Datadog vs New Relic
Eleven of fourteen models named Datadog first on the direct prompt; four named New Relic. Both were named by all fourteen models and Datadog carries 76 labels and New Relic 69, so the shares are not directly comparable.
Named in nine categories this edition.
Named in nine 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 observability platforms page.
By framing
DatadogFirst choices, of fourteen modelsNew Relic
Across every category in the November 2026 Edition, Datadog and New Relic were named in the same answer 401 times, of the 692 answers naming Datadog and the 709 naming New Relic. In those answers New Relic took the first choice thirty-seven times and Datadog eighty-eight.
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 4.20 | | | | | | |
| 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 New Relic first choice named as an alternative argued againstblank: not namedEach cell is one answer, Datadog on the left and New Relic on the right.The direct prompt
Both were the first choice
3 of 14 modelsThe answer named them together, and the judge labeled each a first choice.Datadog first, New Relic an alternative
8 of 14 modelsNew Relic was named in the answer but not as the choice, or not at all.New Relic first, Datadog an alternative
1 of 14 modelsDatadog was named in the answer but not as the choice, or not at all.Neither was the first choice, one was named
1 of 14 modelsThe answer put something else first and named one of the two as an alternative.Neither was named
1 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
New Relic27%#2 of 13Datadog11%#4 of 13Datadog27%#2 of 10New Relic25%#3 of 10Datadog38%#1 of 10New Relic16%#3 of 10What the models said about Datadog
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Three of three in this category shown.
“Alternatives to Avoid on Tight Budgets: High-end leaders like Datadog or Splunk excel in features but often lead to bill shock” Grok 4.20 · budget prompt · hard negative
“High Caution: Datadog ... Surprise billing - Complex, à la carte pricing across 40+ products that compounds unpredictably” Kimi K2 · negative prompt · hard negative
“Start with Datadog if your team is already deeply invested in AWS/Azure/GCP ecosystems and you value a rich ecosystem of integrations above all else.” Qwen 3.7 Flash · direct prompt · first choice
What the models said about New Relic
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Four of four in this category shown.
“New Relic (November 2023): Security breach involving unauthorized access to staging environment, credential theft, and social engineering.” MiniMax M2.5 · negative prompt · hard negative
“Avoid platforms like Datadog, New Relic, or Dynatrace initially” MiniMax M2.5 · budget prompt · hard negative
“New Relic/Datadog - Avoid for tight budgets” Llama 4 Maverick · negative prompt · hard negative
“For most mid-market B2B companies, New Relic often provides the best balance of features, pricing predictability, and ease of use” GLM 4.7 FlashX · direct 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.