# Sisense vs Cube: which do AI models recommend for embedded analytics, October 2026

IT AI Recommendation Index, October 2026 Edition, Embedded analytics. Five of fourteen models named Sisense first on the direct prompt; zero named Cube. Page: https://it-ai-index.com/it-data/embedded-analytics/sisense-vs-cube/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Sisense | 15% | #2 of 19 | 28% | 39 | 14 of 14 |
| Cube | 2% | #6 of 19 | 0% | 20 | 10 of 14 |

## The direct prompt, model by model

- Claude Haiku 4.5: sisense first (first choices: Sisense) (alternatives: Holistics, Looker Embedded, Metabase, Power BI Embedded)
- GPT-5.4 mini: sisense first (first choices: Sisense) (alternatives: Looker, Power BI Embedded, ThoughtSpot Embedded)
- Mistral Small: sisense first (first choices: Explo, Sisense) (alternatives: Microsoft Power BI Embedded, Tableau Embedded Analytics)
- Qwen 3.7 Flash: sisense first (first choices: Sisense) (alternatives: Looker, Microsoft Power BI Embedded, ThoughtSpot Embedded)
- Muse Glimmer 30B: sisense first (first choices: Sisense) (alternatives: Reveal BI, Toucan)
- Perplexity Sonar: neither first, one named (first choices: ThoughtSpot Embedded) (alternatives: Cube, Looker, Zoho Analytics)
- DeepSeek V4 Flash: neither first, one named (first choices: Luzmo) (alternatives: GoodData, Sisense)
- MiniMax M2.5: neither first, one named (first choices: Microsoft Power BI Embedded) (alternatives: Cube, Explo, Grow, Looker, Metabase, Sisense, Tableau Embedded Analytics, ThoughtSpot Embedded)
- GPT-6 Luna: neither first, one named (first choices: Metabase) (alternatives: Looker Embed, Sisense, ThoughtSpot Embedded)
- Gemini 3.5 Flash: neither named (first choices: Omni) (alternatives: Embeddable, Luzmo, Metabase, Qrvey)
- Grok 4.1 Fast: neither named (first choices: Reveal BI) (alternatives: Bold BI, Domo Everywhere, Power BI Embedded, Tableau, Zoho Analytics)
- Llama 4 Maverick: neither named (first choices: Reveal BI) (alternatives: Cube.dev, Omni.co, Thinklytics Insights, Zoho)
- Kimi K2: neither named (first choices: Explo) (alternatives: Looker, Metabase, Power BI Embedded, Preset)
- GLM 4.7 FlashX: neither named (first choices: Metabase) (alternatives: Looker, ThoughtSpot Embedded)

## What the models said about Sisense

- "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 Cube

- "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)

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. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.