Embedded analytics · October 2026 Edition
Cube vs Apache Superset
Zero of fourteen models named Cube first on the direct prompt; zero named Apache Superset. Cube was named by ten of the fourteen models and Apache Superset by nine and Cube carries 20 labels and Apache Superset 10, so the shares are not directly comparable.
Named in three categories this edition.
Named in one category this edition.
First-choice share2%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate0%20%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#6#8A position in a field of 19; printed, not drawn.
Labels2010A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Cube reading right to left. Rank and label count are printed, not drawn.Sisense was named alongside these two in eight of the fourteen direct answers. Metabase vs Cube · Metabase vs Apache Superset · Sisense vs Cube
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 embedded analytics page.
By framing
How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
CubeFirst choices, of fourteen modelsApache Superset
Direct00
Paraphrase00
Comparative20
Budget-constrained012 against Apache Superset
Scale-constrained10
Negative20
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 Cube and Apache Superset stood in it.
| 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 | | |
Cube Apache Superset first choice named as an alternative argued againstblank: not namedEach cell is one answer, Cube on the left and Apache Superset on the right.The direct prompt
The plain question, one answer per model, grouped by where Cube and Apache Superset stood in it.
Neither was the first choice, one was named
2 of 14 modelsThe answer put something else first and named one of the two as an alternative.Perplexity SonarThoughtSpot Embedded alternatives: Cube, Looker, Zoho Analytics
MiniMax M2.5Microsoft Power BI Embedded alternatives: Cube, Explo, Grow, Looker, Metabase, Sisense, Tableau Embedded Analytics, ThoughtSpot Embedded
Neither was named
12 of 14 modelsThe answer made no first choice from these two in this category.Claude Haiku 4.5Sisense alternatives: Holistics, Looker Embedded, Metabase, Power BI Embedded
GPT-5.4 miniSisense alternatives: Looker, Power BI Embedded, ThoughtSpot Embedded
Gemini 3.5 FlashOmni alternatives: Embeddable, Luzmo, Metabase, Qrvey
Grok 4.1 FastReveal BI alternatives: Bold BI, Domo Everywhere, Power BI Embedded, Tableau, Zoho Analytics
Mistral SmallExplo, Sisense alternatives: Microsoft Power BI Embedded, Tableau Embedded Analytics
DeepSeek V4 FlashLuzmo alternatives: GoodData, Sisense
Llama 4 MaverickReveal BI alternatives: Cube.dev, Omni.co, Thinklytics Insights, Zoho
Qwen 3.7 FlashSisense alternatives: Looker, Microsoft Power BI Embedded, ThoughtSpot Embedded
Kimi K2Explo alternatives: Looker, Metabase, Power BI Embedded, Preset
GLM 4.7 FlashXMetabase alternatives: Looker, ThoughtSpot Embedded
GPT-6 LunaMetabase alternatives: Looker Embed, Sisense, ThoughtSpot Embedded
Muse Glimmer 30BSisense alternatives: Reveal BI, Toucan
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
Apache Superset leads by two points.
Apache Superset2%#– of 17Cube0%#– of 17Mid-marketThe figures above
Level: the same share of first choices.
Cube2%#6 of 19Apache Superset2%#8 of 19Enterprise
The order flips: Cube leads at enterprise.
Cube2%#– of 15Apache Superset0%#– of 15What the models said about Cube
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of four in this category shown.
“Cube: An industry-standard "headless" semantic layer with an open-source core. It is excellent for high-concurrency, complex multi-tenancy, and has a native AI-agent API.” Gemini 3.5 Flash · scale prompt · first choice
“Choose Cube or Omni if you are building a modern SaaS application that needs scalable, highly secure multi-tenancy” Gemini 3.5 Flash · comparative prompt · first choice
“Choose Cube if you need strong multi-tenant isolation and row-level security for SaaS embedding.” Perplexity Sonar · comparative prompt · first choice
What the models said about Apache Superset
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“free options, but embedding typically requires paid tiers or significant engineering work to white-label properly” DeepSeek V4 Flash · budget prompt · soft negative
“it has a steeper learning curve to deploy and configure than Metabase” Gemini 3.5 Flash · budget prompt · soft negative
“If you hit their pricing limits and have engineering capacity, move to Apache Superset.” Qwen 3.7 Flash · budget prompt · first choice
“Apache Superset is the lower-license-cost alternative” GPT-6 Luna · budget prompt · alternative
“Apache Superset is a free SQL-native alternative” Muse Glimmer 30B · budget prompt · alternative
Also compared
Metabase vs CubeMetabase vs Apache SupersetSisense vs CubeSisense vs Apache SupersetLuzmo vs CubeLuzmo vs Apache SupersetExplo vs CubeExplo vs Apache SupersetThoughtSpot Embedded vs CubeThoughtSpot Embedded vs Apache SupersetCube vs GoodDataGoodData vs Apache SupersetAll twenty-eight head-to-head pages and the full embedded analytics standing
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.