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

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

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Cube | 2% | #6 of 19 | 0% | 20 | 10 of 14 |
| GoodData | 2% | #7 of 19 | 27% | 15 | 7 of 14 |

## The direct prompt, model by model

- 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)
- Claude Haiku 4.5: neither named (first choices: Sisense) (alternatives: Holistics, Looker Embedded, Metabase, Power BI Embedded)
- GPT-5.4 mini: neither named (first choices: Sisense) (alternatives: Looker, Power BI Embedded, 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)
- Mistral Small: neither named (first choices: Explo, Sisense) (alternatives: Microsoft Power BI Embedded, Tableau Embedded Analytics)
- Llama 4 Maverick: neither named (first choices: Reveal BI) (alternatives: Cube.dev, Omni.co, Thinklytics Insights, Zoho)
- Qwen 3.7 Flash: neither named (first choices: Sisense) (alternatives: Looker, Microsoft Power BI Embedded, ThoughtSpot Embedded)
- 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)
- GPT-6 Luna: neither named (first choices: Metabase) (alternatives: Looker Embed, Sisense, ThoughtSpot Embedded)
- Muse Glimmer 30B: neither named (first choices: Sisense) (alternatives: Reveal BI, Toucan)

## 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)

## What the models said about GoodData

- "Avoid: Looker, Domo, ThoughtSpot, and GoodData if budget is your primary constraint" (DeepSeek V4 Flash, budget prompt, hard negative)
- "Don't use: GoodData (complexity, pricing, proprietary language)" (DeepSeek V4 Flash, negative prompt, hard negative)
- "Steep learning curve (proprietary MAQL language); per-workspace pricing scales poorly for multi-tenant" (Grok 4.1 Fast, negative prompt, soft negative)
- "Choose GoodData/Looker if you are building a software product (SaaS) and need the analytics to look like part of the app itself." (Qwen 3.7 Flash, comparative prompt, first choice)
- "platforms like Sisense, GoodData, ThoughtSpot, and Explo were purpose-built for that use case" (DeepSeek V4 Flash, comparative prompt, first choice)
- "Top for Scalability: GoodData, ThoughtSpot (strong multi-tenant/AI)." (Grok 4.1 Fast, scale 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.