Rootly vs FireHydrant
One of fourteen models named Rootly first on the direct prompt; zero named FireHydrant. Rootly was named by thirteen of the fourteen models and FireHydrant by thirteen and Rootly carries 42 labels and FireHydrant 34, so the shares are not directly comparable.
Named in one category 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 incident management and on-call page.
By framing
RootlyFirst choices, of fourteen modelsFireHydrant
Across every category in the November 2026 Edition, Rootly and FireHydrant were named in the same answer fifty-three times, of the 106 answers naming Rootly and the 80 naming FireHydrant. In those answers FireHydrant took the first choice zero times and Rootly nine.
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 | | | |
Rootly FireHydrant first choice named as an alternative argued againstblank: not namedEach cell is one answer, Rootly on the left and FireHydrant on the right.The direct prompt
Rootly first, FireHydrant not the choice
1 of 14 modelsFireHydrant was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
8 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
5 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
What the models said about Rootly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of five in this category shown.
“Slack-centered coordination can be insufficient when Slack or identity systems are unavailable.” Perplexity Sonar · negative prompt · soft negative
“AI-first approach with automated RCA and fix suggestions; seamless Slack integration; competitive pricing vs. PagerDuty” MiniMax M2.5 · comparative prompt · first choice
“Start with a trial of incident.io or Rootly if your team is engineering-led and Slack-centric” Grok 4.20 · direct prompt · first choice
“Modern / ChatOps-First | Incident.io, Rootly, FireHydrant | Best developer experience.” Gemini 3.5 Flash · scale prompt · first choice
What the models said about FireHydrant
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Pilot any tool with realistic incident creation time, alert routing accuracy and on-call fatigue impact before committing, especially FireHydrant” Muse Glimmer 30B · negative prompt · soft negative
“FireHydrant starts at $9,600/year, making it unsuitable for tight budgets” DeepSeek V4 Flash · budget prompt · soft negative
“some users report it's *too Prescriptive* about incident processes” DeepSeek V4 Flash · negative prompt · soft negative
“incident.io or FireHydrant are both solid starting points that won't cost you anything until you're ready to pay.” Claude Haiku 4.5 · budget prompt · first choice
“Choose Incident.io / Rootly / FireHydrant if you are an engineering-first culture” Qwen 3.7 Flash · scale prompt · first choice
“Strong automation and Slack integration, but less comprehensive than incident.io for end-to-end management.” Mistral Small · comparative 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.