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AIOps platforms · October 2026 Edition

New Relic vs PagerDuty AIOps

Five of fourteen models named New Relic first on the direct prompt; zero named PagerDuty AIOps. New Relic was named by twelve of the fourteen models and PagerDuty AIOps by seven and New Relic carries 36 labels and PagerDuty AIOps 10, so the shares are not directly comparable.

New Relic

accepted challenger

Named in eight categories this edition.

PagerDuty AIOps

criticized challenger

Named in one category this edition.

First-choice share17%6%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%30%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#5A position in a field of 10; printed, not drawn.
Labels3610A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, New Relic reading right to left. Rank and label count are printed, not drawn.Datadog was named alongside these two in nine of the fourteen direct answers. New Relic vs Datadog · New Relic vs Dynatrace · New Relic vs BigPanda

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 aiops platforms page.

By framing

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
New RelicFirst choices, of fourteen modelsPagerDuty AIOps
Direct50
Paraphrase03
Comparative00
Budget-constrained401 against PagerDuty AIOps
Scale-constrained00
Negative001 against New Relic · 2 against PagerDuty AIOps
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 New Relic and PagerDuty AIOps 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
New Relic PagerDuty AIOps first choice named as an alternative argued againstblank: not namedEach cell is one answer, New Relic on the left and PagerDuty AIOps on the right.

The direct prompt

The plain question, one answer per model, grouped by where New Relic and PagerDuty AIOps stood in it.

New Relic first, PagerDuty AIOps not the choice

5 of 14 modelsPagerDuty AIOps was named in the answer but not as the choice, or not at all.
Perplexity SonarNew Relic alternatives: Coralogix
DeepSeek V4 FlashLogicMonitor, New Relic alternatives: BigPanda, Freshservice, PagerDuty
Kimi K2New Relic alternatives: Datadog, LogicMonitor
MiniMax M2.5Datadog, New Relic alternatives: Dynatrace
Muse Glimmer 30BDatadog, Dynatrace, New Relic alternatives: Freshservice

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.
Claude Haiku 4.5PagerDuty alternatives: BigPanda, LogicMonitor, New Relic
GPT-5.4 miniDynatrace alternatives: Datadog, New Relic, Splunk
Gemini 3.5 FlashDatadog, LogicMonitor alternatives: New Relic, PagerDuty
Grok 4.1 FastDatadog alternatives: New Relic
Mistral SmallDynatrace alternatives: BigPanda, LogicMonitor, New Relic
GLM 4.7 FlashXDynatrace alternatives: Datadog, Freshservice, ManageEngine OpManager Nexus, New Relic
GPT-6 LunaDatadog alternatives: Dynatrace, New Relic

Neither was named

2 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 MaverickDynatrace
Qwen 3.7 FlashDynatrace alternatives: BigPanda, Datadog, Elastic Observability, Grafana Cloud, OpenTelemetry

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
New Relic leads by five points.
New Relic12%#2 of 16
PagerDuty AIOps7%#6 of 16
The full small business standing →
Mid-marketThe figures above
New Relic leads by eleven points.
New Relic17%#1 of 10
PagerDuty AIOps6%#5 of 10
The full mid-market standing →
Enterprise
New Relic leads by two points.
New Relic2%#6 of 11
PagerDuty AIOps0%#– of 11
The full enterprise standing →

What the models said about New Relic

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

“New Relic (The Best SaaS Free-Tier Option) ... Its 100 GB/month free tier includes full-access AIOps and APM tools.” Gemini 3.5 Flash · budget prompt · first choice

What the models said about PagerDuty AIOps

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

“It charges by accepted events, so model your real event stream—including spikes and maintenance windows—before signing.” GPT-6 Luna · negative prompt · soft negative
“though some users find per-user costs steep, so it's best to evaluate their pricing carefully” Mistral Small · budget prompt · soft negative
“Users report steep pricing and a "really struggling in the current market" vibe” GLM 4.7 FlashX · negative prompt · soft negative
“For a typical mid-sized B2B company, PagerDuty AIOps offers the best balance of capability, cost transparency, and ease of deployment.” DeepSeek V4 Flash · paraphrase prompt · first choice
“I'd generally recommend PagerDuty AIOps / Signal Intelligence as the best starting point” GPT-5.4 mini · paraphrase prompt · first choice
“My default recommendation: PagerDuty AIOps” GPT-6 Luna · paraphrase 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.