DataHub vs Select Star
Zero of fourteen models named DataHub first on the direct prompt; zero named Select Star. DataHub was named by fourteen of the fourteen models and Select Star by eight and DataHub carries 47 labels and Select Star 12, so the shares are not directly comparable.
Named in three categories this edition.
Named in one category this edition.
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 catalogs and governance page.
By framing
DataHubFirst choices, of fourteen modelsSelect Star
Across every category in the November 2026 Edition, DataHub and Select Star were named in the same answer thirty-two times, of the 126 answers naming DataHub and the 47 naming Select Star. In those answers Select Star took the first choice ten times and DataHub two.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | |||||
| GPT-5.4 mini | | | | | ||
| Gemini 3.5 Flash | | | | | ||
| Perplexity Sonar | | | | |||
| Grok 4.20 | | | | | ||
| 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 | |
DataHub Select Star first choice named as an alternative argued againstblank: not namedEach cell is one answer, DataHub on the left and Select Star on the right.The direct prompt
Neither was the first choice, one was named
5 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
9 of 14 modelsThe answer made no first choice from these two in this category.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
Select Star16%#3 of 16DataHub6%#5 of 16DataHub11%#4 of 16Select Star2%#8 of 16What the models said about DataHub
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Clunky/difficult interface compared to commercial alternatives ... Open-source options (DataHub) viable but require engineering investment” MiniMax M2.5 · negative prompt · soft negative
“Open-source or niche tools (like DataHub, Amundsen, Marquez) Caution level: Low-to-moderate (only if you lack engineering resources)” DeepSeek V4 Flash · negative prompt · soft negative
“plus OpenMetadata or DataHub if you're willing to take on more of the operational and engineering work” GPT-6 Luna · scale prompt · soft negative
“The Heavyweight Champion: DataHub ... is generally considered the most mature and widely adopted open-source data catalog” Qwen 3.7 Flash · budget prompt · first choice
“Mid-market solutions (Atlan, select, DataHub) often fit better than enterprise-only tools (Collibra) at this scale” Kimi K2 · scale prompt · first choice
“Strongest fit for ~500 employees): *Examples:* Atlan, Select Star, Secoda, CastorDoc, DataHub (Cloud/Acryl Data).” Gemini 3.5 Flash · scale prompt · first choice
What the models said about Select Star
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Strongest fit for ~500 employees): *Examples:* Atlan, Select Star, Secoda, CastorDoc, DataHub” Gemini 3.5 Flash · scale prompt · first choice
“Best for Lean Teams & Quick Time-to-Value: Select Star, Secoda, or CastorDoc” Gemini 3.5 Flash · direct prompt · alternative
“Modern, lightweight SaaS catalogs (e.g., Atlan, Select Star, CastorDoc, Secoda)” Gemini 3.5 Flash · negative prompt · alternative
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.