16 companieschecked Sep 12, 2026, 12:00 AM UTCdailyhow we measure this →CSV / JSON

As of

In front today

Weaviate

50.0%gemini recommendation prompt presence

Ahead of second place: 0%
Held by the top three: 41%
Middle of the board: 18.8%
Below half the leader: 8 of 16

Highest 50.0%Middle 18.8%

Rank 1one bar per published rowRank 16

8 checks behind each row · 100% confidence across rows.

Vector Databases brands in Gemini model API answers current rankings
Rank	Name	Gemini recommendation prompt presence	Mention coverage	Share of names	Cited as source	Change	Why
01	Weaviate	50.0%	30.56% · 11 / 36	15.94% · 11 / 69	8.57% · 3 / 35		This rank My rank
02	Qdrant	50.0%	25% · 9 / 36	13.04% · 9 / 69	8.57% · 3 / 35	1	This rank My rank
03	Zilliz Cloud / Milvus	37.5%	33.33% · 12 / 36	17.39% · 12 / 69	22.86% · 8 / 35	1	This rank My rank
04	Pinecone	37.5%	27.78% · 10 / 36	14.49% · 10 / 69	14.29% · 5 / 35	2	This rank My rank
05	Redis	25.0%	19.44% · 7 / 36	10.14% · 7 / 69	17.14% · 6 / 35		This rank My rank
06	Amazon OpenSearch Service	25.0%	13.89% · 5 / 36	7.25% · 5 / 69	11.43% · 4 / 35		This rank My rank
07	Chroma	25.0%	8.33% · 3 / 36	4.35% · 3 / 69	0% · 0 / 35	1	This rank My rank
08	Google Vertex AI Vector Search	25.0%	8.33% · 3 / 36	4.35% · 3 / 69	8.57% · 3 / 35	1	This rank My rank
09	Elasticsearch	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35	4	Showing My rank
10	Vespa	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	0% · 0 / 35	5	This rank My rank
11	Azure AI Search	12.5%	2.78% · 1 / 36	1.45% · 1 / 69	2.86% · 1 / 35	1	This rank My rank
12	Cloudflare Vectorize	12.5%	2.78% · 1 / 36	1.45% · 1 / 69	0% · 0 / 35	4	This rank My rank
13	Turbopuffer	12.5%	2.78% · 1 / 36	1.45% · 1 / 69	0% · 0 / 35	1	This rank My rank
14	MongoDB Atlas Vector Search	0.0%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35		This rank My rank
15	LanceDB	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	4	This rank My rank
16	Supabase Vector	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	7	This rank My rank

Primary brandPrompt intentAnswer enginePrompt contains

Run live test

Answer presence27.8%10/36 filtered answers mention or cite Pinecone

Selected-set share23.8%Share of answer-presences among the compared brands, not market share

Owned citation answers35 answer-level URL signals on reviewed owned domains

Engine breadth2/3Engines with at least one observed presence

Filtered competitive answer-presence comparison
Brand	Presence	Vs. primary	Selected share	Mentions	Cited answers	Owned URL signals	Engine breadth
Zilliz Cloud / Milvuszilliz.com	33.3%12/36	+5.6 pp	28.6%	23	8	8	3/3
Weaviateweaviate.io	30.6%11/36	+2.8 pp	26.2%	25	3	3	3/3
Pineconepinecone.io	27.8%10/36	BASE	23.8%	23	3	5	2/3
Qdrantqdrant.tech	25.0%9/36	-2.8 pp	21.4%	22	3	3	3/3

ANSWER-LEVEL OBSERVATIONS

Prompt × engine matrix

12 prompts · 36 immutable answer hashes · empty cells mean no compared brand was detected.

Brand presence by public prompt and answer engine
Public prompt	OpenAIopenai/gpt-4o-mini	Anthropicanthropic/claude-haiku-4-5	Geminigoogle/gemini-2.5-flash
01 What are the best managed vector database platforms for a production retrieval-augmented generation application?recommendation	PineconeWeaviateQdrantZilliz Cloud / Milvus 6 source URLs`aa989829`	No selected brand 0 source URLs`9fad189b`	PineconeWeaviateQdrantZilliz Cloud / Milvus 6 source URLs`7d5f3f4c`
02 Recommend an open-source vector database that an engineering team can self-host in its own cloud account.recommendation	Weaviate · C1Qdrant · C1Zilliz Cloud / Milvus · C1 6 source URLs`ba4a3aa4`	WeaviateQdrant · C1Zilliz Cloud / Milvus · C1 6 source URLs`b99c6762`	Weaviate · C1Qdrant · C1Zilliz Cloud / Milvus · C1 8 source URLs`50de5eca`
03 Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?recommendation	Zilliz Cloud / Milvus · C1 3 source URLs`87ec9273`	No selected brand 0 source URLs`ba722281`	No selected brand 8 source URLs`541d86f2`
04 What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking?recommendation	PineconeWeaviate 5 source URLs`0c2c9c05`	No selected brand 0 source URLs`f93a3b8e`	No selected brand 5 source URLs`780d110e`
05 Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.recommendation	PineconeWeaviateZilliz Cloud / Milvus · C1 6 source URLs`d4786d7b`	No selected brand 0 source URLs`62fcf9cf`	Weaviate · C1 7 source URLs`3089faad`
06 Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?recommendation	Pinecone · C3WeaviateQdrantZilliz Cloud / Milvus 7 source URLs`f0532c81`	No selected brand 0 source URLs`127b014a`	Pinecone · C1WeaviateQdrant 6 source URLs`760548bb`
07 Recommend a low-latency vector search system for a workload that may grow to billions of embeddings.recommendation	No selected brand 8 source URLs`e0a1c5ed`	No selected brand 0 source URLs`81ceee7c`	No selected brand 9 source URLs`6cf63b9a`
08 What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload?recommendation	PineconeWeaviateQdrant 6 source URLs`cbea3f62`	No selected brand 0 source URLs`ce0741bf`	PineconeQdrantZilliz Cloud / Milvus 7 source URLs`b9666f1b`
09 How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity?commercial	No selected brand 4 source URLs`66064c09`	No selected brand 0 source URLs`a1b600fd`	Pinecone 6 source URLs`93e40019`
10 When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database?commercial	Pinecone · C1 4 source URLs`b5213ed7`	No selected brand 0 source URLs`e706fa34`	No selected brand 7 source URLs`ed59023d`
11 Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?commercial	Zilliz Cloud / Milvus · C1 5 source URLs`d911b3cf`	No selected brand 0 source URLs`aca9c10c`	Zilliz Cloud / Milvus · C1 6 source URLs`8a897be1`
12 How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?commercial	No selected brand 7 source URLs`672a4fa7`	No selected brand 0 source URLs`e1a061c9`	Zilliz Cloud / Milvus · C1 3 source URLs`c8248eb7`

STRUCTURED CITATION LEDGER

Sources shaping this filtered view

Hostnames are derived from validated public HTTP(S) URLs, not provider-supplied domain labels.

Validated citation sources in filtered answers
Source hostname	Cited answers	Distinct URLs	Engine breadth	Best position
medium.com ↗	13	10	2	#1
reddit.com ↗	6	7	2	#2
arxiv.org ↗	6	6	2	#1
redis.io ↗	6	4	3	#1
milvus.io ↗	6	4	3	#2
rank.ai ↗	4	5	1	#1
firecrawl.dev ↗	4	1	3	#1
zenml.io ↗	4	1	3	#2
youtube.com ↗	3	3	2	#4
dev.to ↗	3	3	2	#5
pinecone.io ↗	3	2	2	#1
weaviate.io ↗	3	2	2	#3
feba-systeme.com ↗	3	1	2	#1
qdrant.tech ↗	3	1	3	#1
scylladb.com ↗	2	5	2	#1
docs.cloud.google.com ↗	2	2	1	#1
linkedin.com ↗	2	2	2	#1
zilliz.com ↗	2	2	2	#1
actian.com ↗	2	2	1	#2
facebook.com ↗	2	2	1	#2

PUBLIC-BENCHMARK PRIVACY BOUNDARY

This report contains only code-reviewed public prompts, deterministic brand counts, answer hashes, and validated structured citation URLs. Full model answers are neither stored nor published here. No customer, organization, or private tracked-prompt data is included.

vector-database-platforms-2026-07-27 / 4-vector-database-platforms-2026-07-27-models-64232fd32583

Published rank: #9
Score: 12.5 percent
Sample size: 8

100% confidence · 100% component coverage · as of Sep 12, 2026, 12:00 AM UTC

SCORE CONSTRUCTION

Component ledger

14/14 evidenced

All components available · 1 evidence record each

Components contributing to Elasticsearch's rank
Component	Value	Weight	Contribution
public prompt anthropic brand citation share	0	0%	—
public prompt anthropic brand mention coverage	0	0%	—
public prompt anthropic brand recommendation presence	0	0%	—
public prompt brand citation share	2.857143	0%	—
public prompt brand engine breadth	2	0%	—
public prompt brand mention coverage	5.555556	0%	—
public prompt brand mention share	2.898551	0%	—
public prompt brand recommendation presence	8.333333	0%	—
public prompt gemini brand citation share	6.25	0%	—
public prompt gemini brand mention coverage	8.333333	0%	—
public prompt gemini brand recommendation presence	12.5	100%	12.5
public prompt openai brand citation share	0	0%	—
public prompt openai brand mention coverage	8.333333	0%	—
public prompt openai brand recommendation presence	12.5	0%	—

WHY IT MOVED

up since prior snapshot

+4 ranks

public prompt gemini brand recommendation presence0 → 12.5
+12.5
public prompt gemini brand mention coverage0 → 8.333
+8.333
public prompt gemini brand citation share0 → 6.25
+6.25
public prompt brand recommendation presence4.167 → 8.333
+4.167
public prompt brand citation share0 → 2.857
+2.857
public prompt brand mention coverage2.778 → 5.556
+2.778
public prompt brand mention share1.429 → 2.899
+1.47
public prompt brand engine breadth1 → 2
+1
public prompt anthropic brand citation share0 → 0
0
public prompt anthropic brand mention coverage0 → 0
0
public prompt anthropic brand recommendation presence0 → 0
0
public prompt openai brand citation share0 → 0
0
public prompt openai brand mention coverage8.333 → 8.333
0
public prompt openai brand recommendation presence12.5 → 12.5
0

PRIMARY EVIDENCE

Source trail

14 public records

Anthropic citation sharepublic prompt anthropic brand citation share · observed Sep 12, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Anthropic mention coveragepublic prompt anthropic brand mention coverage · observed Sep 12, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Anthropic recommendation-prompt presencepublic prompt anthropic brand recommendation presence · observed Sep 12, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Citation sharepublic prompt brand citation share · observed Sep 12, 2026, 12:00 AM UTC2.857 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Engine breadthpublic prompt brand engine breadth · observed Sep 12, 2026, 12:00 AM UTC2 engines100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Mention coveragepublic prompt brand mention coverage · observed Sep 12, 2026, 12:00 AM UTC5.556 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Reviewed-brand presence sharepublic prompt brand mention share · observed Sep 12, 2026, 12:00 AM UTC2.899 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Recommendation-prompt presencepublic prompt brand recommendation presence · observed Sep 12, 2026, 12:00 AM UTC8.333 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Gemini citation sharepublic prompt gemini brand citation share · observed Sep 12, 2026, 12:00 AM UTC6.25 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Gemini mention coveragepublic prompt gemini brand mention coverage · observed Sep 12, 2026, 12:00 AM UTC8.333 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
Gemini recommendation-prompt presencepublic prompt gemini brand recommendation presence · observed Sep 12, 2026, 12:00 AM UTC12.5 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
{"corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["e33dd985-f176-4ab4-9454-aee96d33c088","dcbac240-1491-4352-842c-0806750555ae","0d37b2a2-eb3a-439a-a79f-41e84711c1a4","bf953d93-d4cf-4f60-a020-6d347d999323","071dbba4-9632-433a-b537-174e3f51b2de","ba80dea3-391d-4437-9459-e60a9d2ea270","9124e2fb-1ff5-4d4e-a5a4-f73819f680a0","0bc1d0cf-6fe6-4ac9-84ec-c865babfcab8","d5047f79-2f05-40f8-86a9-6cb4a813ed07","cef04144-25c1-44e7-ad6e-7f5f564b05ef","3585da81-4f5f-492f-9da3-a3fbf759d91d","1c6b58cf-7892-4808-93ae-6dbb60ecc298","b783808a-2cf8-431f-9345-d4b420ff2cec","08d3b7f5-a9d2-4e9e-9c5c-f6556f5ddbb8","f705d98a-5806-4721-a721-a502ef397f5c","0b1394c3-a8ca-47b3-a187-684d6a145034","3c9d31d8-537e-40f2-b7f2-216c0859da16","080de0fb-96ee-44d0-96a5-51bc5ee55213","54094093-8713-40cf-a8b0-49631e8d91a6","7b904caf-c982-47d4-8de3-1d1ccd86b56e","8e5d85b5-7830-4473-8e40-c607c13b54ba","2047a3bb-e040-4b7b-82b6-bde729dd621a","8588f880-47ff-4daf-8bc6-f38d0d042e19","5c0d184c-90d6-4362-a70f-146080a509e7","4ae3f702-03fe-4114-bf19-3e004b674d38","38d5e84f-c1cd-4974-8d4d-7a0f20cfb88d","2f773a7d-d6f0-4f03-ace9-469ec5ced46d","a8d43bb0-c5f0-45a3-956f-a653df389bdc","9e4ec911-0582-44fe-9676-7a41f7739e01","0e2afed9-0e73-47fa-b652-54438e0c6550","e61d72df-b2a3-4fcb-b5b9-74f69aebac68","f34c5a15-9290-4144-9dcd-f5100777220c","8502a803-448b-41df-8089-513d17573c3e","1469281a-da62-4e78-8a6d-ed5d882547cf","bae7de20-12db-4bb0-80ec-91c424506360","97ece4d4-251c-4d3a-9366-6e5fe4bd799c"],"benchmarkVersion":"4-vector-database-platforms-2026-07-27-models-64232fd32583","providerMatrixVersion":"64232fd32583"}
Open primary evidence ↗
OpenAI citation sharepublic prompt openai brand citation share · observed Sep 12, 2026, 12:00 AM UTC0 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open primary evidence ↗
OpenAI mention coveragepublic prompt openai brand mention coverage · observed Sep 12, 2026, 12:00 AM UTC8.333 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open primary evidence ↗
OpenAI recommendation-prompt presencepublic prompt openai brand recommendation presence · observed Sep 12, 2026, 12:00 AM UTC12.5 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Plain textVector Databases Brands in Gemini Model API Answers. Rank.ai, data as of September 12, 2026. https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-gemini-visibility
HTML<a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-gemini-visibility">Vector Databases Brands in Gemini Model API Answers</a> — Rank.ai, data as of September 12, 2026
Markdown[Vector Databases Brands in Gemini Model API Answers](https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-gemini-visibility) — Rank.ai, data as of September 12, 2026
APARank.ai. (2026). Vector Databases Brands in Gemini Model API Answers. Retrieved September 17, 2026, from https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-gemini-visibility

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<p style="font-size:12px">Source: <a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-gemini-visibility">Vector Databases Brands in Gemini Model API Answers</a> by Rank.ai</p>

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