AIOps platforms · October 2026 Edition
BigPanda vs PagerDuty
Zero of fourteen models named BigPanda first on the direct prompt; one named PagerDuty. BigPanda was named by fourteen of the fourteen models and PagerDuty by seven and BigPanda carries 47 labels and PagerDuty 14, so the shares are not directly comparable.
Named in two categories this edition.
Named in four categories this edition.
First-choice share11%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate15%7%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#7A position in a field of 10; printed, not drawn.
Labels4714A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, BigPanda reading right to left. Rank and label count are printed, not drawn.New Relic was named alongside these two in twelve of the fourteen direct answers. New Relic vs BigPanda · New Relic vs PagerDuty · Datadog 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.
BigPandaFirst choices, of fourteen modelsPagerDuty
Direct01
Paraphrase602 against BigPanda
Comparative00
Budget-constrained002 against BigPanda
Scale-constrained00
Negative003 against BigPanda · 1 against PagerDuty
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 BigPanda and PagerDuty stood in it.
| 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 | | | |
BigPanda PagerDuty first choice named as an alternative argued againstblank: not namedEach cell is one answer, BigPanda on the left and PagerDuty on the right.The direct prompt
The plain question, one answer per model, grouped by where BigPanda and PagerDuty stood in it.
PagerDuty first, BigPanda an alternative
1 of 14 modelsBigPanda was named in the answer but not as the choice, or not at all.Claude Haiku 4.5PagerDuty alternatives: BigPanda, LogicMonitor, New Relic
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.Gemini 3.5 FlashDatadog, LogicMonitor alternatives: New Relic, PagerDuty
Mistral SmallDynatrace alternatives: BigPanda, LogicMonitor, New Relic
DeepSeek V4 FlashLogicMonitor, New Relic alternatives: BigPanda, Freshservice, PagerDuty
Qwen 3.7 FlashDynatrace alternatives: BigPanda, Datadog, Elastic Observability, Grafana Cloud, OpenTelemetry
Neither was named
9 of 14 modelsThe answer made no first choice from these two in this category.GPT-5.4 miniDynatrace alternatives: Datadog, New Relic, Splunk
Perplexity SonarNew Relic alternatives: Coralogix
Grok 4.1 FastDatadog alternatives: New Relic
Llama 4 MaverickDynatrace
Kimi K2New Relic alternatives: Datadog, LogicMonitor
GLM 4.7 FlashXDynatrace alternatives: Datadog, Freshservice, ManageEngine OpManager Nexus, New Relic
MiniMax M2.5Datadog, New Relic alternatives: Dynatrace
GPT-6 LunaDatadog alternatives: Dynatrace, New Relic
Muse Glimmer 30BDatadog, Dynatrace, New Relic alternatives: Freshservice
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
Level: the same share of first choices.
BigPanda3%#9 of 16PagerDuty3%#8 of 16Mid-marketThe figures above
Enterprise
BigPanda leads by twenty-eight points.
BigPanda28%#2 of 11PagerDuty0%#10 of 11What the models said about BigPanda
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Avoids the high custom pricing of specialists like BigPanda ($6K+/year base, often $60K+ first year for mid setups)” Grok 4.1 Fast · paraphrase prompt · soft negative
“BigPanda is primarily an alert correlation and deduplication engine, not a full-stack observability platform.” Qwen 3.7 Flash · negative prompt · soft negative
“open-source alternative to expensive event-correlation tools like BigPanda or Splunk ITSI” Gemini 3.5 Flash · budget prompt · soft negative
“Dynatrace and BigPanda are often recommended due to their scalability, ease of integration, and strong noise reduction features” Mistral Small · paraphrase prompt · first choice
“My primary recommendation would be BigPanda if budget allows, as it offers the most comprehensive AI-powered correlation.” Claude Haiku 4.5 · paraphrase prompt · first choice
“If you're already using multiple monitoring tools and alert noise is your biggest problem → BigPanda or Moogsoft” Kimi K2 · paraphrase prompt · first choice
What the models said about PagerDuty
No label in this category carried a quote.
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.