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AIOps platforms · October 2026 Edition

New Relic vs BigPanda

Five of fourteen models named New Relic first on the direct prompt; zero named BigPanda. New Relic was named by twelve of the fourteen models and BigPanda by fourteen and New Relic carries 36 labels and BigPanda 47, so the shares are not directly comparable.

New Relic

accepted challenger

Named in eight categories this edition.

BigPanda

accepted challenger

Named in two categories this edition.

First-choice share17%11%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%15%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#4A position in a field of 10; printed, not drawn.
Labels3647A 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 PagerDuty AIOps

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 modelsBigPanda
Direct50
Paraphrase062 against BigPanda
Comparative00
Budget-constrained402 against BigPanda
Scale-constrained00
Negative001 against New Relic · 3 against BigPanda
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 BigPanda 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 BigPanda first choice named as an alternative argued againstblank: not namedEach cell is one answer, New Relic on the left and BigPanda on the right.

The direct prompt

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

New Relic first, BigPanda an alternative

5 of 14 modelsBigPanda 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

8 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
Qwen 3.7 FlashDynatrace alternatives: BigPanda, Datadog, Elastic Observability, Grafana Cloud, OpenTelemetry
GLM 4.7 FlashXDynatrace alternatives: Datadog, Freshservice, ManageEngine OpManager Nexus, New Relic
GPT-6 LunaDatadog alternatives: Dynatrace, New Relic

Neither was named

1 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 MaverickDynatrace

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 eight points.
New Relic12%#2 of 16
BigPanda3%#9 of 16
The full small business standing →
Mid-marketThe figures above
New Relic leads by six points.
New Relic17%#1 of 10
BigPanda11%#4 of 10
The full mid-market standing →
Enterprise
The order flips: BigPanda leads at enterprise.
BigPanda28%#2 of 11
New Relic2%#6 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 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
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.