Rootly vs FireHydrant
One of fourteen models named Rootly first on the direct prompt; zero named FireHydrant. Rootly was named by twelve of the fourteen models and FireHydrant by ten and Rootly carries 33 labels and FireHydrant 21, so the shares are not directly comparable.
Named in two categories this edition.
Named in two 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 October 2026 Edition, Rootly and FireHydrant were named in the same answer twenty-eight times, of the 106 answers naming Rootly and the 51 naming FireHydrant. In those answers FireHydrant took the first choice zero times and Rootly four.
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 4.1 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 | | | | |
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
7 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
6 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
Rootly0%#7 of 9FireHydrant0%#9 of 9What the models said about Rootly
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“some vendors (like Rootly) have separate pricing for on-call, AI, and status pages that can balloon your bill” DeepSeek V4 Flash · scale prompt · soft negative
“The Best All-Around Alternative: Rootly ... Choose Rootly if you want to improve your incident response processes and save money simultaneously.” Qwen 3.7 Flash · paraphrase prompt · first choice
“Actively consider instead: For modern teams, look at incident.io, Rootly, FireHydrant” DeepSeek V4 Flash · negative prompt · first choice
“Consider modern, Slack-native platforms like incident.io, Rootly” Gemini 3.5 Flash · negative 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. Five of six in this category shown.
“potentially fine products, but be cautious if you're sensitive to ownership changes, product consolidation, or roadmap uncertainty” GPT-5.4 mini · negative prompt · soft negative
“FireHydrant was acquired by Freshworks... acquisitions often trigger shifts in pricing” Gemini 3.5 Flash · negative prompt · soft negative
“The Best All-Rounder: FireHydrant ... widely considered the best modern alternative to expensive enterprise platforms” Qwen 3.7 Flash · budget prompt · first choice
“Best for: Teams wanting an all-in-one platform spanning alerting, on-call, incident response, and retrospectives” DeepSeek V4 Flash · comparative prompt · alternative
“Best for ITIL & Customer Support Alignment ... the Freshworks ecosystem is highly compelling.” Gemini 3.5 Flash · direct 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.