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Observability platforms · November 2026 Edition

SigNoz vs Honeycomb

Zero of fourteen models named SigNoz first on the direct prompt; one named Honeycomb. SigNoz was named by thirteen of the fourteen models and Honeycomb by fourteen and both carry 29 labels, so the shares below are directly comparable.

SigNoz

accepted challenger

Named in eight categories this edition.

Honeycomb

accepted challenger

Named in five categories this edition.

First-choice share5%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate3%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#4#6A position in a field of 10; printed, not drawn.
Labels2929Equal, which is what makes the shares comparable.
The two percentage rows are drawn on one 0 to 100 track, SigNoz reading right to left. Rank and label count are printed, not drawn.Datadog was named alongside these two in thirteen of the fourteen direct answers. Grafana vs SigNoz · Grafana vs Honeycomb · Datadog vs SigNoz

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

How many of the fourteen models made each the first choice, per way of asking, and how many argued against it.
SigNozFirst choices, of fourteen modelsHoneycomb
Direct01
Paraphrase00
Comparative00
Budget-constrained30
Scale-constrained00
Negative001 against SigNoz
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.

Across every category in the November 2026 Edition, SigNoz and Honeycomb were named in the same answer forty-seven times, of the 211 answers naming SigNoz and the 140 naming Honeycomb. In those answers Honeycomb took the first choice one time and SigNoz five.

Every model, every framing

The eighty-four answers behind the chart above, one cell each: where SigNoz and Honeycomb stood in it.
ModelDirectParaphraseComparativeBudget-constrainedScale-constrainedNegative
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
SigNoz Honeycomb first choice named as an alternative argued againstblank: not namedEach cell is one answer, SigNoz on the left and Honeycomb on the right.

The direct prompt

The plain question, one answer per model, grouped by where SigNoz and Honeycomb stood in it.

Honeycomb first, SigNoz not the choice

1 of 14 modelsSigNoz was named in the answer but not as the choice, or not at all.
Kimi K2Grafana, Honeycomb

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 alternatives: Grafana, Honeycomb, New Relic
Perplexity SonarDatadog alternatives: Grafana, New Relic, SigNoz
DeepSeek V4 FlashDatadog alternatives: Grafana, Honeycomb, New Relic, SigNoz
Llama 4 MaverickDatadog alternatives: New Relic, SigNoz, SolarWinds Observability

Neither was named

9 of 14 modelsThe answer made no first choice from these two in this category.
Claude Haiku 4.5Datadog, Grafana, New Relic
GPT-5.4 miniNew Relic alternatives: Datadog, Grafana
Grok 4.20Datadog alternatives: Grafana, LogicMonitor, New Relic
Mistral SmallDatadog, New Relic alternatives: Grafana, IBM Instana
Qwen 3.7 FlashDatadog alternatives: Dynatrace, New Relic
GLM 4.7 FlashXDatadog, New Relic alternatives: Splunk Observability Cloud
MiniMax M2.5Grafana, LogicMonitor alternatives: Cribl Stream, Datadog
GPT-6 LunaDatadog alternatives: Grafana, New Relic
Muse Glimmer 30BDatadog alternatives: Grafana, LogicMonitor, New Relic

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
SigNoz leads by eleven points.
SigNoz11%#3 of 13
Honeycomb0%#8 of 13
The full small business standing →
Mid-marketThe figures above
SigNoz leads by three points.
SigNoz5%#4 of 10
Honeycomb2%#6 of 10
The full mid-market standing →
Enterprise
Level: the same share of first choices.
SigNoz0%#– of 10
Honeycomb0%#9 of 10
The full enterprise standing →

What the models said about SigNoz

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

“Signoz | Lacking documentation, inadequate customer support” MiniMax M2.5 · negative prompt · soft negative
“Move to SigNoz Cloud when you need more capacity—it offers the best balance of simplicity, features, and predictable pricing” MiniMax M2.5 · budget prompt · first choice
“SigNoz (Best All-Around Value & Simplicity) ... widely considered the best open-source alternative to Datadog” Qwen 3.7 Flash · budget prompt · first choice
“The best observability platform for a company with a limited budget is SigNoz” Llama 4 Maverick · budget prompt · first choice

What the models said about Honeycomb

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

“I'd suggest Grafana Cloud or Honeycomb as the starting point” Kimi K2 · direct prompt · first choice
“For High-Cardinality & Developer-Centric APM: Honeycomb (predictable event-based pricing, excellent for distributed tracing)” Gemini 3.5 Flash · negative prompt · alternative
“The Engineering-First Choice: Honeycomb ... Trade-off: It lacks some of the "batteries included" infrastructure features” Qwen 3.7 Flash · paraphrase prompt · alternative
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.