OpenMetadata vs Select Star
Zero of fourteen models named OpenMetadata first on the direct prompt; zero named Select Star. OpenMetadata was named by fourteen of the fourteen models and Select Star by eight and OpenMetadata carries 38 labels and Select Star 12, so the shares are not directly comparable.
Named in two 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
OpenMetadataFirst choices, of fourteen modelsSelect Star
Across every category in the November 2026 Edition, OpenMetadata and Select Star were named in the same answer thirty-three times, of the 120 answers naming OpenMetadata and the 47 naming Select Star. In those answers Select Star took the first choice eleven times and OpenMetadata ten.
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 | |
OpenMetadata Select Star first choice named as an alternative argued againstblank: not namedEach cell is one answer, OpenMetadata on the left and Select Star on the right.The direct prompt
Neither was the first choice, one was named
7 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
OpenMetadata19%#2 of 16Select Star16%#3 of 16OpenMetadata16%#2 of 16Select Star2%#8 of 16OpenMetadata2%#9 of 11Select Star0%#– of 11What the models said about OpenMetadata
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Tools like OpenMetadata are popular for their cost, but they carry a specific risk: Operational Overhead.” GLM 4.7 FlashX · 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
“they still require deployment, connector maintenance, upgrades, and customization” Perplexity Sonar · negative prompt · soft negative
“If you have some engineering capacity: Start with OpenMetadata. It offers the best balance of modern features, ease of deployment, and community support for a free tool.” Kimi K2 · budget prompt · first choice
“For a limited budget, OpenMetadata appears to be the best balance of features, ease of use, and zero licensing costs” MiniMax M2.5 · budget prompt · first choice
“likely to be an open-source solution such as OpenMetadata or DataHub, as they offer a cost-effective option” Llama 4 Maverick · budget 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.