Grafana IRM vs Rootly
Zero of fourteen models named Grafana IRM first on the direct prompt; one named Rootly. Grafana IRM was named by ten of the fourteen models and Rootly by twelve and Grafana IRM carries 22 labels and Rootly 33, so the shares are not directly comparable.
Named in one category 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
Grafana IRMFirst choices, of fourteen modelsRootly
Across every category in the October 2026 Edition, Grafana IRM and Rootly were named in the same answer twenty times, of the 43 answers naming Grafana IRM and the 106 naming Rootly. In those answers Rootly took the first choice three times and Grafana IRM one.
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 | | | | |
Grafana IRM Rootly first choice named as an alternative argued againstblank: not namedEach cell is one answer, Grafana IRM on the left and Rootly on the right.The direct prompt
Rootly first, Grafana IRM not the choice
1 of 14 modelsGrafana IRM was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
6 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
7 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
Rootly7%#5 of 13Grafana IRM0%#10 of 13Grafana IRM7%#5 of 11Rootly5%#7 of 11Grafana IRM0%#– of 9Rootly0%#7 of 9What 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
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
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.