One of fourteen models named Databricks Clean Rooms first on the direct prompt; zero named LiveRamp Clean Room. Databricks Clean Rooms was named by thirteen of the fourteen models and LiveRamp Clean Room by thirteen and Databricks Clean Rooms carries 26 labels and LiveRamp Clean Room 35, so the shares are not directly comparable.
By Databricks, San Francisco, United States, founded 2013. Named in one category this edition.
By LiveRamp, San Francisco, United States, founded 2011. 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 November 2026 Edition, Databricks Clean Rooms and LiveRamp Clean Room were named in the same answer forty-one times, of the 74 answers naming Databricks Clean Rooms and the 101 naming LiveRamp Clean Room. In those answers LiveRamp Clean Room took the first choice one time and Databricks Clean Rooms two.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
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 seven in this category shown.
“Databricks requires data to live in Databricks storage or an accessible cloud bucket, which can add setup complexity” GPT-5.4 mini · budget prompt · soft negative
“Strong for advanced analytics if you're in that ecosystem, but steeper learning curve for mid-market teams.” Grok 4.20 · direct prompt · soft negative
“more accessible than heavy engineering-heavy options like raw Databricks or AWS Clean Rooms” Grok 4.20 · paraphrase prompt · soft negative
“Databricks Clean Rooms is generally the most flexible for mid-size companies needing cross-cloud, privacy-policy governed collaboration with full audit logs.” Muse Glimmer 30B · paraphrase prompt · first choice
“Prefer neutral, interoperable platforms (like Snowflake, Databricks, AWS Clean Rooms, InfoSum, or independent providers)” DeepSeek V4 Flash · negative prompt · first choice
“start with your primary Cloud Data Warehouse's native DCR (typically Snowflake DCR or Databricks Clean Rooms)” Gemini 3.5 Flash · direct 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.
“Avoid Standalone SaaS Clean Rooms Initially... the $50k–$100k+ annual licensing fee for neutral enterprise tools (e.g., LiveRamp, InfoSum) won't deliver a positive ROI.” Gemini 3.5 Flash · budget prompt · hard negative
“What to Avoid - LiveRamp / Snowflake Data Clean Rooms: These are powerful but enterprise-grade in pricing and complexity.” DeepSeek V4 Flash · paraphrase prompt · hard negative
“Why Avoid Traditional Vendors Right Now? ... these are likely out of budget for a limited-fund operation.” Qwen 3.7 Flash · budget prompt · hard negative
“LiveRamp and OneTrust are the top recommendations due to their strong privacy safeguards, scalability, and industry adoption” Mistral Small · paraphrase prompt · first choice
“Top Recommendation: LiveRamp ... It is the industry standard for identity resolution and secure data exchange.” Qwen 3.7 Flash · paraphrase prompt · first choice
“For broad collaboration across ecosystems: LiveRamp or InfoSum.” Grok 4.20 · comparative 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.