OpenMetadata vs Secoda
Zero of fourteen models named OpenMetadata first on the direct prompt; four named Secoda. OpenMetadata was named by thirteen of the fourteen models and Secoda by ten and OpenMetadata carries 27 labels and Secoda 18, 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
OpenMetadataFirst choices, of fourteen modelsSecoda
Across every category in the October 2026 Edition, OpenMetadata and Secoda were named in the same answer forty-nine times, of the 94 answers naming OpenMetadata and the 83 naming Secoda. In those answers Secoda took the first choice sixteen times and OpenMetadata nine.
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 | |
OpenMetadata Secoda first choice named as an alternative argued againstblank: not namedEach cell is one answer, OpenMetadata on the left and Secoda on the right.The direct prompt
Secoda first, OpenMetadata not the choice
4 of 14 modelsOpenMetadata was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
3 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
OpenMetadata18%#1 of 16Secoda16%#2 of 16OpenMetadata18%#2 of 15Secoda11%#3 of 15OpenMetadata4%#5 of 11Secoda0%#11 of 11What the models said about OpenMetadata
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“While open source options like OpenMetadata and DataHub are credible, they require substantial in-house engineering expertise to deploy, maintain, and scale.” Mistral Small · negative prompt · soft 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
“OpenMetadata is generally the better starting point due to its simpler stack and faster time-to-value.” MiniMax M2.5 · budget prompt · first choice
“OpenMetadata (Best Overall Open Source) ... this is widely considered the best open-source data catalog” GLM 4.7 FlashX · budget prompt · first choice
“The Best All-Around (Open Source): OpenMetadata ... this is currently the top contender.” Qwen 3.7 Flash · budget prompt · first choice
What 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
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.