Data warehouse and reverse ETL for marketing for enterprise buyers
Asked as “marketing data warehouse and reverse ETL stack”, and as “reverse ETL and warehouse setup for a marketing team”, on behalf of an enterprise B2B company. 93 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
43%
Clear leader
43Hightouch29Snowflake13Census15others
43% of first choices, clear leader.
Since September 2026▼−1Since September 2026: 44% → 43%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.Hightouch held the lead, −1 point on 44%, inside the 11-point floor.
By buyer segment
The same question asked on behalf of a different buyer. Each standing is computed within its segment; they sit side by side and are never added together.
Small business
Hightouch41%Clear leader▲+1Since September 2026: 39% → 40%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketHightouch40%Contested▼−4Since September 2026: 42% → 38%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →EnterpriseHightouch43%Clear leader▼−1Since September 2026: 44% → 43%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.This page
Hightouch41%Clear leader▲+1Since September 2026: 39% → 40%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →Mid-marketHightouch40%Contested▼−4Since September 2026: 42% → 38%, −4 points. Inside the 11-point floor: within noise. Read over the models both editions asked.Standing →EnterpriseHightouch43%Clear leader▼−1Since September 2026: 44% → 43%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageThe standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for an enterprise B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01Hightouch43%12%76endorsed leader▼−1Since September 2026: 44% → 43%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.44% → 43%
Hightouch43%12%76endorsed leader▼−1Since September 2026: 44% → 43%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.44% → 43%02
Snowflake29%13%71accepted challenger▲+1Since September 2026: 30% → 30%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.30% → 30%
Snowflake29%13%71accepted challenger▲+1Since September 2026: 30% → 30%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.30% → 30%03
Census13%19%70accepted challenger=heldSince September 2026: 10% → 10%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 10%
Census13%19%70accepted challenger=heldSince September 2026: 10% → 10%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.10% → 10%04
BigQuery Lab in the set6%9%69accepted challenger▼−5Since September 2026: 11% → 6%, −5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.11% → 6%
BigQuery Lab in the set6%9%69accepted challenger▼−5Since September 2026: 11% → 6%, −5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.11% → 6%05
Polytomic2%5%20accepted challenger▲+3Since September 2026: 0% → 3%, +3 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 3%
Polytomic2%5%20accepted challenger▲+3Since September 2026: 0% → 3%, +3 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 3%06
Databricks SQL Warehouse1%10%41accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%
Databricks SQL Warehouse1%10%41accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%07
RudderStack Reverse ETL1%17%30accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%
RudderStack Reverse ETL1%17%30accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%08
Amazon Redshift1%25%36criticized challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%
Amazon Redshift1%25%36criticized challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%Show the five products at 0%, ordered by negative rate
13
Segment0%50%10criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Segment0%50%10criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%11
Airbyte0%9%23accepted challenger▼−1Since September 2026: 1% → 0%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.1% → 0%
Airbyte0%9%23accepted challenger▼−1Since September 2026: 1% → 0%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.1% → 0%09
Fivetran0%8%40accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Fivetran0%8%40accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%12
Improvado0%7%15accepted challenger▼−1Since September 2026: 1% → 0%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.1% → 0%
Improvado0%7%15accepted challenger▼−1Since September 2026: 1% → 0%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.1% → 0%10
dbt0%0%26accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
dbt0%0%26accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured
Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.
BigQuery is made by Google, whose model Gemini 3.5 Flash is in the set. On the four share prompts that model made it the first choice one time of 4; the other thirteen models five times of 52. Lab treatment is defined on the method page; the row is marked, not excluded.
All fifteen head-to-head pages: the top six products, each against each
Hightouch vs RudderStack Reverse ETLHightouch vs AirbyteBigQuery vs SnowflakeBigQuery vs CensusBigQuery vs RudderStack Reverse ETLBigQuery vs AirbyteSnowflake vs CensusSnowflake vs RudderStack Reverse ETLSnowflake vs AirbyteCensus vs RudderStack Reverse ETLCensus vs AirbyteRudderStack Reverse ETL vs Airbyte
Recommended versus criticized
Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.
Criticized challengerCriticized default
Negative label rate →
01020304050607080910111213Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Hightouch43%
Hightouch43%02Snowflake29%
Snowflake29%03Census13%
Census13%04BigQuery6%
BigQuery6%05Polytomic2%
Polytomic2%06Databricks SQL Warehouse1%
Databricks SQL Warehouse1%07RudderStack Reverse ETL1%
RudderStack Reverse ETL1%08Amazon Redshift1%
Amazon Redshift1%09Fivetran0%
Fivetran0%10dbt0%
dbt0%11Airbyte0%
Airbyte0%12Improvado0%
Improvado0%13Segment0%
Segment0%What they warned about
Five of fourteen models held their first choice under the paraphrase. GPT-5.4 mini, Gemini 3.5 Flash, Perplexity Sonar, Mistral Small, Qwen 3.7 Flash, Kimi K2, MiniMax M2.5, GPT-6 Luna and Muse Glimmer 30B changed. A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.
Census19%
13 of 70 labels negative · 10 of 14 models · 2 hard negative
“Ones to **Avoid** (Better for SMBs): **Census (Entry/Mid-Tier Plans)** ... lacks enterprise SLAs” Grok 4.1 Fast, negative prompt
Snowflake13%
9 of 71 labels negative · 9 of 14 models · 1 hard negative
“**Avoid**: Snowflake uses consumption-based pricing... Many companies report bills 200-300% higher than budgeted.” Claude Haiku 4.5, budget prompt
Amazon Redshift25%
9 of 36 labels negative · 7 of 14 models · 4 hard negative
“took approximately 574 minutes – over 9 hours – to complete the query, which is clearly unacceptable” Muse Glimmer 30B, negative prompt
Hightouch12%
9 of 76 labels negative · 6 of 14 models
“Enterprise platforms like Hightouch and Census offer 200+ destinations but require SQL expertise and dedicated engineering support to maintain” Claude Haiku 4.5, scale prompt
What they cite
Citations exist only for the models that return a source list: fourteen of the fourteen in this edition, and all six flagship models on the expanded tier.
Sites the answers cite
77 of 84 answers in this category came back with a source list, from 14 of 14 models: citations where the model returns them, or the search results it consulted. 1278 links across 301 sites, every framing counted. Ranked by the number of answers carrying the site or page. 27 of the 252 answers across every segment cited this index's own page for the category; the method page measures whether that reading tilts an answer.
vendor site · Improvado55 answers · 105 citations · 12 models
vendor site · Hightouch46 answers · 65 citations · 14 models
vendor site · Guideflow36 answers · 36 citations · 9 models
vendor site · Integrate23 answers · 35 citations · 10 models
vendor site · LeadJourney23 answers · 23 citations · 8 models
vendor site · Funnel22 answers · 33 citations · 10 models
vendor site · Supermetrics21 answers · 25 citations · 10 models
21 answers · 23 citations · 10 models
vendor site · Basedash21 answers · 21 citations · 10 models
vendor site · Skyvia20 answers · 22 citations · 9 models
19 answers · 20 citations · 9 models
18 answers · 20 citations · 11 models
Pages the answers cite
The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.
37 answers · 12 models
36 answers · 9 models
23 answers · 8 models
20 answers · 10 models
19 answers · 9 models
16 answers · 8 models
15 answers · 10 models
15 answers · 8 models
14 answers · 8 models
14 answers · 9 models
Search against answers
Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best marketing data warehouse and reverse etl stack”, “marketing data warehouse and reverse etl stack”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.
| Company and products | Share | Own site cited | Name searches, Google | AI search demand, est. | Organic visits | Paid search a month, est. | Google ads, last 30 days | Best Google position |
|---|---|---|---|---|---|---|---|---|
| Hightouch hightouch.com | 43% | 363 | 12,100 | 434 | 127,772 | 23 | ||
| Snowflake snowflake.com | 29% | 247 | 368,000 | 37,077 | 718,749 | 118 | ||
| Fivetran fivetran.com Census 13%, Fivetran 0% | 13% | 224 | 60,500 | 14,424 | 41,501 company | $126 | 97 | |
| Google cloud.google.com BigQuery 6%, Looker 0% | 6% | 248 | 22,200 | 2,048 | 7,405,347 | |||
| Polytomic polytomic.com | 2% | 50 | 590 | 16 | 882 | 1 | ||
| Databricks Lakehouse databricks.com Databricks SQL Warehouse 1%, Databricks Lakehouse 0% | 1% | 72 | 1,000 | 225 | 689,756 | $34,664 | 120 | |
| RudderStack Reverse ETL rudderstack.com | 1% | 90 | 20 | 0 | 46,132 | 50 | 2 | |
| Amazon Web Services aws.amazon.com Amazon Redshift 1% | 1% | 128 | 368,000 | 733 | 9,238,321 | $20,476 | ||
| Integrate.io integrate.io | 1% | 15 | 110 | 2 | 53,336 | 3 | 8 | |
| Cylance/Census | 1% |
Google's top ten for the two searches hold 17 sites; 3 of them are among the sites the models cited here (improvado.io, integrate.io, skyvia.com). The first result for “best marketing data warehouse and reverse etl stack” is improvado.io.
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.ShowHide
How they answered
Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.
ShowHideModelDirect“What is the best marketing data warehouse and reverse ETL stack for an enterprise B2B company?”Paraphrase“Which reverse ETL and warehouse setup for a marketing team would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade data warehouse and reverse ETL tools for marketing teams and how do they differ?”Budget-constrained“What is the best marketing data warehouse and reverse ETL stack for a large company that needs predictable total cost across thousands of users?”Scale-constrained“We are a 5,000 person company with SSO, SOC 2 and procurement review requirements evaluating a marketing data warehouse and reverse ETL stack. What should we look at?”Negative“Which data warehouse and reverse ETL tools for marketing teams should a large enterprise avoid or be cautious about?”
Claude Haiku 4.5HightouchHightouchHeldHightouchagainst: RudderStack Reverse ETLAmazon Redshift, BigQuery, Weldagainst: Hightouch, Snowflakeno first choiceagainst: Census, Hightouchagainst: Grouparoo, Informatica, Polytomic
Eight alternatives
Amazon Redshift, BigQuery, Census, Databricks SQL Warehouse, Fivetran, Polytomic, Snowflake, dbtFive alternatives
Amazon Redshift, BigQuery, Census, Fivetran, SnowflakeSix alternatives
Census, DinMo, Fivetran, Improvado, Omnata, PolytomicTwo alternatives
Census, FivetranGPT-5.4 miniHightouch, SnowflakeHightouchChangedHightouch, SnowflakeHightouch, SnowflakeCensus, Hightouch, Snowflakeagainst: Microsoft Fabric
Seven alternatives
Airbyte, BigQuery, Census, Databricks SQL Warehouse, Fivetran, Segment, dbtOne alternative
SnowflakeThree alternatives
BigQuery, Census, Databricks SQL WarehouseTwo alternatives
BigQuery, Databricks SQL WarehouseThree alternatives
BigQuery, Databricks SQL Warehouse, FivetranGemini 3.5 FlashHightouch, Snowflakeagainst: Segment, TealiumCensus, HightouchChangedagainst: Salesforce Data Cloud, SegmentHightouch, SnowflakeBigQuery, Polytomicagainst: Census, Hightouch, Snowflakeno first choiceagainst: Adobe Real-Time CDP, Airbyte, Amazon Redshift, Census, Databricks SQL Warehouse, Hightouch, Make, Oracle Exadata, RudderStack Reverse ETL, Salesforce Data Cloud, Segment, Teradata, Workato, Zapier
Seven alternatives
BigQuery, Census, Databricks SQL Warehouse, Fivetran, Funnel, Improvado, dbtFour alternatives
BigQuery, Fivetran, Snowflake, dbtFour alternatives
BigQuery, Census, Databricks SQL Warehouse, RudderStack Reverse ETLOne alternative
Integrate.ioPerplexity SonarCensus, Hightouch, SnowflakeHightouchChangedagainst: WeldBigQuery, Hightouch, SnowflakeBigQueryno first choiceagainst: Adobe Real-Time CDP, AgencyAnalytics, Clarisights, DashThis, Databox, Fivetran, Funnel, Looker Studio, Salesforce Data Cloud, Supermetrics, Whatagraph
Three alternatives
BigQuery, Fivetran, dbtThree alternatives
Fivetran, Snowflake, dbtThree alternatives
Funnel, Improvado, SupermetricsFive alternatives
Funnel, Hightouch, Improvado, Snowflake, SupermetricsGrok 4.1 FastHightouch, Snowflakeagainst: Amazon Redshift, Databricks SQL WarehouseHightouch, SnowflakeHeldagainst: Databricks SQL Warehouse, RudderStack Reverse ETLHightouch, SnowflakeCensus, Snowflakeagainst: HightouchCensus, Hightouch, Snowflakeagainst: Amazon Redshift, Cometlyagainst: Amazon RDS Postgres, Amazon Redshift, Census, Custom Airflow/Dbt + Scripts, Fivetran, Hightouch, Oracle Exadata, Teradata, Traditional Hadoop/Hive
Six alternatives
Airbyte, BigQuery, Census, Fivetran, Stitch, dbtFive alternatives
BigQuery, Census, Fivetran, Looker, dbtFour alternatives
Amazon Redshift, BigQuery, Census, RiveryOne alternative
BigQueryFour alternatives
BigQuery, Databricks SQL Warehouse, Fivetran, dbtMistral SmallHightouch, SnowflakeCensus, SnowflakeChangedBigQuery, HightouchHightouch, Integrate.ioBigQuery, Snowflakeagainst: Census, ClickHouse, DuckDB
One alternative
CensusFive alternatives
Airbyte, BigQuery, Fivetran, Hightouch, dbtEight alternatives
Airbyte, Amazon Redshift, Census, Databricks SQL Warehouse, Fivetran, Microsoft Azure Synapse, Polytomic, SnowflakeTwo alternatives
BigQuery, SnowflakeThree alternatives
Census, Hightouch, SegmentDeepSeek V4 FlashHightouch, SnowflakeHightouch, SnowflakeHeldagainst: Census, Segment, TealiumHightouch, Snowflakeagainst: SegmentBigQuery, Hightouchagainst: SnowflakeCensus, Hightouch, Snowflakeagainst: Amazon Redshift, Census, Hightouch, Oracle Autonomous Data Warehouse, PostgreSQL used as a warehouse, Teradata
Six alternatives
Amazon Redshift, BigQuery, Census, Fivetran, RudderStack Reverse ETL, dbtThree alternatives
BigQuery, Fivetran, dbtFive alternatives
Amazon Redshift, BigQuery, Census, Databricks SQL Warehouse, RudderStack Reverse ETLFour alternatives
Airbyte, Amazon Redshift, Census, FivetranOne alternative
BigQueryLlama 4 Maverickno first choiceno first choiceHeldCensus, Hightouchno first choiceno first choicenothing named
One alternative
FivetranQwen 3.7 FlashHightouch, RudderStack Reverse ETL, SnowflakeHightouchChangedHightouch, SnowflakePolytomic, Snowflakeagainst: BigQuery, Census, HightouchCylance/Census, Hightouchagainst: Customer.io, Iterableagainst: Amazon Redshift, BigQuery, Extradata, Fivetran Reverse ETL, Snowflake
Six alternatives
Airbyte, BigQuery, Census, Databricks SQL Warehouse, Fivetran, dbtFour alternatives
Census, Fivetran, Snowflake, dbtFour alternatives
Airbyte, BigQuery, Census, Databricks SQL WarehouseOne alternative
dbtOne alternative
AirbyteKimi K2Census, Hightouch, SnowflakeHightouchChangedagainst: Databricks SQL WarehouseBigQuery, Census, Hightouch, SnowflakeDatabricks SQL Warehouse, Hightouchagainst: SnowflakeHightouch, Snowflakeagainst: Censusagainst: BigQuery, Census, Hightouch, PostgreSQL-based warehouses, Teradata
Five alternatives
Airbyte, BigQuery, Databricks SQL Warehouse, Fivetran, Microsoft FabricFour alternatives
Census, Omnata, RudderStack Reverse ETL, SnowflakeFour alternatives
Amazon Redshift, Azure Synapse, Databricks SQL Warehouse, RudderStack Reverse ETLThree alternatives
Census, Funnel, ImprovadoFour alternatives
BigQuery, Databricks SQL Warehouse, RudderStack Reverse ETL, dbtGLM 4.7 FlashXHightouch, SnowflakeHightouch, SnowflakeHeldagainst: FivetranCensus, Hightouch, SnowflakeHightouch, SnowflakeHightouchagainst: Amazon Redshift, BigQuery, Snowflake
Four alternatives
BigQuery, Census, Fivetran, dbtFour alternatives
BigQuery, Census, Databricks SQL Warehouse, dbtThree alternatives
AWS Redshift, BigQuery, RiveryFive alternatives
Amazon Redshift, BigQuery, Census, Fivetran, dbtThree alternatives
Census, Polytomic, RudderStack Reverse ETLMiniMax M2.5Hightouch, SnowflakeHightouchChangedBigQuery, Hightouch, SnowflakeHightouch, Snowflakeagainst: CensusHightouchagainst: Improvadoagainst: Amazon Redshift, BigQuery, Informatica, Snowflake
Six alternatives
Airbyte, Amazon Redshift, BigQuery, Census, Fivetran, RudderStack Reverse ETLFive alternatives
BigQuery, Census, RudderStack Reverse ETL, Segment, SnowflakeSeven alternatives
Amazon Redshift, Azure Synapse Analytics, Census, Databricks SQL Warehouse, Fivetran, Improvado, PolytomicOne alternative
BigQueryFour alternatives
Amazon Redshift, BigQuery, Census, SnowflakeGPT-6 LunaCensus, HightouchHightouchChangedno first choiceBigQuery, Hightouchagainst: SnowflakeCensus, Hightouchagainst: Airbyte, Census, RudderStack Reverse ETL
Five alternatives
BigQuery, Databricks SQL Warehouse, Fivetran, Snowflake, dbtSix alternatives
BigQuery, Census, Databricks SQL Warehouse, Fivetran, Snowflake, dbtEight alternatives
Amazon Redshift, BigQuery, Census, Databricks Lakehouse, Hightouch, Microsoft Fabric Warehouse, RudderStack Reverse ETL, SnowflakeThree alternatives
Activations, Fivetran, dbtMuse Glimmer 30BHightouch, SnowflakeCensus, SnowflakeChangedBigQuery, Hightouch, SnowflakeHightouchCensus, Snowflakeagainst: RudderStack Reverse ETLagainst: Amazon Redshift, Azure Synapse, BigQuery, Census, Snowflake
Five alternatives
Airbyte, BigQuery, Census, Fivetran, RudderStack Reverse ETLThree alternatives
BigQuery, Hightouch, OmnataThree alternatives
Census, Databricks Lakehouse, RudderStack Reverse ETLFour alternatives
BigQuery, Census, Databricks SQL Warehouse, SnowflakeThree alternatives
BigQuery, Hightouch, OmnataBold is the first choiceAlternatives are counted; the count opens them.What the answer argued against
The record
One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency. Full answer text is in the free responses file. Download the record
Eighty-four rows: every prompt, every model, every answer.
| Prompt | Model | Version string | Time (UTC) | Searched | Sources | Latency |
|---|---|---|---|---|---|---|
| Direct recommendation | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:08 | yes | 16 | 11 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 07:58 | yes | 4 | 6 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 10:55 | yes | 23 | 26 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 13:47 | yes | 20 | 5 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:48 | yes | 25 | 13 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 07:54 | yes | 10 | 4 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:47 | yes | 24 | 39 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 11:57 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:24 | no | 0 | 47 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 11:24 | yes | 24 | 28 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:48 | yes | 23 | 45 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 08:56 | yes | 10 | 28 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 10:34 | yes | 6 | 29 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:35 | yes | 16 | 44 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 11:57 | yes | 28 | 12 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:05 | yes | 5 | 9 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:04 | yes | 8 | 28 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 07:59 | yes | 12 | 6 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:59 | yes | 25 | 9 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:49 | no | 0 | 9 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 07:56 | yes | 22 | 36 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 10:28 | yes | 5 | 2 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 08:40 | no | 0 | 34 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:08 | yes | 20 | 29 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:20 | yes | 24 | 69 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:04 | yes | 5 | 17 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 12:12 | yes | 3 | 22 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:11 | yes | 14 | 24 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:27 | yes | 14 | 9 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:30 | yes | 4 | 8 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 07:57 | yes | 23 | 35 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 09:06 | yes | 19 | 4 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:13 | yes | 23 | 13 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 08:16 | yes | 14 | 8 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 12:58 | yes | 25 | 119 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 09:22 | yes | 5 | 4 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:26 | no | 0 | 36 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:05 | yes | 23 | 31 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 07:39 | yes | 18 | 19 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:34 | yes | 25 | 55 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:56 | yes | 10 | 39 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 08:49 | yes | 25 | 49 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:31 | yes | 26 | 14 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:05 | yes | 5 | 6 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:31 | yes | 23 | 32 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 10:36 | yes | 19 | 3 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:59 | yes | 23 | 12 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:44 | yes | 10 | 5 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:34 | yes | 25 | 51 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:34 | yes | 5 | 1 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:41 | yes | 13 | 57 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 08:00 | yes | 25 | 32 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 13:49 | yes | 24 | 24 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:37 | yes | 10 | 37 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:11 | yes | 4 | 30 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 07:42 | yes | 22 | 41 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:18 | yes | 37 | 18 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 12:08 | yes | 1 | 10 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:48 | no | 0 | 26 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 09:17 | yes | 18 | 5 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:14 | yes | 22 | 10 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:16 | no | 0 | 8 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 10:05 | yes | 24 | 63 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 11:37 | yes | 5 | 2 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 10:53 | no | 0 | 32 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 12:33 | yes | 25 | 47 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:15 | yes | 24 | 38 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:11 | yes | 19 | 28 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:04 | yes | 4 | 34 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:51 | yes | 15 | 28 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 13:09 | yes | 31 | 16 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:48 | yes | 4 | 7 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 13:01 | yes | 21 | 40 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 11:55 | yes | 20 | 4 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:19 | yes | 19 | 10 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 10:35 | yes | 13 | 8 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 13:41 | yes | 25 | 64 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:44 | yes | 5 | 1 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:56 | yes | 20 | 55 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 13:20 | yes | 24 | 40 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:36 | yes | 22 | 72 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 13:30 | yes | 14 | 28 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 14:14 | yes | 6 | 29 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 14:01 | yes | 21 | 33 s |
Normalization in this category
Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.
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Category-scoped readings
Databricks read as Databricks SQL Warehouse
Databricks (Delta Lake) read as Databricks SQL Warehouse
Databricks (Lakehouse) read as Databricks SQL Warehouse
RudderStack read as RudderStack Reverse ETL
Unresolved, counted raw
Activations (the Census product under Fivetran)
Amazon RDS Postgres
Clarisights
Custom Airflow/Dbt + Scripts
Cylance/Census
DashThis
Dataslayer
Extradata
Firebolt
Funnel's Data Hub
Improve/Talisman
Microsoft Fabric Warehouse
PostgreSQL used as a warehouse
PostgreSQL-based warehouses
Traditional Hadoop/Hive
Discontinued, still offered
Muse Glimmer 30B named Grouparoo as mention on the direct prompt. Open source reverse ETL, shut down 2023.
Muse Glimmer 30B named Grouparoo as mention on the paraphrase prompt. Open source reverse ETL, shut down 2023.
Muse Glimmer 30B named Grouparoo as mention on the scale prompt. Open source reverse ETL, shut down 2023.
Mistral Small named Grouparoo as mention on the scale prompt. Open source reverse ETL, shut down 2023.
GLM 4.7 FlashX named Grouparoo as mention on the scale prompt. Open source reverse ETL, shut down 2023.