Embedded analytics · October 2026 Edition
Sisense vs Apache Superset
Five of fourteen models named Sisense first on the direct prompt; zero named Apache Superset. Sisense was named by fourteen of the fourteen models and Apache Superset by nine and Sisense carries 39 labels and Apache Superset 10, so the shares are not directly comparable.
Named in one category this edition.
Named in one category this edition.
First-choice share15%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate28%20%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#8A position in a field of 19; printed, not drawn.
Labels3910A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Sisense reading right to left. Rank and label count are printed, not drawn.Metabase vs Sisense · Metabase vs Apache Superset · Sisense vs Luzmo
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.
SisenseFirst choices, of fourteen modelsApache Superset
Direct501 against Sisense
Paraphrase302 against Sisense
Comparative20
Budget-constrained012 against Sisense · 2 against Apache Superset
Scale-constrained002 against Sisense
Negative004 against Sisense
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 Sisense 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 | | |
Sisense Apache Superset first choice named as an alternative argued againstblank: not namedEach cell is one answer, Sisense on the left and Apache Superset on the right.The direct prompt
The plain question, one answer per model, grouped by where Sisense and Apache Superset stood in it.
Sisense first, Apache Superset not the choice
5 of 14 modelsApache Superset was named in the answer but not as the choice, or not at all.Claude Haiku 4.5Sisense alternatives: Holistics, Looker Embedded, Metabase, Power BI Embedded
GPT-5.4 miniSisense alternatives: Looker, Power BI Embedded, ThoughtSpot Embedded
Mistral SmallExplo, Sisense alternatives: Microsoft Power BI Embedded, Tableau Embedded Analytics
Qwen 3.7 FlashSisense alternatives: Looker, Microsoft Power BI Embedded, ThoughtSpot Embedded
Muse Glimmer 30BSisense alternatives: Reveal BI, Toucan
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.DeepSeek V4 FlashLuzmo alternatives: GoodData, Sisense
MiniMax M2.5Microsoft Power BI Embedded alternatives: Cube, Explo, Grow, Looker, Metabase, Sisense, Tableau Embedded Analytics, ThoughtSpot Embedded
GPT-6 LunaMetabase alternatives: Looker Embed, Sisense, ThoughtSpot Embedded
Neither was named
6 of 14 modelsThe answer made no first choice from these two in this category.Gemini 3.5 FlashOmni alternatives: Embeddable, Luzmo, Metabase, Qrvey
Perplexity SonarThoughtSpot Embedded alternatives: Cube, Looker, Zoho Analytics
Grok 4.1 FastReveal BI alternatives: Bold BI, Domo Everywhere, Power BI Embedded, Tableau, Zoho Analytics
Llama 4 MaverickReveal BI alternatives: Cube.dev, Omni.co, Thinklytics Insights, Zoho
Kimi K2Explo alternatives: Looker, Metabase, Power BI Embedded, Preset
GLM 4.7 FlashXMetabase alternatives: Looker, ThoughtSpot Embedded
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 17Sisense0%#15 of 17Mid-marketThe figures above
The order flips: Sisense leads at mid-market.
Sisense15%#2 of 19Apache Superset2%#8 of 19Enterprise
Sisense leads by thirteen points.
Sisense13%#2 of 15Apache Superset0%#– of 15What the models said about Sisense
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“A catastrophic, highly publicized supply chain data breach in April 2024.” Gemini 3.5 Flash · negative prompt · hard negative
“Avoid: Enterprise platforms like Tableau, Looker, or Sisense” Kimi K2 · budget prompt · hard negative
“Enterprise-grade tools (like Looker, Sisense, or Tableau) often require complex sales negotiations and five-to-six-figure annual contracts.” Gemini 3.5 Flash · budget prompt · soft negative
“Sisense is widely considered the strongest all-around contender due to its balance of price, ease of integration, and powerful visualization engine.” Qwen 3.7 Flash · direct prompt · first choice
“Best for mid-market SaaS products that want a turnkey embedded solution; teams without ecosystem commitments to Microsoft or Salesforce” Claude Haiku 4.5 · direct prompt · first choice
“For most mid-market B2B companies, Sisense and Explo are the strongest contenders, with Sisense leading for technical flexibility” Mistral Small · direct 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 SisenseMetabase vs Apache SupersetSisense vs LuzmoSisense vs ExploSisense vs ThoughtSpot EmbeddedSisense vs CubeSisense vs GoodDataLuzmo vs Apache SupersetExplo vs Apache SupersetThoughtSpot Embedded vs Apache SupersetCube vs Apache SupersetGoodData 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.