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Incident management and on-call · October 2026 Edition

Freshservice vs Rootly

Six of fourteen models named Freshservice first on the direct prompt; one named Rootly. Freshservice was named by ten of the fourteen models and Rootly by twelve and Freshservice carries 17 labels and Rootly 33, so the shares are not directly comparable.

Freshservice

accepted challenger

Named in nine categories this edition.

Rootly

accepted challenger

Named in two categories this edition.

First-choice share10%5%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%3%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#7A position in a field of 11; printed, not drawn.
Labels1733A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Freshservice reading right to left. Rank and label count are printed, not drawn.incident.io was named alongside these two in eleven of the fourteen direct answers. incident.io vs Freshservice · incident.io vs Rootly · PagerDuty vs Freshservice

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
FreshserviceFirst choices, of fourteen modelsRootly
Direct61
Paraphrase02
Comparative001 against Freshservice
Budget-constrained00
Scale-constrained001 against Rootly
Negative02
Bars are first choices, 0 to 14 each sideModels that argued againstA model can name both, so the two sides of a row do not sum to fourteen.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where Freshservice and Rootly stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
Freshservice Rootly first choice named as an alternative argued againstblank: not namedEach cell is one answer, Freshservice on the left and Rootly on the right.

The direct prompt

The plain question, one answer per model, grouped by where Freshservice and Rootly stood in it.

Freshservice first, Rootly an alternative

6 of 14 modelsRootly was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Freshservice alternatives: HaloITSM, Hyperping, ManageEngine ServiceDesk Plus, Rootly, Zenduty, ilert, incident.io
Perplexity SonarFreshservice alternatives: PagerDuty, Rootly, incident.io
Grok 4.1 FastFreshservice alternatives: InvGate Service Management, Jira Service Management, ManageEngine ServiceDesk Plus, PagerDuty, incident.io
Mistral SmallFreshservice, PagerDuty alternatives: ManageEngine ServiceDesk Plus
MiniMax M2.5Freshservice, PagerDuty alternatives: Jira Service Management, incident.io
Muse Glimmer 30BFreshservice alternatives: PagerDuty, Rootly, incident.io

Rootly first, Freshservice not the choice

1 of 14 modelsFreshservice was named in the answer but not as the choice, or not at all.
GLM 4.7 FlashXRootly, incident.io alternatives: Jira Service Management, PagerDuty

Neither was the first choice, one was named

4 of 14 modelsThe answer put something else first and named one of the two as an alternative.
GPT-5.4 miniincident.io alternatives: PagerDuty, Rootly
Gemini 3.5 Flashincident.io alternatives: FireHydrant, Freshservice, Rootly
DeepSeek V4 Flashincident.io alternatives: Freshservice, Jira Service Management
GPT-6 Lunaincident.io alternatives: PagerDuty, Rootly

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 Maverickno first choice
Qwen 3.7 FlashPagerDuty alternatives: Datadog Incident Management, FireHydrant, Opsgenie
Kimi K2incident.io alternatives: Squadcast

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

The same question asked on behalf of a different buyer. Each standing is computed within its segment and they are never added together. The figures above are the mid-market standing, which is the one the category orders by.
Small business
Rootly leads by five points.
Rootly7%#5 of 13
Freshservice2%#8 of 13
The full small business standing →
Mid-marketThe figures above
The order flips: Freshservice leads at mid-market.
Freshservice10%#3 of 11
Rootly5%#7 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Freshservice0%#– of 9
Rootly0%#7 of 9
The full enterprise standing →

What the models said about Freshservice

Every negative label with a quote, up to three, then the highest-weighted positives, up to three. One of one in this category shown.

“Very slow to deploy, highly complex, and intensely disliked by most modern software engineering/DevOps teams” Gemini 3.5 Flash · comparative prompt · soft negative

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 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
Also compared

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.