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Observability platforms · November 2026 Edition

Grafana vs Honeycomb

Three of fourteen models named Grafana first on the direct prompt; one named Honeycomb. Both were named by all fourteen models and Grafana carries 74 labels and Honeycomb 29, so the shares are not directly comparable.

Grafana

endorsed leader

Named in nine categories this edition.

Honeycomb

accepted challenger

Named in five categories this edition.

First-choice share32%2%Of first choices across the direct, paraphrase, budget and scale prompts, 0 to 100.
Negative rate4%0%Negative labels as a share of the product's labels, 0 to 100.
Rank in category#1#6A position in a field of 10; printed, not drawn.
Labels7429A count; the two differ.
The two percentage rows are drawn on one 0 to 100 track, Grafana 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 Datadog · Grafana vs New Relic · Grafana 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.
GrafanaFirst choices, of fourteen modelsHoneycomb
Direct31
Paraphrase40
Comparative00
Budget-constrained110
Scale-constrained10
Negative203 against Grafana
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, Grafana and Honeycomb were named in the same answer seventy-two times, of the 332 answers naming Grafana and the 140 naming Honeycomb. In those answers Honeycomb took the first choice one time and Grafana twenty.

Every model, every framing

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

The direct prompt

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

Both were the first choice

1 of 14 modelsThe answer named them together, and the judge labeled each a first choice.
Kimi K2Grafana, Honeycomb

Grafana first, Honeycomb not the choice

2 of 14 modelsHoneycomb was named in the answer but not as the choice, or not at all.
Claude Haiku 4.5Datadog, Grafana, New Relic
MiniMax M2.5Grafana, LogicMonitor alternatives: Cribl Stream, Datadog

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.
GPT-5.4 miniNew Relic alternatives: Datadog, Grafana
Gemini 3.5 FlashDatadog alternatives: Grafana, Honeycomb, New Relic
Perplexity SonarDatadog alternatives: Grafana, New Relic, SigNoz
Grok 4.20Datadog alternatives: Grafana, LogicMonitor, New Relic
Mistral SmallDatadog, New Relic alternatives: Grafana, IBM Instana
DeepSeek V4 FlashDatadog alternatives: Grafana, Honeycomb, New Relic, SigNoz
GPT-6 LunaDatadog alternatives: Grafana, New Relic
Muse Glimmer 30BDatadog alternatives: Grafana, LogicMonitor, New Relic

Neither was named

3 of 14 modelsThe answer made no first choice from these two in this category.
Llama 4 MaverickDatadog alternatives: New Relic, SigNoz, SolarWinds Observability
Qwen 3.7 FlashDatadog alternatives: Dynatrace, New Relic
GLM 4.7 FlashXDatadog, New Relic alternatives: Splunk Observability Cloud

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
Grafana leads by thirty-eight points.
Grafana38%#1 of 13
Honeycomb0%#8 of 13
The full small business standing →
Mid-marketThe figures above
Grafana leads by thirty-one points.
Grafana32%#1 of 10
Honeycomb2%#6 of 10
The full mid-market standing →
Enterprise
Grafana leads by two points.
Grafana2%#5 of 10
Honeycomb0%#9 of 10
The full enterprise standing →

What the models said about Grafana

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

“Grafana OSS (with Prometheus, Loki, Tempo) or New Relic's free tier are typically the strongest options for a company with a limited budget.” Grok 4.20 · budget prompt · first choice
“1. Open-Source Stack (Most Economical) Prometheus + Grafana + OpenTelemetry - Cost: Completely free (open source)” GLM 4.7 FlashX · budget prompt · first choice
“Safer Alternatives to Consider | Grafana Cloud | Open source foundation, transparent pricing, strong OpenTelemetry” Kimi K2 · negative 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.