Six of fourteen models named Snowflake Data Clean Rooms first on the direct prompt; two named Amazon Marketing Cloud. Snowflake Data Clean Rooms was named by fourteen of the fourteen models and Amazon Marketing Cloud by twelve and Snowflake Data Clean Rooms carries 52 labels and Amazon Marketing Cloud 24, so the shares are not directly comparable.
By Snowflake Inc., San Mateo, United States, founded 2012. Named in one category this edition.
By Amazon, Seattle, United States, founded 1994. 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, Snowflake Data Clean Rooms and Amazon Marketing Cloud were named in the same answer forty-four times, of the 158 answers naming Snowflake Data Clean Rooms and the 62 naming Amazon Marketing Cloud. In those answers Amazon Marketing Cloud took the first choice nine times and Snowflake Data Clean Rooms ten.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.1 Fast | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | AM | |||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | AM | AM | ||||
| GPT-6 Luna | AM | |||||
| Muse Glimmer 30B | 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.
“Usage-based through Snowflake consumption; costs scale with data volume” Kimi K2 · budget prompt · hard negative
“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
“Snowflake and AWS Clean Rooms get reasonable marks when properly configured — but watch for the cost/complexity pitfalls” DeepSeek V4 Flash · negative prompt · soft negative
“Choose Snowflake, Databricks, or AWS if you already have a mature data team, store most of your data in these warehouses” Gemini 3.5 Flash · comparative prompt · first choice
“Start with Snowflake Secure Data Sharing/Clean Rooms. It offers the highest probability of successful collaborations” Qwen 3.7 Flash · paraphrase prompt · first choice
“Choose Snowflake if you are a data-led organization that needs to run complex SQL queries, ensure strict data governance” GLM 4.7 FlashX · comparative 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.
“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
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.