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Incident management and on-call · November 2026 Edition

Rootly vs Better Stack

One of fourteen models named Rootly first on the direct prompt; zero named Better Stack. Rootly was named by thirteen of the fourteen models and Better Stack by ten and Rootly carries 42 labels and Better Stack 20, so the shares are not directly comparable.

Rootly

accepted challenger

Named in one category this edition.

Better Stack

accepted challenger

Named in eight categories this edition.

First-choice share9%4%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate2%5%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#6A position in a field of 13; printed, not drawn.
Labels4220A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Rootly reading right to left. Rank and label count are printed, not drawn.incident.io was named alongside these two in twelve of the fourteen direct answers. incident.io vs Rootly · incident.io vs Better Stack · PagerDuty vs Rootly

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.
RootlyFirst choices, of fourteen modelsBetter Stack
Direct10
Paraphrase10
Comparative40
Budget-constrained12
Scale-constrained201 against Better Stack
Negative201 against Rootly
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 Rootly and Better Stack stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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 Better Stack first choice named as an alternative argued againstblank: not namedEach cell is one answer, Rootly on the left and Better Stack on the right.

The direct prompt

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

Rootly first, Better Stack not the choice

1 of 14 modelsBetter Stack was named in the answer but not as the choice, or not at all.
Grok 4.20Rootly, incident.io alternatives: Freshservice, Jira Service Management, PagerDuty

Neither was the first choice, one was named

9 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Claude Haiku 4.5Zenduty alternatives: Freshservice, Jira Service Management, Rootly, Squadcast
GPT-5.4 miniPagerDuty alternatives: FireHydrant, Jira Service Management, Rootly, xMatters
Gemini 3.5 Flashincident.io alternatives: FireHydrant, Jira Service Management, Rootly
Perplexity SonarPagerDuty alternatives: Better Stack, Freshservice, Jira Service Management, Squadcast, incident.io
DeepSeek V4 Flashincident.io alternatives: FireHydrant, PagerDuty, Rootly, Squadcast
Kimi K2incident.io alternatives: FireHydrant, PagerDuty, Rootly
MiniMax M2.5incident.io alternatives: FireHydrant, Rootly
GPT-6 Lunaincident.io alternatives: PagerDuty, Rootly
Muse Glimmer 30Bincident.io alternatives: All Quiet, FireHydrant, OnPage, PagerDuty, Rootly, SolarWinds IT Incident Response, formerly Squadcast, Zenduty

Neither was named

4 of 14 modelsThe answer made no first choice from these two in this category.
Mistral Smallincident.io
Llama 4 Maverickincident.io alternatives: Freshservice, PagerDuty, Squadcast
Qwen 3.7 Flashincident.io alternatives: Opsgenie, PagerDuty
GLM 4.7 FlashXincident.io alternatives: Jira Service Management, PagerDuty, ServiceNow ITSM

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
Better Stack leads by seven points.
Better Stack11%#3 of 14
Rootly4%#5 of 14
The full small business standing →
Mid-marketThe figures above
The order flips: Rootly leads at mid-market.
Rootly9%#3 of 13
Better Stack4%#6 of 13
The full mid-market standing →
Enterprise
Rootly leads by four points.
Rootly4%#4 of 12
Better Stack0%#– of 12
The full enterprise standing →

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 Better Stack

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

“Simple, fast setup, but may lack advanced workflow automation and enterprise governance needed at 500+ employees.” Gemini 3.5 Flash · scale prompt · soft negative
“Best Overall Value SaaS: Better Stack ... If you want cheap & easy SaaS with monitoring built-in: Go with Better Stack.” Gemini 3.5 Flash · budget 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.