Elasticsearch
Read the same way at every company size.
The standing depends on who is buying. Every model is asked the same questions for each company size.
pgvector is first at 35%.
Qdrant is first at 34%. 0 of 14 models made it the first choice when asked directly.
Pinecone is first at 45%.
Why models recommend it
“1. Elasticsearch: Best for combining vector, full-text, and log search.”Llama 4 Maverick · Paraphrase question · first choice
“Your existing search platform—for example, Elasticsearch—if you already use it and need lexical search, filters, and vector retrieval in one system.”GPT-6 Luna · Scale-constrained question · alternative
“Databases like MongoDB or Cassandra aren’t optimized for complex search. Use Elasticsearch or specialized search engines.”MiniMax M2.5 · Negative question · alternative
“Elasticsearch / OpenSearch | Best if you already run it for search; good hybrid search capabilities”DeepSeek V4 Flash · Scale-constrained question · alternative
What gives them pause
“Tools like Elasticsearch (often paired with NoSQL) or standalone Cassandra clusters require significant DevOps overhead to maintain.”Qwen 3.7 Flash · Comparative question · warning
“Traditional log engines like Elasticsearch/OpenSearch build heavy inverted indexes ... driving up disk and RAM costs massively”Gemini 3.5 Flash · Budget-constrained question · warning
“Using open-source tools like Elasticsearch, Graylog, or Wazuh stitched together... often fail compliance audits”MiniMax M2.5 · Negative question · warning
“Using ES for pure time-series metrics leads to massive disk consumption compared to specialized databases.”Qwen 3.7 Flash · Negative question · warning
Also on the shortlist
The products named in the same answers as Elasticsearch, over the 86 answers that named it, and how often each was the first choice instead.
| Product | Named together | Picked instead | |
|---|---|---|---|
| 34 | 13 | Compare → | |
| 32 | 7 | Compare → | |
| 28 | 1 | Compare → | |
| 25 | 6 | Compare → | |
| 24 | 1 | View → | |
| 22 | 9 | View → | |
| 22 | 1 | View → | |
| 18 | 0 | View → | |
| 17 | 0 | Compare → | |
| 16 | 0 | View → |
What the models named instead of Elasticsearch →
A side-by-side page exists where both products are among a category's top eight.
How this is measured
Fourteen AI models from 12 labs are asked the same buying questions six ways, for three company sizes. Standings refresh every edition.
Gane sells AI visibility software. No company can pay to change a result.
Read the method →Sources returned with these answers
Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted. These counts come from 60 of the 86 answers that named Elasticsearch and are not a share of its labels.
Domains cited
No domain is on file for Elasticsearch, so its own site is not marked. Eleven were to the publisher's own pages (the Gane Index), which the models cite like any other source; they are not counted as a third party.
Pages cited
Pages are listed as the models cited them.
By model
| Model | First choice | Alternative | Mention | Warning | Labels |
|---|---|---|---|---|---|
| Claude Haiku 4.5 | 0 | 0 | 1 | 0 | 1 |
| GPT-5.4 mini | 0 | 0 | 0 | 1 | 1 |
| Gemini 3.5 Flash | 0 | 0 | 0 | 4 | 4 |
| Perplexity Sonar | 0 | 2 | 1 | 0 | 3 |
| Grok 4.20 | 0 | 3 | 3 | 1 | 7 |
| Mistral Small | 0 | 0 | 1 | 0 | 1 |
| DeepSeek V4 Flash | 0 | 1 | 1 | 0 | 2 |
| Llama 4 Maverick | 1 | 0 | 0 | 0 | 1 |
| Qwen 3.7 Flash | 0 | 0 | 2 | 3 | 5 |
| Kimi K2 | 0 | 1 | 2 | 1 | 4 |
| GLM 4.7 FlashX | 0 | 0 | 0 | 0 | 0 |
| MiniMax M2.5 | 0 | 3 | 3 | 1 | 7 |
| GPT-6 Luna | 0 | 1 | 0 | 0 | 1 |
| Muse Glimmer 30B | 0 | 0 | 1 | 0 | 1 |