AIOps platforms · October 2026 Edition
Datadog vs BigPanda
Five of fourteen models named Datadog first on the direct prompt; zero named BigPanda. Datadog was named by thirteen of the fourteen models and BigPanda by fourteen and Datadog carries 40 labels and BigPanda 47, so the shares are not directly comparable.
Named in eight categories this edition.
Named in two categories this edition.
First-choice share15%11%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate15%15%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#2#4A position in a field of 10; printed, not drawn.
Labels4047A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Datadog 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 Datadog · New Relic vs BigPanda · Datadog vs Dynatrace
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.
DatadogFirst choices, of fourteen modelsBigPanda
Direct501 against Datadog
Paraphrase362 against BigPanda
Comparative20
Budget-constrained002 against Datadog · 2 against BigPanda
Scale-constrained00
Negative003 against Datadog · 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 Datadog and BigPanda 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 | | | | | |
Datadog BigPanda first choice named as an alternative argued againstblank: not namedEach cell is one answer, Datadog on the left and BigPanda on the right.The direct prompt
The plain question, one answer per model, grouped by where Datadog and BigPanda stood in it.
Datadog first, BigPanda not the choice
5 of 14 modelsBigPanda was named in the answer but not as the choice, or not at all.Gemini 3.5 FlashDatadog, LogicMonitor alternatives: New Relic, PagerDuty
Grok 4.1 FastDatadog alternatives: New Relic
MiniMax M2.5Datadog, New Relic alternatives: Dynatrace
GPT-6 LunaDatadog alternatives: Dynatrace, New Relic
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
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
Kimi K2New Relic alternatives: Datadog, LogicMonitor
GLM 4.7 FlashXDynatrace alternatives: Datadog, Freshservice, ManageEngine OpManager Nexus, New Relic
Neither was named
2 of 14 modelsThe answer made no first choice from these two in this category.Perplexity SonarNew Relic alternatives: Coralogix
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
Datadog leads by seven points.
Datadog10%#3 of 16BigPanda3%#9 of 16Mid-marketThe figures above
Enterprise
The order flips: BigPanda leads at enterprise.
BigPanda28%#2 of 11Datadog4%#4 of 11What the models said about Datadog
No label in this category carried a quote.
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.