clean Rooms took the first choice zero times and Amazon Marketing Cloud five.
| Model | DirectAM | ParaphraseAM | ComparativeAM | Budget-constrainedAM | Scale-constrainedAM | NegativeAM |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | AM | |||||
| GPT-5.4 mini | AM | |||||
| Gemini 3.5 Flash | AM | AM | AM | AM | ||
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | AM | |||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | AM | AM | ||||
| Llama 4 Maverick | AM | |||||
| Qwen 3.7 Flash | AM | |||||
| Kimi K2 | AM | |||||
| GLM 4.7 FlashX | AM | AM | ||||
| MiniMax M2.5 | AM | AM | ||||
| GPT-6 Luna | AM | AM | ||||
| Muse Glimmer 30B | AM | AM |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Walled garden DCRs are incredibly powerful, but only within their own ecosystems. They are plagued by severe vendor lock-in.” Gemini 3.5 Flash · negative prompt · soft negative
“Walled Garden Clean Rooms (Google, Meta, Amazon) ... Why to be cautious: Vendor lock-in” DeepSeek V4 Flash · negative prompt · soft negative
“Amazon documents a 100-distinct-user threshold for certain first-party data analyses.” GPT-6 Luna · negative prompt · soft negative
“Amazon Marketing Cloud (AMC) seems particularly relevant if your company has significant Amazon advertising presence... it could be an accessible option for mid-market companies” MiniMax M2.5 · direct prompt · first choice
“set up Google Ads Data Hub or Amazon Marketing Cloud. It will teach your team how to work with clean room query logic without costing you a massive licensing fee.” Gemini 3.5 Flash · scale prompt · first choice
“If ad-focused, begin with free walled-garden DCRs (AMC/Google)—they solve 80% of needs without spend.” Grok 4.1 Fast · budget prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Cloud-based (e.g., AWS Clean Rooms, Snowflake, Databricks) | Strong infrastructure, but integration complexity and variable TCO” Grok 4.1 Fast · negative prompt · soft negative
“Some concerns about identity resolution rules not being well-documented” Kimi K2 · negative prompt · soft negative
“user reviews of Databricks Clean Rooms have scored poorly here” DeepSeek V4 Flash · negative prompt · soft negative
“Snowflake, Databricks, and LiveRamp Safe Haven provide balanced scalability and governance support.” Claude Haiku 4.5 · direct prompt · first choice
“Choose Snowflake, Databricks, or AWS if you already have a mature data team” Gemini 3.5 Flash · comparative prompt · first choice
“If your data ecosystem is built on Databricks, their Delta Sharing and Unity Catalog offer a similar, highly secure distributed data clean room experience.” Gemini 3.5 Flash · paraphrase prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.