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

As of

In front today

Zilliz Cloud / Milvus

22.9%citation share

Ahead of second place: 5.7%
Held by the top three: 54%
Middle of the board: 2.9%
Below half the leader: 13 of 16

Highest 22.9%Middle 2.9%

Rank 1one bar per published rowRank 16

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

Vector Databases citation share current rankings
Rank	Name	Citation share	Mention coverage	Share of names	Cited as source	Change	Why
01	Zilliz Cloud / Milvus	22.9%	33.33% · 12 / 36	17.39% · 12 / 69	22.86% · 8 / 35	6	This rank My rank
02	Redis	17.1%	19.44% · 7 / 36	10.14% · 7 / 69	17.14% · 6 / 35		This rank My rank
03	Pinecone	14.3%	27.78% · 10 / 36	14.49% · 10 / 69	14.29% · 5 / 35		This rank My rank
04	Amazon OpenSearch Service	11.4%	13.89% · 5 / 36	7.25% · 5 / 69	11.43% · 4 / 35		This rank My rank
05	Weaviate	8.6%	30.56% · 11 / 36	15.94% · 11 / 69	8.57% · 3 / 35		This rank My rank
06	Qdrant	8.6%	25% · 9 / 36	13.04% · 9 / 69	8.57% · 3 / 35	5	This rank My rank
07	Google Vertex AI Vector Search	8.6%	8.33% · 3 / 36	4.35% · 3 / 69	8.57% · 3 / 35	1	This rank My rank
08	Elasticsearch	2.9%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35	3	This rank My rank
09	MongoDB Atlas Vector Search	2.9%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35	4	This rank My rank
10	Azure AI Search	2.9%	2.78% · 1 / 36	1.45% · 1 / 69	2.86% · 1 / 35	2	This rank My rank
11	Chroma	0.0%	8.33% · 3 / 36	4.35% · 3 / 69	0% · 0 / 35	2	This rank My rank
12	Vespa	0.0%	5.56% · 2 / 36	2.9% · 2 / 69	0% · 0 / 35	3	This rank My rank
13	Cloudflare Vectorize	0.0%	2.78% · 1 / 36	1.45% · 1 / 69	0% · 0 / 35	3	This rank My rank
14	Turbopuffer	0.0%	2.78% · 1 / 36	1.45% · 1 / 69	0% · 0 / 35		This rank My rank
15	LanceDB	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	3	This rank My rank
16	Supabase Vector	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	6	Showing 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: #16
Score: 0 percent
Sample size: 35

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 Supabase Vector's rank
Component	Weight	Contribution
public prompt anthropic brand citation share	0%	—
public prompt anthropic brand mention coverage	0%	—
public prompt anthropic brand recommendation presence	0%	—
public prompt brand citation share	100%	0
public prompt brand engine breadth	0%	—
public prompt brand mention coverage	0%	—
public prompt brand mention share	0%	—
public prompt brand recommendation presence	0%	—
public prompt gemini brand citation share	0%	—
public prompt gemini brand mention coverage	0%	—
public prompt gemini brand recommendation presence	0%	—
public prompt openai brand citation share	0%	—
public prompt openai brand mention coverage	0%	—
public prompt openai brand recommendation presence	0%	—

WHY IT MOVED

down since prior snapshot

-6 ranks

public prompt anthropic brand recommendation presence12.5 → 0
-12.5
public prompt gemini brand recommendation presence12.5 → 0
-12.5
public prompt anthropic brand mention coverage8.333 → 0
-8.333
public prompt brand recommendation presence8.333 → 0
-8.333
public prompt gemini brand mention coverage8.333 → 0
-8.333
public prompt brand mention coverage5.556 → 0
-5.556
public prompt brand mention share2.857 → 0
-2.857
public prompt brand engine breadth2 → 0
-2
public prompt anthropic brand citation share0 → 0
0
public prompt brand citation share0 → 0
0
public prompt gemini brand citation share0 → 0
0
public prompt openai brand citation share0 → 0
0
public prompt openai brand mention coverage0 → 0
0
public prompt openai brand recommendation presence0 → 0
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 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 ↗
Engine breadthpublic prompt brand engine breadth · observed Sep 12, 2026, 12:00 AM UTC0 engines100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open primary evidence ↗
Mention coveragepublic prompt 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 ↗
Reviewed-brand presence sharepublic prompt brand mention 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 ↗
Recommendation-prompt presencepublic prompt 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
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Open primary evidence ↗
Gemini citation sharepublic prompt gemini 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 ↗
Gemini mention coveragepublic prompt gemini 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
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Open primary evidence ↗
Gemini recommendation-prompt presencepublic prompt gemini 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
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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 UTC0 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 UTC0 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Plain textVector Databases Citation Share. Rank.ai, data as of September 12, 2026. https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-citation-share
HTML<a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-citation-share">Vector Databases Citation Share</a> — Rank.ai, data as of September 12, 2026
Markdown[Vector Databases Citation Share](https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-citation-share) — Rank.ai, data as of September 12, 2026
APARank.ai. (2026). Vector Databases Citation Share. Retrieved September 17, 2026, from https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-citation-share

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<p style="font-size:12px">Source: <a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-citation-share">Vector Databases Citation Share</a> by Rank.ai</p>

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