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Data clean rooms · October 2026 Edition

InfoSum vs Databricks Clean Rooms

One of fourteen models named InfoSum first on the direct prompt; one named Databricks Clean Rooms. InfoSum was named by fourteen of the fourteen models and Databricks Clean Rooms by twelve and InfoSum carries 39 labels and Databricks Clean Rooms 27, so the shares are not directly comparable.

InfoSum

accepted challenger

Named in three categories this edition.

Databricks Clean Rooms

accepted challenger

By Databricks, San Francisco, United States, founded 2013. Named in one category this edition.

First-choice share10%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate23%15%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 10; printed, not drawn.
Labels3927A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, InfoSum 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 InfoSum · Snowflake Data Clean Rooms vs Databricks Clean Rooms · AWS Clean Rooms vs InfoSum

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.
InfoSumFirst choices, of fourteen modelsDatabricks Clean Rooms
Direct113 against InfoSum · 1 against Databricks Clean Rooms
Paraphrase501 against InfoSum
Comparative011 against InfoSum
Budget-constrained004 against InfoSum
Scale-constrained00
Negative003 against Databricks 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, InfoSum and Databricks Clean Rooms were named in the same answer forty-six times, of the 119 answers naming InfoSum and the 73 naming Databricks Clean Rooms. In those answers Databricks Clean Rooms took the first choice one time and InfoSum one.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where InfoSum and Databricks Clean Rooms stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Qwen 3.7 Flash
Kimi K2
GLM 4.7 FlashX
MiniMax M2.5
GPT-6 Luna
Muse Glimmer 30B
InfoSum Databricks Clean Rooms first choice named as an alternative argued againstblank: not namedEach cell is one answer, InfoSum on the left and Databricks Clean Rooms on the right.

The direct prompt

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

InfoSum first, Databricks Clean Rooms not the choice

1 of 14 modelsDatabricks Clean Rooms was named in the answer but not as the choice, or not at all.
Mistral SmallInfoSum alternatives: Decentriq, Habu, LiveRamp Clean Room

Databricks Clean Rooms first, InfoSum not the choice

1 of 14 modelsInfoSum was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Databricks Clean Rooms, LiveRamp Safe Haven, Snowflake Data Clean Rooms alternatives: Apollo.io

Neither was the first choice, one was named

3 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniAWS Clean Rooms alternatives: Acxiom, Databricks Clean Rooms, Snowflake Data Clean Rooms
Perplexity SonarSnowflake Data Clean Rooms alternatives: AWS Clean Rooms, InfoSum, LiveRamp Clean Room
Grok 4.1 FastAWS Clean Rooms, Snowflake Data Clean Rooms alternatives: Decentriq, InfoSum, LiveRamp Clean Room

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Gemini 3.5 FlashCrossbeam alternatives: Amazon Marketing Cloud, Google Ads Data Hub, Salesforce Data Cloud DCR, Snowflake Data Clean Rooms
DeepSeek V4 FlashOptable alternatives: AWS Clean Rooms, 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
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
MiniMax M2.5Amazon Marketing Cloud alternatives: Deciq
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

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
Level: the same share of first choices.
InfoSum0%#11 of 12
Databricks Clean Rooms0%#10 of 12
The full small business standing →
Mid-marketThe figures above
InfoSum leads by eight points.
InfoSum10%#4 of 10
Databricks Clean Rooms2%#7 of 10
The full mid-market standing →
Enterprise
InfoSum leads by eleven points.
InfoSum11%#3 of 9
Databricks Clean Rooms0%#7 of 9
The full enterprise standing →

What the models said about InfoSum

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 enterprise SaaS (e.g., LiveRamp, InfoSum) at $500+/month unless trialing.” Grok 4.1 Fast · budget prompt · hard negative
“pricing and complexity are often overkill for mid-market needs” DeepSeek V4 Flash · direct prompt · hard negative
“like LiveRamp (Habu), InfoSum, or Decentriq is rarely feasible, as licensing fees for these platforms often run in the tens or hundreds of thousands of dollars annually” Gemini 3.5 Flash · budget prompt · soft negative
“InfoSum – Praised for its flexible collaboration capabilities and suitability for mid-market needs, without requiring enterprise-scale resources.” Mistral Small · direct prompt · first choice
“I would recommend InfoSum as a privacy-safe data collaboration platform for a mid-sized B2B company.” Llama 4 Maverick · paraphrase prompt · first choice
“I would recommend InfoSum if your priority is privacy-safe data collaboration with partners” Perplexity Sonar · paraphrase prompt · first choice

What the models said about Databricks Clean Rooms

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
Also compared

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.