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Incident management and on-call · October 2026 Edition

Freshservice vs Grafana IRM

Six of fourteen models named Freshservice first on the direct prompt; zero named Grafana IRM. Freshservice was named by ten of the fourteen models and Grafana IRM by ten and Freshservice carries 17 labels and Grafana IRM 22, so the shares are not directly comparable.

Freshservice

accepted challenger

Named in nine categories this edition.

Grafana IRM

accepted challenger

Named in one category this edition.

First-choice share10%7%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate6%14%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#3#5A position in a field of 11; printed, not drawn.
Labels1722A 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 Grafana IRM · 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 modelsGrafana IRM
Direct60
Paraphrase022 against Grafana IRM
Comparative001 against Freshservice
Budget-constrained02
Scale-constrained00
Negative001 against Grafana IRM
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 Grafana IRM 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 Grafana IRM first choice named as an alternative argued againstblank: not namedEach cell is one answer, Freshservice on the left and Grafana IRM on the right.

The direct prompt

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

Freshservice first, Grafana IRM not the choice

6 of 14 modelsGrafana IRM 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

Neither was the first choice, one was named

2 of 14 modelsThe answer put something else first and named one of the two as an alternative.
Gemini 3.5 Flashincident.io alternatives: FireHydrant, Freshservice, Rootly
DeepSeek V4 Flashincident.io alternatives: Freshservice, Jira Service Management

Neither was named

6 of 14 modelsThe answer made no first choice from these two in this category.
GPT-5.4 miniincident.io alternatives: PagerDuty, Rootly
Llama 4 Maverickno first choice
Qwen 3.7 FlashPagerDuty alternatives: Datadog Incident Management, FireHydrant, Opsgenie
Kimi K2incident.io alternatives: Squadcast
GLM 4.7 FlashXRootly, incident.io alternatives: Jira Service Management, PagerDuty
GPT-6 Lunaincident.io alternatives: PagerDuty, Rootly

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
Freshservice leads by two points.
Freshservice2%#8 of 13
Grafana IRM0%#10 of 13
The full small business standing →
Mid-marketThe figures above
Freshservice leads by three points.
Freshservice10%#3 of 11
Grafana IRM7%#5 of 11
The full mid-market standing →
Enterprise
Level: the same share of first choices.
Freshservice0%#– of 9
Grafana IRM0%#– 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 Grafana IRM

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

“Avoid Grafana OnCall Open Source” Gemini 3.5 Flash · paraphrase prompt · hard negative
“open-source version entered maintenance mode in March 2025 and is being replaced by Grafana Cloud IRM” Muse Glimmer 30B · paraphrase prompt · soft negative
“be cautious if you're not already committed to that observability ecosystem” GPT-5.4 mini · negative prompt · soft negative
“start with Grafana OnCall if you're already in the Grafana ecosystem” Kimi K2 · budget prompt · first choice
“the pragmatic pick is Squadcast or Grafana Cloud IRM for cost control” Muse Glimmer 30B · paraphrase prompt · first choice
“🏆 Top Pick: Grafana OnCall (Free / Open Source)” DeepSeek V4 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.