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Data catalogs and governance · November 2026 Edition

Secoda vs OvalEdge

Seven of fourteen models named Secoda first on the direct prompt; one named OvalEdge. Secoda was named by thirteen of the fourteen models and OvalEdge by seven and Secoda carries 26 labels and OvalEdge 13, so the shares are not directly comparable.

Secoda

accepted challenger

Named in two categories this edition.

OvalEdge

accepted challenger

Named in three categories this edition.

First-choice share13%3%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate4%8%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 16; printed, not drawn.
Labels2613A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Secoda reading right to left. Rank and label count are printed, not drawn.Atlan was named alongside these two in twelve of the fourteen direct answers. Atlan vs Secoda · Atlan vs OvalEdge · OpenMetadata vs Secoda

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
SecodaFirst choices, of fourteen modelsOvalEdge
Direct71
Paraphrase01
Comparative00
Budget-constrained00
Scale-constrained10
Negative111 against Secoda · 1 against OvalEdge
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Secoda and OvalEdge stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Secoda OvalEdge first choice named as an alternative argued againstblank: not namedEach cell is one answer, Secoda on the left and OvalEdge on the right.

The direct prompt

The plain question, one answer per model, grouped by where Secoda and OvalEdge stood in it.

Both were the first choice

1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
DeepSeek V4 FlashOvalEdge, Secoda alternatives: Atlan, DataHub, Select Star

Secoda first, OvalEdge an alternative

6 of 14 modelsOvalEdge was named in the answer but not as the choice, or not at all.
Mistral SmallAtlan, Secoda
Llama 4 MaverickSecoda alternatives: Atlan, data.world
Qwen 3.7 FlashAtlan, Secoda alternatives: Coalesce Catalog, Microsoft Purview
Kimi K2CastorDoc, Secoda alternatives: Atlan, Select Star
MiniMax M2.5Secoda alternatives: Atlan
Muse Glimmer 30BSecoda alternatives: Dataedo, OvalEdge, data.world

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.
Gemini 3.5 FlashAtlan alternatives: AWS Glue Data Catalog, Acryl Data, CastorDoc, Databricks Unity Catalog, OpenMetadata, Secoda, Select Star, Snowflake Horizon
Perplexity SonarAtlan alternatives: Collibra, Microsoft Purview, OvalEdge, Secoda
Grok 4.20Atlan alternatives: DataHub, OpenMetadata, Secoda, Select Star
GPT-6 LunaAtlan alternatives: OpenMetadata, Secoda, Your platform's built-in catalog

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5no first choice
GPT-5.4 miniAtlan alternatives: Alation, DataHub, Dataplex, Microsoft Purview, OpenMetadata, Unity Catalog
GLM 4.7 FlashXAtlan alternatives: DataGalaxy, Dawiso, OpenMetadata, Zeenea

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Secoda leads by seventeen points.
Secoda19%#1 of 16
OvalEdge2%#7 of 16
The full small business standing →
Mid-marketThe figures above
Secoda leads by ten points.
Secoda13%#3 of 16
OvalEdge3%#6 of 16
The full mid-market standing →
Enterprise
Secoda leads by two points.
Secoda2%#8 of 11
OvalEdge0%#– of 11
The full enterprise standing →

What the models said about Secoda

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.

“mentioned as being "criticized challengers" or having negative reviews” Llama 4 Maverick · negative prompt · soft negative
“If you want the cheapest, fastest adoption with AI-assisted search, Secoda is the most common mid-market recommendation today.” Muse Glimmer 30B · direct prompt · first choice
“Secoda is frequently highlighted as the strongest recommendation for most mid-market B2B companies” Mistral Small · direct prompt · first choice
“Strongest fit for ~500 employees): *Examples:* Atlan, Select Star, Secoda, CastorDoc, DataHub” Gemini 3.5 Flash · scale prompt · first choice

What the models said about OvalEdge

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.

“mentioned as being "criticized challengers" or having negative reviews” Llama 4 Maverick · negative prompt · soft negative
“Cloud-native independent platforms (e.g., Atlan, Secoda, OvalEdge) for faster time-to-value” Kimi K2 · negative prompt · first choice
“Secoda or OvalEdge offer the best balance of capability, cost, and time-to-value” DeepSeek V4 Flash · direct prompt · first choice
“Best Overall Recommendation: OvalEdge” Claude Haiku 4.5 · paraphrase prompt · first choice
Also compared

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.