Secoda vs DataHub
Four of fourteen models named Secoda first on the direct prompt; one named DataHub. Secoda was named by ten of the fourteen models and DataHub by twelve and Secoda carries 18 labels and DataHub 36, so the shares are not directly comparable.
Named in two categories this edition.
Named in two categories 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
SecodaFirst choices, of fourteen modelsDataHub
Across every category in the October 2026 Edition, Secoda and DataHub were named in the same answer forty-eight times, of the 83 answers naming Secoda and the 101 naming DataHub. In those answers DataHub took the first choice three times and Secoda seventeen.
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 4.1 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 | | |
Secoda DataHub first choice named as an alternative argued againstblank: not namedEach cell is one answer, Secoda on the left and DataHub on the right.The direct prompt
Secoda first, DataHub an alternative
4 of 14 modelsDataHub was named in the answer but not as the choice, or not at all.DataHub first, Secoda not the choice
1 of 14 modelsSecoda was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
4 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 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
Secoda0%#11 of 11DataHub0%#8 of 11What the models said about Secoda
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Best low-cost SaaS option: Secoda for small or early-stage teams... Secoda is the strongest SaaS candidate for small teams” Perplexity Sonar · budget prompt · first choice
“Secoda and Select Star are incredibly fast to stand up (1–2 weeks) and highly intuitive for lean teams.” Gemini 3.5 Flash · scale prompt · first choice
“For most mid-market B2B companies, Secoda is the strongest recommendation” DeepSeek V4 Flash · direct prompt · first choice
What 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.
“Avoid for Mid-Market: ... open-source like DataHub/Amundsen (free but needs engineering overhead)” Grok 4.1 Fast · direct prompt · hard negative
“Only choose this if you have a dedicated data platform engineer who has the bandwidth to manage and upgrade the infrastructure.” Gemini 3.5 Flash · scale prompt · soft negative
“While open source options like OpenMetadata and DataHub are credible, they require substantial in-house engineering expertise” Mistral Small · negative prompt · soft negative
“likely to be an open-source tool such as Amundsen or DataHub, which are suitable for on-premise environments” Llama 4 Maverick · budget prompt · first choice
“If you have some technical resources, OpenMetadata or DataHub are the best free options.” Mistral Small · budget prompt · first choice
“Best overall low-budget choice: DataHub” GPT-5.4 mini · budget prompt · first choice
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.