Data warehouse and reverse ETL for marketing for small business buyers
Asked as “marketing data warehouse and reverse ETL stack”, and as “reverse ETL and warehouse setup for a marketing team”, on behalf of a small B2B company. 102 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.
Standing · first-choice share
41%
Clear leader
41Hightouch35BigQuery07Census17others
41% of first choices, clear leader.
Since September 2026▲+1Since September 2026: 39% → 40%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.Hightouch held the lead, +1 point on 39%, 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.This pageMid-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.Standing →
Hightouch41%Clear leader▲+1Since September 2026: 39% → 40%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.This pageMid-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.Standing →The standing
Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for a small B2B company. Ordered by share.
ProductFirst-choice shareNegative rateLabelsQuadrantSince September 2026
01Hightouch41%13%75endorsed leader▲+1Since September 2026: 39% → 40%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.39% → 40%
Hightouch41%13%75endorsed leader▲+1Since September 2026: 39% → 40%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.39% → 40%02
BigQuery Lab in the set35%12%76endorsed leader=heldSince September 2026: 35% → 35%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.35% → 35%
BigQuery Lab in the set35%12%76endorsed leader=heldSince September 2026: 35% → 35%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.35% → 35%03
Census7%28%61criticized challenger▼−6Since September 2026: 12% → 6%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.12% → 6%
Census7%28%61criticized challenger▼−6Since September 2026: 12% → 6%, −6 points. Inside the 11-point floor: within noise. Read over the models both editions asked.12% → 6%04
Snowflake7%37%68criticized challenger▲+5Since September 2026: 4% → 8%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 8%
Snowflake7%37%68criticized challenger▲+5Since September 2026: 4% → 8%, +5 points. Inside the 11-point floor: within noise. Read over the models both editions asked.4% → 8%05
RudderStack Reverse ETL2%15%33accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%
RudderStack Reverse ETL2%15%33accepted challenger=heldSince September 2026: 2% → 2%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 2%06
Airbyte1%8%38accepted challenger=heldSince September 2026: 1% → 1%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.1% → 1%
Airbyte1%8%38accepted challenger=heldSince September 2026: 1% → 1%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.1% → 1%07
Fivetran1%14%49accepted challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%
Fivetran1%14%49accepted 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
Polytomic1%0%14accepted challenger▼−1Since September 2026: 2% → 1%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 1%
Polytomic1%0%14accepted challenger▼−1Since September 2026: 2% → 1%, −1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.2% → 1%09
PostgreSQL1%25%12criticized challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%
PostgreSQL1%25%12criticized challenger▲+1Since September 2026: 0% → 1%, +1 point. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 1%10
Amazon Redshift1%49%37criticized 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%49%37criticized 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 seven products at 0%, ordered by negative rate
17
Databricks SQL Warehouse0%71%17criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Databricks SQL Warehouse0%71%17criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%16
Segment0%31%13criticized 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%31%13criticized challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%13
Improvado0%15%13accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Improvado0%15%13accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%15
Stitch0%10%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Stitch0%10%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%14
Supermetrics0%9%11accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Supermetrics0%9%11accepted 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
dbt0%5%38accepted 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%5%38accepted 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
Metabase0%0%10accepted challenger=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.0% → 0%
Metabase0%0%10accepted 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 three times of 4; the other thirteen models thirty-three 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 →
0102030405060708091011121314151617Accepted challengerEndorsed leader
0%First-choice share → · lines at 30% share and 25% negative60%
Key
01Hightouch41%
Hightouch41%02BigQuery35%
BigQuery35%03Census7%
Census7%04Snowflake7%
Snowflake7%05RudderStack Reverse ETL2%
RudderStack Reverse ETL2%06Airbyte1%
Airbyte1%07Fivetran1%
Fivetran1%08Polytomic1%
Polytomic1%09PostgreSQL1%
PostgreSQL1%10Amazon Redshift1%
Amazon Redshift1%11dbt0%
dbt0%12Metabase0%
Metabase0%13Improvado0%
Improvado0%14Supermetrics0%
Supermetrics0%15Stitch0%
Stitch0%16Segment0%
Segment0%17Databricks SQL Warehouse0%
Databricks SQL Warehouse0%What they warned about
Six of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, Gemini 3.5 Flash, Perplexity Sonar, Grok 4.1 Fast, Mistral Small, Kimi K2, MiniMax M2.5 and GPT-6 Luna 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.
Snowflake37%
25 of 68 labels negative · 12 of 14 models · 6 hard negative
“Small marketing teams should **avoid or delay adopting** enterprise-grade tools like Snowflake, Hightouch, Census, Fivetran, and dbt” Kimi K2, negative prompt
Amazon Redshift49%
18 of 37 labels negative · 10 of 14 models · 8 hard negative
“**Amazon Redshift**: Often recommended for enterprise-level teams due to complexity and maintenance requirements. Small businesses may find it overkill” Mistral Small, negative prompt
Census28%
17 of 61 labels negative · 10 of 14 models · 1 hard negative
“avoid or delay adopting** enterprise-grade tools like Snowflake, Hightouch, Census, Fivetran, and dbt” Kimi K2, negative prompt
Hightouch13%
10 of 75 labels negative · 10 of 14 models · 1 hard negative
“avoid or delay adopting** enterprise-grade tools like Snowflake, Hightouch, Census, Fivetran, and dbt” Kimi K2, negative 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. 1266 links across 243 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 · 109 citations · 12 models
vendor site · Guideflow32 answers · 32 citations · 9 models
vendor site · Hightouch31 answers · 37 citations · 13 models
vendor site · Airbyte25 answers · 26 citations · 12 models
vendor site · Skyvia23 answers · 25 citations · 9 models
vendor site · Zoho23 answers · 23 citations · 10 models
vendor site · Porter Metrics22 answers · 31 citations · 8 models
vendor site · Supermetrics22 answers · 24 citations · 9 models
vendor site · Integrate21 answers · 24 citations · 9 models
21 answers · 22 citations · 12 models
21 answers · 21 citations · 12 models
vendor site · Cometly20 answers · 36 citations · 9 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
32 answers · 9 models
23 answers · 10 models
21 answers · 11 models
20 answers · 9 models
20 answers · 7 models
17 answers · 8 models
17 answers · 10 models
16 answers · 8 models
15 answers · 7 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 | 41% | 363 | 12,100 | 434 | 127,772 | 23 | ||
| Google cloud.google.com BigQuery 35%, Looker Studio 0% | 35% | 248 | 40,500 | 452 | 7,405,347 | |||
| Fivetran fivetran.com Census 7%, Fivetran 1% | 8% | 224 | 60,500 | 14,424 | 41,501 company | $126 | 97 | |
| Snowflake snowflake.com | 7% | 247 | 368,000 | 37,077 | 718,749 | 118 | ||
| RudderStack Reverse ETL rudderstack.com | 2% | 90 | 20 | 0 | 46,132 | 50 | 2 | |
| Airbyte airbyte.com | 1% | 84 | 6,600 | 120 | 17,507 | $320 | 42 | |
| Polytomic polytomic.com | 1% | 50 | 590 | 16 | 882 | 1 | ||
| Skyvia skyvia.com | 1% | 11 | 1,300 | 15 | 33,150 | 1 | 7 | |
| Hevo hevodata.com | 1% | 24 | 720 | 29 | 16,828 | 18 | ||
| PostgreSQL postgresql.org | 1% | 17 | 74,000 | 14,143 | 465,585 |
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 a small B2B company?”Paraphrase“Which reverse ETL and warehouse setup for a marketing team would you recommend to a small business that sells to other businesses?”Comparative“What are the top data warehouse and reverse ETL tools for marketing teams for a small team and how do they differ?”Budget-constrained“What is the best marketing data warehouse and reverse ETL stack for a small company with a tight budget?”Scale-constrained“We are a 30 person company 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 small business avoid or be cautious about?”
Claude Haiku 4.5BigQueryPolytomicChangedHightouchSkyviano first choiceagainst: Astera Centerprise AI, Census, Hightouch, Integrate.io
Four alternatives
Hevo, Hightouch, Polytomic, SnowflakeFour alternatives
Hightouch, Improvado, Mozart Data, SnowflakeThree alternatives
BigQuery, Census, Mozart DataFour alternatives
Hightouch, Mozart Data, RudderStack Reverse ETL, StitchGPT-5.4 miniBigQuery, Hightouchagainst: CensusBigQuery, HightouchHeldBigQuery, Census, Hightouch, Snowflakeagainst: Databricks SQL Warehouse, PostgreSQLBigQuery, Hightouchagainst: SnowflakeBigQuery, Census, Hightouch, Snowflakeagainst: Amazon Redshift, Databricks SQL Warehouse, PostgreSQLagainst: Funnel, RudderStack Reverse ETL
Two alternatives
Snowflake, dbtTwo alternatives
Amazon Redshift, SnowflakeTwo alternatives
Fivetran, RudderStack Reverse ETLTwo alternatives
Amazon Redshift, dbtThree alternatives
Airbyte, Fivetran, dbtGemini 3.5 FlashBigQueryagainst: SnowflakeHightouchChangedBigQuery, HightouchBigQuery, HightouchBigQuery, Hightouchagainst: Amazon Redshift, Census, Databricks SQL Warehouse, Hightouch, RudderStack Reverse ETL, Segment, Self-Hosted Airbyte, Snowflake
Eight alternatives
Airbyte, Bruin, Census, Estuary Flow, Fivetran, Hightouch, MotherDuck, PolytomicTwo alternatives
BigQuery, FivetranFour alternatives
Census, MotherDuck, Polytomic, SnowflakeSix alternatives
Airbyte, Fivetran, MotherDuck, Multiwoven, Supabase, dbtEight alternatives
Airbyte, Census, Fivetran, Hevo, Improvado, Snowflake, Weld, dbtPerplexity SonarBigQuery, Hightouchagainst: Amazon Redshift, Databox, Porter MetricsHightouchChangedBigQuery, SnowflakeAirbyte, BigQueryagainst: Cometly, Supermetricsno first choiceagainst: Amazon Redshift, BigQuery, Funnel, Hightouch, Improvado, Looker, Snowflake
Three alternatives
Census, Fivetran, SupermetricsThree alternatives
BigQuery, Census, dbtFive alternatives
Fivetran, Funnel, Metabase, Mode, SupermetricsTwo alternatives
Metabase, dbtGrok 4.1 FastBigQuery, Hightouchagainst: ZapierHightouch, PostgreSQLChangedagainst: BigQuery, SnowflakeBigQuery, Hightouchagainst: Amazon Redshift, Databricks SQL Warehouse, FivetranBigQuery, Hightouchagainst: Fivetran, Panoply, Snowflake, StitchBigQuery, Hightouchagainst: Databricks SQL Warehouse, Funnel, Improvadoagainst: Amazon Redshift, Census, Fivetran, Hightouch, Segment, Snowflake
Nine alternatives
Airbyte, Census, Fivetran, Improvado, MotherDuck, Panoply, Snowflake, Supermetrics, dbtFour alternatives
Airbyte, Census, Fivetran, RudderStack Reverse ETLFour alternatives
Census, RudderStack Reverse ETL, Snowflake, dbtThree alternatives
Airbyte, RudderStack Reverse ETL, SupabaseFive alternatives
Census, Fivetran, RudderStack Reverse ETL, Snowflake, dbtMistral SmallBigQuery, Census, Hightouch, Snowflakeagainst: Fivetran, RiveryCensus, HightouchChangedagainst: Pipedream, ZapierBigQuery, Census, Hightouchagainst: Amazon Redshift, Databricks SQL WarehouseBigQuery, Hevoagainst: SnowflakeBigQuery, Snowflakeagainst: Amazon Redshift, BigQuery, Snowflake
Two alternatives
Pipedream, ZapierFive alternatives
Airbyte, BigQuery, Fivetran, Snowflake, dbtFour alternatives
Airbyte, Cometly, Snowflake, Zoho DataprepFour alternatives
Airbyte, Metabase, PostgreSQL, RiveryFour alternatives
Amazon Redshift, Census, Fivetran, dbtDeepSeek V4 FlashBigQuery, Hightouchagainst: Amazon Redshift, ClickHouse, FivetranBigQuery, HightouchHeldagainst: Amazon Redshift, SnowflakeBigQuery, HightouchBigQuery, Multiwovenagainst: Amazon Redshift, ClickHouse, SnowflakeBigQuery, Hightouchagainst: RudderStack Reverse ETL, Segment, mParticleagainst: Amazon Redshift, Census, Databricks SQL Warehouse, Hightouch, IBM Db2, RudderStack Reverse ETL, SAP HANA Cloud, Snowflake, Teradata
Six alternatives
Airbyte, Amplitude, Census, Segment, Snowflake Standard Edition, dbtTwo alternatives
Airbyte, CensusFour alternatives
Amazon Redshift, Census, RudderStack Reverse ETL, SnowflakeSix alternatives
Airbyte, DuckDB, Hightouch, Meltano, Polytomic, dltEight alternatives
Census, Fivetran, Lightdash, Metabase, Microsoft Power BI, Snowflake, Supermetrics, dbtLlama 4 Maverickno first choiceno first choiceHeldno first choiceRudderStack Reverse ETLHightouchnothing named
Three alternatives
BigQuery, Databricks SQL Warehouse, SnowflakeThree alternatives
Airbyte, Hevo, HightouchThree alternatives
Census, Improvado, RudderStack Reverse ETLQwen 3.7 FlashBigQuery, HightouchBigQuery, HightouchHeldagainst: Airbyte, Segment, mParticleBigQuery, Funnel, Improvado, Polytomic, RudderStack Reverse ETLagainst: CensusAmazon Redshift, BigQuery, Hightouchagainst: Fivetran, SnowflakeFivetran, Hightouch, Snowflakeagainst: Airbyte, Databricks SQL Warehouseagainst: Airbyte, Amazon Redshift, BigQuery, Census, Hightouch, MariaDB, Raw PostgreSQL, Snowflake, Symmetric, dbt
Six alternatives
Airbyte, Astile, Census, Fivetran, Snowflake, dbtThree alternatives
Fivetran, Snowflake, dbtFour alternatives
Fivetran, Hightouch, Snowflake, SupermetricsThree alternatives
Airbyte, RudderStack Reverse ETL, dbtFour alternatives
BigQuery, Extrata, RudderStack Reverse ETL, dbtKimi K2BigQuery, Hightouchagainst: SnowflakeHightouchChangedagainst: Census, SnowflakeBigQuery, HightouchBigQuery, Hightouchagainst: Census, SnowflakeBigQuery, Hightouchagainst: Amazon Redshift, RudderStack Reverse ETL, self-hosted Postgresagainst: Amazon Redshift, BigQuery, Census, Fivetran, Hightouch, Snowflake, dbt
Seven alternatives
Airbyte, Fivetran, Hevo, PostgreSQL, RudderStack Reverse ETL, Skyvia, dbtTwo alternatives
Amazon Redshift, BigQueryFive alternatives
Amazon Redshift, Census, RudderStack Reverse ETL, Snowflake, WeldThree alternatives
Airbyte, RudderStack Reverse ETL, dbtEight alternatives
Airbyte, Census, Fivetran, Improvado, Looker Studio, Metabase, Snowflake, dbtGLM 4.7 FlashXHightouchHightouchHeldBigQuery, HightouchBigQuery, HightouchBigQuery, Census, Hightouch, Snowflakeagainst: BigQuery, Census, Databricks SQL Warehouse, Hightouch, Microsoft Fabric, Oracle Autonomous Data Warehouse, Snowflake
Three alternatives
Airbyte, BigQuery, FivetranFour alternatives
BigQuery, Census, Fivetran, SnowflakeFive alternatives
Census, Fivetran Reverse ETL, PostgreSQL, Snowflake, ZoodyThree alternatives
Fivetran, Hevo, StitchTwo alternatives
Fivetran, dbtMiniMax M2.5BigQuery, HightouchHightouch, SnowflakeChangedagainst: PostgreSQLBigQuery, Hightouchagainst: Databricks SQL WarehouseBigQuery, RudderStack Reverse ETLCensus, Hightouch, Snowflakeagainst: Amazon Redshift, ClickHouse, Databricks SQL Warehouseagainst: Amazon Redshift, BigQuery, Census, Hightouch, Snowflake
Four alternatives
Airbyte, Census, Metabase, dbtFive alternatives
Airbyte, BigQuery, Census, Fivetran, dbtFive alternatives
Airbyte, Amazon Redshift, Census, Fivetran, SnowflakeSix alternatives
Airbyte, ClickHouse, Fivetran, Hightouch, Metabase, TinybirdThree alternatives
Airbyte, BigQuery, FivetranGPT-6 LunaBigQuery, HightouchHightouchChangedBigQuery, Hightouch, MotherDuckagainst: CensusBigQuery, HightouchCensus, Hightouchagainst: Amazon Redshift Provisioned, BigQuery, Census, Hightouch, Snowflake
Three alternatives
Airbyte, Dataform, FivetranTwo alternatives
BigQuery, dbtFour alternatives
Amazon Redshift, Polytomic, Snowflake, dbtTwo alternatives
Airbyte, dbtMuse Glimmer 30BBigQuery, Hightouchagainst: SnowflakeBigQuery, HightouchHeldagainst: SnowflakeBigQuery, Hightouch, SnowflakeBigQuery, HightouchBigQuery, Census, Hightouchagainst: Databricks SQL Warehouse, Snowflakeagainst: Amazon Redshift, BigQuery, Snowflake
Four alternatives
Census, Postgres, managed, RudderStack Reverse ETL, dbtTwo alternatives
Census, RudderStack Reverse ETLOne alternative
SeekwellSeven alternatives
Airbyte, DuckDB, Metabase, Multiwoven, PostgreSQL, RudderStack Reverse ETL, dbtThree alternatives
Airbyte, Fivetran, dbtBold 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 13:28 | yes | 18 | 9 s |
| Direct recommendation | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 09:26 | yes | 3 | 6 s |
| Direct recommendation | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 09:13 | yes | 11 | 25 s |
| Direct recommendation | Perplexity Sonar | sonar | 2026-10-01 11:51 | yes | 20 | 5 s |
| Direct recommendation | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 12:58 | yes | 25 | 13 s |
| Direct recommendation | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 13:53 | yes | 10 | 6 s |
| Direct recommendation | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:12 | yes | 24 | 32 s |
| Direct recommendation | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 08:02 | yes | 5 | 2 s |
| Direct recommendation | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:34 | no | 0 | 31 s |
| Direct recommendation | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 07:39 | yes | 24 | 23 s |
| Direct recommendation | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 12:21 | yes | 23 | 83 s |
| Direct recommendation | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 12:12 | yes | 18 | 27 s |
| Direct recommendation | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:16 | yes | 5 | 23 s |
| Direct recommendation | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 11:58 | yes | 23 | 39 s |
| Paraphrase | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:59 | yes | 14 | 9 s |
| Paraphrase | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 12:32 | yes | 2 | 5 s |
| Paraphrase | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:11 | yes | 7 | 24 s |
| Paraphrase | Perplexity Sonar | sonar | 2026-10-01 14:13 | yes | 14 | 4 s |
| Paraphrase | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:37 | yes | 24 | 13 s |
| Paraphrase | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 07:52 | no | 0 | 7 s |
| Paraphrase | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 13:57 | yes | 22 | 55 s |
| Paraphrase | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:04 | yes | 5 | 4 s |
| Paraphrase | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 12:32 | yes | 5 | 52 s |
| Paraphrase | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:39 | yes | 24 | 26 s |
| Paraphrase | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:48 | yes | 25 | 195 s |
| Paraphrase | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:56 | no | 0 | 57 s |
| Paraphrase | GPT-6 Luna | gpt-6-luna | 2026-10-01 11:53 | yes | 3 | 14 s |
| Paraphrase | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 10:14 | yes | 25 | 27 s |
| Comparative | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:35 | yes | 24 | 12 s |
| Comparative | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 11:50 | yes | 4 | 11 s |
| Comparative | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:30 | yes | 19 | 30 s |
| Comparative | Perplexity Sonar | sonar | 2026-10-01 12:47 | yes | 19 | 7 s |
| Comparative | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 10:14 | yes | 20 | 10 s |
| Comparative | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 13:13 | yes | 15 | 10 s |
| Comparative | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 14:11 | yes | 24 | 41 s |
| Comparative | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 11:32 | yes | 5 | 1 s |
| Comparative | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:53 | yes | 15 | 40 s |
| Comparative | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 10:45 | yes | 25 | 29 s |
| Comparative | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:31 | yes | 22 | 38 s |
| Comparative | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:14 | yes | 20 | 47 s |
| Comparative | GPT-6 Luna | gpt-6-luna | 2026-10-01 09:18 | yes | 7 | 23 s |
| Comparative | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 13:25 | yes | 15 | 30 s |
| Budget constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 09:09 | yes | 15 | 11 s |
| Budget constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 10:17 | yes | 5 | 7 s |
| Budget constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 12:47 | yes | 12 | 26 s |
| Budget constrained | Perplexity Sonar | sonar | 2026-10-01 09:12 | yes | 20 | 2 s |
| Budget constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 08:22 | yes | 25 | 10 s |
| Budget constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:16 | yes | 10 | 5 s |
| Budget constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 11:59 | yes | 25 | 40 s |
| Budget constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 12:35 | yes | 5 | 1 s |
| Budget constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 14:01 | yes | 25 | 63 s |
| Budget constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 07:49 | yes | 19 | 40 s |
| Budget constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 07:44 | yes | 25 | 174 s |
| Budget constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:53 | yes | 24 | 58 s |
| Budget constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 13:26 | yes | 4 | 24 s |
| Budget constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 14:09 | yes | 20 | 28 s |
| Scale constrained | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 07:45 | no | 0 | 6 s |
| Scale constrained | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 12:18 | no | 0 | 8 s |
| Scale constrained | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 11:40 | yes | 9 | 24 s |
| Scale constrained | Perplexity Sonar | sonar | 2026-10-01 10:10 | yes | 20 | 6 s |
| Scale constrained | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 11:58 | yes | 20 | 10 s |
| Scale constrained | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 11:24 | yes | 25 | 11 s |
| Scale constrained | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:51 | yes | 24 | 51 s |
| Scale constrained | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 07:54 | yes | 5 | 1 s |
| Scale constrained | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 07:46 | no | 0 | 35 s |
| Scale constrained | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 09:34 | yes | 23 | 43 s |
| Scale constrained | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 11:37 | yes | 24 | 86 s |
| Scale constrained | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 11:57 | yes | 5 | 34 s |
| Scale constrained | GPT-6 Luna | gpt-6-luna | 2026-10-01 07:48 | yes | 3 | 33 s |
| Scale constrained | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 09:38 | yes | 25 | 43 s |
| Negative framing | Claude Haiku 4.5 | claude-haiku-4-5-20251001 | 2026-10-01 08:57 | yes | 16 | 9 s |
| Negative framing | GPT-5.4 mini | gpt-5.4-mini-2026-03-17 | 2026-10-01 07:45 | yes | 4 | 6 s |
| Negative framing | Gemini 3.5 Flash | gemini-3.5-flash | 2026-10-01 08:46 | yes | 30 | 26 s |
| Negative framing | Perplexity Sonar | sonar | 2026-10-01 11:16 | yes | 20 | 5 s |
| Negative framing | Grok 4.1 Fast | spacexai/grok-4.1-fast-non-reasoning via vertex | 2026-10-01 09:04 | yes | 22 | 10 s |
| Negative framing | Mistral Small | mistral/mistral-small via mistral | 2026-10-01 09:15 | yes | 19 | 6 s |
| Negative framing | DeepSeek V4 Flash | deepseek/deepseek-v4-flash via deepinfra | 2026-10-01 09:05 | yes | 24 | 39 s |
| Negative framing | Llama 4 Maverick | meta/llama-4-maverick via bedrock | 2026-10-01 13:53 | yes | 5 | 2 s |
| Negative framing | Qwen 3.7 Flash | alibaba/qwen3.7-flash via alibaba | 2026-10-01 09:02 | no | 0 | 40 s |
| Negative framing | Kimi K2 | moonshotai/kimi-k2 via novita | 2026-10-01 13:23 | yes | 25 | 33 s |
| Negative framing | GLM 4.7 FlashX | zai/glm-4.7-flashx via zai | 2026-10-01 10:50 | yes | 25 | 56 s |
| Negative framing | MiniMax M2.5 | minimax/minimax-m2.5 via minimax | 2026-10-01 10:30 | yes | 19 | 27 s |
| Negative framing | GPT-6 Luna | gpt-6-luna | 2026-10-01 10:45 | yes | 5 | 19 s |
| Negative framing | Muse Glimmer 30B | meta/muse-glimmer-30b via togetherai | 2026-10-01 12:38 | yes | 22 | 21 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
Amazon Redshift Serverless read as Amazon Redshift
Databricks read as Databricks SQL Warehouse
Databricks SQL read as Databricks SQL Warehouse
RudderStack read as RudderStack Reverse ETL
Unresolved, counted raw
AWS Aurora Serverless
Amazon Redshift Provisioned
Astera Centerprise AI
Astile
Dataform
Estuary Flow
Extrata
Google Transfer Service
MariaDB
Neon
Porter
Postgres, managed
Raw PostgreSQL
Self-Hosted Airbyte
Snowflake Standard Edition
Stackmatix
Symmetric
Syncly
Tinybird
self-hosted Postgres
Discontinued, still offered
Muse Glimmer 30B named Grouparoo as mention on the budget prompt. Open source reverse ETL, shut down 2023.
MiniMax M2.5 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.