Starburst vs Microsoft Fabric
Zero of fourteen models named Starburst first on the direct prompt; zero named Microsoft Fabric. Starburst was named by eleven of the fourteen models and Microsoft Fabric by eight and Starburst carries 31 labels and Microsoft Fabric 12, so the shares are not directly comparable.
Named in two categories this edition.
Named in three categories this edition.
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 virtualization page.
By framing
StarburstFirst choices, of fourteen modelsMicrosoft Fabric
Across every category in the November 2026 Edition, Starburst and Microsoft Fabric were named in the same answer fifteen times, of the 83 answers naming Starburst and the 45 naming Microsoft Fabric. In those answers Microsoft Fabric took the first choice two times and Starburst three.
Every model, every framing
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | | | ||||
| GPT-5.4 mini | | |||||
| Gemini 3.5 Flash | | | | | ||
| Perplexity Sonar | | | | | ||
| Grok 4.20 | | | | | ||
| 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 | | |
Starburst Microsoft Fabric first choice named as an alternative argued againstblank: not namedEach cell is one answer, Starburst on the left and Microsoft Fabric on the right.The direct prompt
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.Neither was named
11 of 14 modelsThe answer made no first choice from these two in this category.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
Starburst4%#6 of 14Microsoft Fabric4%#– of 14Starburst6%#5 of 10Microsoft Fabric2%#7 of 10Microsoft Fabric4%#2 of 9Starburst2%#3 of 9What the models said about Starburst
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Starburst Galaxy at 2% first-choice share with 36% negative rate, 11 labels, “criticized challenger”” Muse Glimmer 30B · negative prompt · soft negative
“can be cost-prohibitive” Gemini 3.5 Flash · budget prompt · soft negative
“I recommend Starburst (built on Trino) as the top choice for a data fabric focused on federated queries.” Grok 4.20 · paraphrase prompt · first choice
“Denodo, Dremio, Starburst Galaxy, and AtScale tend to rank higher” Grok 4.20 · negative prompt · first choice
“start with Starburst Galaxy for managed federation” Muse Glimmer 30B · paraphrase prompt · first choice
What the models said about Microsoft Fabric
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Five of six in this category shown.
“Microsoft Fabric or Databricks used as "pure" virtualization by default... can create unnecessary platform, storage, and skills overhead” Perplexity Sonar · negative prompt · soft negative
“Capacity-based pricing (~$32K-$100K+ annually); requires architecture rebuild if migrating” Kimi K2 · paraphrase prompt · soft negative
“Top Recommendation: Microsoft Fabric (with Starburst Galaxy integration)” GLM 4.7 FlashX · paraphrase prompt · first choice
“Choose Microsoft Fabric/Azure or AWS services when minimizing cloud-platform fragmentation matters most.” Perplexity Sonar · comparative prompt · alternative
“wants an integrated Microsoft analytics platform rather than a source-agnostic virtualization layer” Perplexity Sonar · paraphrase prompt · alternative
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.