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Data clean rooms · October 2026 Edition

AWS Clean Rooms vs Habu

Two of fourteen models named AWS Clean Rooms first on the direct prompt; zero named Habu. AWS Clean Rooms was named by twelve of the fourteen models and Habu by seven and AWS Clean Rooms carries 42 labels and Habu 14, so the shares are not directly comparable.

AWS Clean Rooms

accepted challenger

Named in one category this edition.

Habu

accepted challenger

Named in two categories this edition.

First-choice share15%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate7%14%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#8A position in a field of 10; printed, not drawn.
Labels4214A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, AWS Clean Rooms reading right to left. Rank and label count are printed, not drawn.Snowflake Data Clean Rooms was named alongside these two in eleven of the fourteen direct answers. Snowflake Data Clean Rooms vs AWS Clean Rooms · Snowflake Data Clean Rooms vs Habu · AWS Clean Rooms vs Amazon Marketing Cloud

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.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
AWS Clean RoomsFirst choices, of fourteen modelsHabu
Direct201 against Habu
Paraphrase21
Comparative20
Budget-constrained501 against AWS Clean Rooms
Scale-constrained001 against Habu
Negative002 against AWS Clean Rooms
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Across every category in the October 2026 Edition, AWS Clean Rooms and Habu were named in the same answer thirty-four times, of the 142 answers naming AWS Clean Rooms and the 52 naming Habu. In those answers Habu took the first choice two times and AWS Clean Rooms seven.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where AWS Clean Rooms and Habu stood in it.
ModelDirectACHAParaphraseACHAComparativeACHABudget-constrainedACHAScale-constrainedACHANegativeACHA
Claude Haiku 4.5
GPT-5.4 miniACACACHAAC
Gemini 3.5 FlashHAACACACHAHA
Perplexity SonarACACACAC
Grok 4.1 FastACACACACAC
Mistral SmallHAACHAAC
DeepSeek V4 FlashACHAACHAACACACHA
Llama 4 Maverick
Qwen 3.7 FlashACAC
Kimi K2HAACHAAC
GLM 4.7 FlashXACHAACAC
MiniMax M2.5ACAC
GPT-6 LunaACACACAC
Muse Glimmer 30BACAC
AC AWS Clean RoomsHA HabuAC first choiceAC named as an alternativeAC argued againstblank: not namedEach cell is one answer, AWS Clean Rooms on the left and Habu on the right.

The direct prompt

The plain question, one answer per model, grouped by where AWS Clean Rooms and Habu stood in it.

AWS Clean Rooms first, Habu not the choice

2 of 14 modelsHabu was named in the answer but not as the choice, or not at all.
GPT-5.4 miniAWS Clean Rooms alternatives: Acxiom, Databricks Clean Rooms, Snowflake Data Clean Rooms
Grok 4.1 FastAWS Clean Rooms, Snowflake Data Clean Rooms alternatives: Decentriq, InfoSum, LiveRamp Clean Room

Neither was the first choice, one was named

7 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Perplexity SonarSnowflake Data Clean Rooms alternatives: AWS Clean Rooms, InfoSum, LiveRamp Clean Room
Mistral SmallInfoSum alternatives: Decentriq, Habu, LiveRamp Clean Room
DeepSeek V4 FlashOptable alternatives: AWS Clean Rooms, Snowflake Data Clean Rooms
Kimi K2Optable alternatives: Habu, Snowflake Data Clean Rooms
GLM 4.7 FlashXSnowflake Data Clean Rooms alternatives: AWS Clean Rooms, Amazon Marketing Cloud, Google Ads Data Hub, Habu, LiveRamp Relate
GPT-6 LunaSnowflake Data Clean Rooms alternatives: AWS Clean Rooms, BigQuery Data Clean Rooms, LiveRamp Clean Room
Muse Glimmer 30BSnowflake Data Clean Rooms alternatives: AWS Clean Rooms, Amazon Marketing Cloud, Google Ads Data Hub, LiveRamp Data Collaboration Platform powered by Habu

Neither was named

5 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Databricks Clean Rooms, LiveRamp Safe Haven, Snowflake Data Clean Rooms alternatives: Apollo.io
Gemini 3.5 FlashCrossbeam alternatives: Amazon Marketing Cloud, Google Ads Data Hub, Salesforce Data Cloud DCR, Snowflake Data Clean Rooms
Llama 4 MaverickAmazon Marketing Cloud
Qwen 3.7 FlashLiveRamp Clean Room alternatives: Google Ads, Meta Advanced Analytics, Snowflake Data Clean Rooms, The Trade Desk
MiniMax M2.5Amazon Marketing Cloud alternatives: Deciq

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.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
AWS Clean Rooms leads by twenty-six points.
AWS Clean Rooms26%#1 of 12
Habu0%#12 of 12
The full small business standing →
Mid-marketThe figures above
AWS Clean Rooms leads by fourteen points.
AWS Clean Rooms15%#2 of 10
Habu2%#8 of 10
The full mid-market standing →
Enterprise
AWS Clean Rooms leads by nine points.
AWS Clean Rooms11%#2 of 9
Habu2%#6 of 9
The full enterprise standing →

What the models said about AWS Clean Rooms

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.

“Pay-per-use (CRPU-hours); no free tier; costs can escalate quickly” 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
“AWS Clean Rooms is often the safer default because the pricing is usage-based and there's no separate license to buy just to get started” GPT-5.4 mini · budget prompt · first choice
“AWS Clean Rooms is the top recommendation for most small businesses due to its pay-as-you-go pricing and free tier” Mistral Small · budget prompt · first choice
“Already on AWS → AWS Clean Rooms pay-as-you-go is the most budget-friendly entry point, no big upfront investment.” Muse Glimmer 30B · budget prompt · first choice

What the models said about Habu

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of five in this category shown.

“What to Avoid as a Mid-Market B2B - LiveRamp / Habu: Enterprise-grade” DeepSeek V4 Flash · direct prompt · hard negative
“Only purchase a dedicated independent SaaS DCR (like LiveRamp/Habu) if you have a specific, high-revenue partner usecase lined up” Gemini 3.5 Flash · scale prompt · soft negative
“Top Recommendation: Habu (now LiveRamp Clean Room)” DeepSeek V4 Flash · paraphrase prompt · first choice
“Recognized for its user-friendly interface and ability to handle multiple partnerships and advanced analytics, ideal for mid-market B2B companies.” Mistral Small · direct prompt · alternative
“modern integrations like Habu or InfoSum) that allow partners to query and analyze data directly where it already lives” Gemini 3.5 Flash · negative prompt · alternative
Also compared
Snowflake Data Clean Rooms vs AWS Clean RoomsSnowflake Data Clean Rooms vs HabuAWS Clean Rooms vs Amazon Marketing CloudAWS Clean Rooms vs InfoSumAWS Clean Rooms vs Google Ads Data HubAWS Clean Rooms vs DecentriqAWS Clean Rooms vs Databricks Clean RoomsAmazon Marketing Cloud vs HabuInfoSum vs HabuGoogle Ads Data Hub vs HabuDecentriq vs HabuDatabricks Clean Rooms vs HabuAll twenty-eight head-to-head pages and the full data clean rooms standing

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.