Small businessnewNew since September 2026: not ranked then, 0% now.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 50 of 59, unchanged
Elasticsearch
64Judge labels
1First choices
27Negative labels
14 / 14Models named it
6Categories
October 2026 Edition. Every number here is derived from the raw labels under vendor table v2026-10.8, every buyer segment counted.
Best standing
2% in Vector databases for enterprise buyers
Rank 27 of 73 in the mid-market standing
0 of 14 models made it the first choice on the direct prompt; 25% of its 8 labels there were negative.
By buyer segmentRead the same way at every buyer size.
Small business52 of 60
0%3 labels
under the label threshold
Mid-market27 of 73
0%8 labels
under the label threshold
Enterprise49 of 54
0%2 labels
under the label threshold
In nosql · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading
Standing by category
Every category where a model named Elasticsearch for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.
| Category | Function | Share | Rank | Negative rate | Labels | Quadrant | Since September 2026 |
|---|---|---|---|---|---|---|---|
| NoSQL databases | Data platform | 0% | 27 of 73 | 25% | 8 | under 10 labels · led by MongoDB at 74% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Vector databases | Data platform | 0% | 13 of 44 | 12% | 8 | under 10 labels · led by pgvector at 35% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. |
| Telemetry pipelines | IT operations and endpoint | 0% | 121 of 124 | 60% | 5 | under 10 labels · led by OpenTelemetry Collector at 37% | |
| Observability platforms | IT operations and endpoint | 0% | 62 of 80 | 50% | 4 | under 10 labels · led by Grafana at 27% | newNew since September 2026: not ranked then, 0% now. |
| Application performance monitoring | IT operations and endpoint | 0% | 33 of 66 | 0% | 1 | under 10 labels · led by New Relic at 46% | newNew since September 2026: not ranked then, 0% now. |
| Time series databases | Data platform | 0% | 42 of 66 | 100% | 1 | under 10 labels · led by TimescaleDB at 63% |
Movement
Since September 2026, Elasticsearch held within the floor in all six categories: no change cleared 11 points.
Best category · NoSQL · by segment
Mid-market=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 47 → 22 of 69
Enterprise=heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked.
0%from 0% in September 2026
Inside the floor by 11 pointsRank 27 → 44 of 53
All categories · Mid-market
| Category | September 2026 | Now | Change | Reading | Rank |
|---|---|---|---|---|---|
| NoSQL | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 47 → 27 of 73 |
| Vector databases | 0% | 0% | =heldSince September 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise. Read over the models both editions asked. | Within noise | Rank 23 → 13 of 44 |
| Telemetry pipelines | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 121 of 124, unchanged |
| Observability | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 62 of 80, unchanged |
| APM | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 33 of 66, unchanged |
| Time series DBs | 0% | 0% | newNew since September 2026: not ranked then, 0% now. | New this edition | Rank 42 of 66, unchanged |
Shares here are read over the models both editions asked, so they can differ by a point or two from the standing above, which counts every model in this edition.
The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one is noise, and both are shown. Movement is read over the twelve models both editions asked; GPT-6 Luna, Muse Glimmer 30B joined this edition and are in the standing but not yet in the comparison. How the floor is measured · The editions
By model
How each model treated Elasticsearch across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.ShowHide
By model
How each model treated Elasticsearch across every prompt where it was named for a mid-market B2B company. Fourteen models, six prompts per category.
ShowHide| Model | First choice | Alternative | Mention | Negative | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 0 | 0 | 0 |
| GPT-5.4 mini | 0 | 0 | 1 | 0 | 1 |
| Gemini 3.5 Flash | 0 | 0 | 1 | 3 | 4 |
| Perplexity Sonar | 0 | 2 | 0 | 0 | 2 |
| Grok 4.20 | 0 | 0 | 4 | 0 | 4 |
| Mistral Small | 0 | 0 | 2 | 0 | 2 |
| DeepSeek V4 Flash | 0 | 0 | 0 | 0 | 0 |
| Llama 4 Maverick | 0 | 0 | 0 | 0 | 0 |
| Qwen 3.7 Flash | 0 | 2 | 1 | 5 | 8 |
| Kimi K2 | 0 | 0 | 0 | 0 | 0 |
| GLM 4.7 FlashX | 0 | 1 | 1 | 0 | 2 |
| MiniMax M2.5 | 0 | 1 | 1 | 1 | 3 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 0 | 0 | 0 | 0 |
By framing
Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.
FramingLabels by classFirst choices
Direct8 labelsNone
Paraphrase5 labels1
Comparative12 labelsNone
Budget-constrained7 labelsNone
Scale-constrained11 labelsNone
Negative21 labelsNone
First choiceAlternativeMentionNegative64 labels in all, every segment counted; 1 of the 1 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.
What the models said for it
Verbatim evidence the judge attached to positive labels.
“Need keyword + semantic search in one system? Weaviate or Elasticsearch” GPT-6 Luna · Vector databases · comparative prompt · alternative
“Elasticsearch/Loki/Jaeger (Logs/Traces). This eliminates the monthly "tax"” Qwen 3.7 Flash · Observability · negative prompt · alternative
“Elastic creates powerful correlations between logs, metrics, and traces” Qwen 3.7 Flash · APM · paraphrase prompt · alternative
“Elasticsearch if your primary need is search and log analytics.” Perplexity Sonar · NoSQL · comparative prompt · alternative
And against it
Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.
“Do not use ElasticSearch as your primary "source of truth"... It is strictly for indexing and searching, not for authoritative data storage.” Qwen 3.7 Flash · NoSQL · negative prompt · hard negative
“Caution: Relying entirely on Logstash or heavy Elasticsearch clusters for simple filtering before storage.” Qwen 3.7 Flash · Telemetry pipelines · negative prompt · soft negative
“Elasticsearch is not transactional... can quietly drop writes or suffer from data corruption.” Gemini 3.5 Flash · NoSQL · negative prompt · soft negative
“Running Elasticsearch solely for vectors is often less efficient than using a specialized tool.” Qwen 3.7 Flash · Vector databases · negative prompt · soft negative
Named alongside
The products named in the same answers as Elasticsearch, over the 64 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Elasticsearch was named but was not.ShowHide
Named alongside
The products named in the same answers as Elasticsearch, over the 64 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Elasticsearch was named but was not.
ShowHideProductSame answerTook the first choice insteadHead to head
A head-to-head page exists where both products are among a category's top eight. The other rows are the same fact without a page behind them, so they link to the product instead.
What carried it into the answer
The sites and pages cited by the answers that named Elasticsearch. A fact about retrieval, not a lever on the model.
Citations exist only for the models that return a source list, five of the fourteen in this edition, so these counts come from 46 of the 64 answers that named Elasticsearch and are not a share of its labels.
Domains cited
dev.to15
atlan.com13
g2.com13
marktechpost.com12
linkedin.com11
iternal.ai10
aloa.co9
github.com9
medium.com8
reddit.com8
No domain is on file for Elasticsearch, so its own site is not marked.
Pages cited
Pages are listed as the models cited them.
Search and answers
Where Elasticsearch stands in Google search beside where it stands in the models' answers.ShowHide
Search and answers
Where Elasticsearch stands in Google search beside where it stands in the models' answers.
ShowHideIn search
Google, US estimates- Searches for its name, Google
- 12,100 a month (“elasticsearch”)
- AI search demand for its name, est.
- 2,798 a month
- Its own site
- No site of its own on file, so no site figures
In answers
This editionShare of first choices
0%
rank 27 of 73 in nosql
Segment leader
74%
MongoDB
- First choices
- 1 across its categories
- Named in
- 64 answers
- Its own site cited
- No site on file to match
Search figures are US estimates from DataForSEO, read September 28, 2026; AI search demand is its modeled, directional estimate, not a count of queries to any assistant. The answers are this edition's. Two measurements side by side: neither is read as the cause of the other.
Names read as Elasticsearch
What the judge wrote, as written, with how often. The vendor table decides that these count as Elasticsearch; a claim can dispute any of them.
ELK/Elasticsearch 1ElasticSearch 1Elasticsearch (Elastic Observability) 1Elasticsearch (with vector search plugins) 1Elasticsearch/Loki/Jaeger 1
Follow Elasticsearch
An email the morning each edition publishes: where this product moved, where it held, and by how much against the noise floor. One address, confirmed by a click; a stop link in every email.
Already following? Everything you follow, with a stop for each.
Is this your product?
Claim this page
Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when Elasticsearch's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as Elasticsearch, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.
What a new claim receivesHide what a new claim receives
A new claim receives the current edition's vendor brief for Elasticsearch by email, built from the raw record of the edition. It shows:
- where Elasticsearch is named, by buyer and by framing, and which cells hold its first choices;
- the claims the models make when they name it, ranked, with the strongest and the weakest quoted;
- its vocabulary against the segment leader's, and the pages the models cited;
- who was chosen in the answers that did not name Elasticsearch, and every reason the record gives;
- a battlecard for each top rival: the head-to-head split, why they win, and the reservation quoted against them;
- one page of published figures cleared to show a buyer.