# BigQuery vs Census: which do AI models recommend for Warehouse & ETL, September 2026

GTM AI Recommendation Index, September 2026 Edition, Data warehouse and reverse ETL for marketing. Six of twelve models named BigQuery first on the direct prompt; zero named Census. Page: https://gtm-ai-index.com/gtm-data/data-warehouse-reverse-etl/bigquery-vs-census/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| BigQuery | 24% | #2 of 14 | 11% | 65 | 12 of 12 |
| Census | 5% | #4 of 14 | 19% | 63 | 12 of 12 |

## The direct prompt, model by model

- Claude Haiku 4.5: bigquery first (first choices: BigQuery, Hightouch, Polytomic, Snowflake) (alternatives: Amazon Redshift, Hevo, RudderStack)
- Gemini 3.5 Flash: bigquery first (first choices: BigQuery, Hightouch, Snowflake) (alternatives: Census, Fivetran, dbt)
- Perplexity Sonar: bigquery first (first choices: BigQuery, Hightouch) (alternatives: Cometly, Fivetran, Funnel, Improvado, LeadJourney, Segment)
- Grok 4.1 Fast: bigquery first (first choices: BigQuery, Hightouch) (alternatives: Census, Snowflake)
- Mistral Small: bigquery first (first choices: BigQuery, Hightouch) (alternatives: Airbyte, Census, Databricks, Fivetran, Improvado, Snowflake, dbt)
- DeepSeek V4 Flash: bigquery first (first choices: BigQuery, Hightouch) (alternatives: Airbyte, Census, Fivetran, PostgreSQL, Snowflake, dbt)
- GPT-5.4 mini: neither first, one named (first choices: Hightouch, Snowflake) (alternatives: BigQuery, Census, Databricks, Fivetran, dbt)
- Qwen 3.7 Flash: neither first, one named (first choices: Hightouch, Snowflake) (alternatives: BigQuery, Census, Fivetran, Improvado, Polytomic, dbt)
- Kimi K2: neither first, one named (first choices: Hightouch, Snowflake) (alternatives: Airbyte, BigQuery, Census, Fivetran, Hevo, Improvado, Polytomic, dbt)
- GLM 4.7 FlashX: neither first, one named (first choices: Hightouch, Snowflake) (alternatives: BigQuery, Census, Databricks, Polytomic, dbt)
- MiniMax M2.5: neither first, one named (first choices: Hightouch, Snowflake) (alternatives: BigQuery, Census)
- Llama 4 Maverick: neither named

## What the models said about BigQuery

- "BigQuery (Proceed with Query Guardrails) ... can cost the company hundreds of dollars in a single click" (Gemini 3.5 Flash, negative prompt, soft negative)
- "BigQuery's pay-per-query model offers flexibility but can get unpredictable based on query volume." (Claude Haiku 4.5, negative prompt, soft negative)
- "unpredictable on-demand query costs ($6.25/TB scanned) spike on unoptimized marketing queries" (Grok 4.1 Fast, negative prompt, soft negative)
- "Google BigQuery | Best price-to-performance ratio; separates storage from compute so you only pay for what you query. Zero maintenance." (Qwen 3.7 Flash, paraphrase prompt, first choice)
- "For most mid-market companies, BigQuery's serverless model and lower operational overhead make it the easier starting point." (GLM 4.7 FlashX, paraphrase prompt, first choice)
- "BigQuery (free tier) → dlt or open-source Airbyte → dbt Core → Looker Studio, plus Adapters (~$49/month) for reverse ETL" (DeepSeek V4 Flash, budget prompt, first choice)

## What the models said about Census

- "❌ Census: Requires Enterprise for most features; quote-based pricing" (GLM 4.7 FlashX, budget prompt, hard negative)
- "Both Census and Hightouch excel at the "reverse" direction but require separate solutions for data ingestion, transformation, and real-time replication." (Claude Haiku 4.5, negative prompt, soft negative)
- "Why not Census/Fivetran Activations? ... now shifting to consumption-based (MAR) pricing. It's better suited for data engineering-owned pipelines" (Kimi K2, paraphrase prompt, soft negative)
- "Stick to the current market leaders (Hightouch, Census) which have significant funding" (Qwen 3.7 Flash, negative prompt, first choice)
- "Hightouch and Census are the safest choices for reverse ETL in marketing" (Mistral Small, negative prompt, first choice)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all twelve models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.