Vector database engine breadth

As of

All 16 companies by engine breadth

Weaviate leads at 3 engines; the middle of the board sits at 2.

Ahead of second place: 0
Held by the top three: 32%
Middle of the board: 2
Below half the leader: 6 of 16

Rank, name and engine breadthMiddle 2

1Weaviate3 engines
2Zilliz Cloud / Milvus3 engines
3Qdrant3 engines
4Redis3 engines
5Pinecone2 engines
6Amazon OpenSearch Service2 engines
7Chroma2 engines
8Google Vertex AI Vector Search2 engines
9Elasticsearch2 engines
10Vespa2 engines
11MongoDB Atlas Vector Search1 engines
12Azure AI Search1 engines
13Cloudflare Vectorize1 engines
14Turbopuffer1 engines
15LanceDB0 engines
16Supabase Vector0 engines

The prompt boards are re-run in rotation, about one category a day, so each board refreshes every few weeks and shows the date of its snapshot.

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

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

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

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

Primary brandQuestion typeModel APIQuestion contains

Run a question live

27.8%

of these answers name or cite Pinecone

10 of 36 answers

23.8%

Its share of the compared brands' appearances

3

Answers citing its own site

5 links to its domains

2 of 3

Model APIs that name it

The compared brands across the filtered answers
Brand	Named or cited	Against primary	Share of compared	Mentions	Answers citing it	Links to its site	Model APIs
Zilliz Cloud / Milvuszilliz.com	33.3%12 of 36	+5.6 pts	28.6%	23	8	8	3 of 3
Weaviateweaviate.io	30.6%11 of 36	+2.8 pts	26.2%	25	3	3	3 of 3
Pineconepinecone.io	27.8%10 of 36	Primary	23.8%	23	3	5	2 of 3
Qdrantqdrant.tech	25.0%9 of 36	-2.8 pts	21.4%	22	3	3	3 of 3

Compared brands named in each answer, by question and model API
Question	OpenAIopenai/gpt-4o-mini	Anthropicanthropic/claude-haiku-4-5	Geminigoogle/gemini-2.5-flash
01What are the best managed vector database platforms for a production retrieval-augmented generation application?Recommendation	PineconeWeaviateQdrantZilliz Cloud / Milvus 6 sources`aa989829`	None named 0 sources`9fad189b`	PineconeWeaviateQdrantZilliz Cloud / Milvus 6 sources`7d5f3f4c`
02Recommend an open-source vector database that an engineering team can self-host in its own cloud account.Recommendation	Weaviate, cited 1Qdrant, cited 1Zilliz Cloud / Milvus, cited 1 6 sources`ba4a3aa4`	WeaviateQdrant, cited 1Zilliz Cloud / Milvus, cited 1 6 sources`b99c6762`	Weaviate, cited 1Qdrant, cited 1Zilliz Cloud / Milvus, cited 1 8 sources`50de5eca`
03Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?Recommendation	Zilliz Cloud / Milvus, cited 1 3 sources`87ec9273`	None named 0 sources`ba722281`	None named 8 sources`541d86f2`
04What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking?Recommendation	PineconeWeaviate 5 sources`0c2c9c05`	None named 0 sources`f93a3b8e`	None named 5 sources`780d110e`
05Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.Recommendation	PineconeWeaviateZilliz Cloud / Milvus, cited 1 6 sources`d4786d7b`	None named 0 sources`62fcf9cf`	Weaviate, cited 1 7 sources`3089faad`
06Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?Recommendation	Pinecone, cited 3WeaviateQdrantZilliz Cloud / Milvus 7 sources`f0532c81`	None named 0 sources`127b014a`	Pinecone, cited 1WeaviateQdrant 6 sources`760548bb`
07Recommend a low-latency vector search system for a workload that may grow to billions of embeddings.Recommendation	None named 8 sources`e0a1c5ed`	None named 0 sources`81ceee7c`	None named 9 sources`6cf63b9a`
08What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload?Recommendation	PineconeWeaviateQdrant 6 sources`cbea3f62`	None named 0 sources`ce0741bf`	PineconeQdrantZilliz Cloud / Milvus 7 sources`b9666f1b`
09How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity?Commercial comparison	None named 4 sources`66064c09`	None named 0 sources`a1b600fd`	Pinecone 6 sources`93e40019`
10When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database?Commercial comparison	Pinecone, cited 1 4 sources`b5213ed7`	None named 0 sources`e706fa34`	None named 7 sources`ed59023d`
11Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?Commercial comparison	Zilliz Cloud / Milvus, cited 1 5 sources`d911b3cf`	None named 0 sources`aca9c10c`	Zilliz Cloud / Milvus, cited 1 6 sources`8a897be1`
12How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?Commercial comparison	None named 7 sources`672a4fa7`	None named 0 sources`e1a061c9`	Zilliz Cloud / Milvus, cited 1 3 sources`c8248eb7`

Sites cited in the filtered answers
Site	Answers citing it	Distinct pages	Model APIs	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

What this report publishesThe public buying questions, the brands each answer names, the sources it cites and a fingerprint of each answer. Answer text is not published, and no customer data is in it.vector-database-platforms-2026-07-27 / 4-vector-database-platforms-2026-07-27-models-64232fd32583

Primary brandQuestion typeModel APIQuestion contains

Run a question live

27.8%

of these answers name or cite Pinecone

10 of 36 answers

23.8%

Its share of the compared brands' appearances

3

Answers citing its own site

5 links to its domains

2 of 3

Model APIs that name it

The compared brands across the filtered answers
Brand	Named or cited	Against primary	Share of compared	Mentions	Answers citing it	Links to its site	Model APIs
Zilliz Cloud / Milvuszilliz.com	33.3%12 of 36	+5.6 pts	28.6%	23	8	8	3 of 3
Weaviateweaviate.io	30.6%11 of 36	+2.8 pts	26.2%	25	3	3	3 of 3
Pineconepinecone.io	27.8%10 of 36	Primary	23.8%	23	3	5	2 of 3
Qdrantqdrant.tech	25.0%9 of 36	-2.8 pts	21.4%	22	3	3	3 of 3

Compared brands named in each answer, by question and model API
Question	OpenAIopenai/gpt-4o-mini	Anthropicanthropic/claude-haiku-4-5	Geminigoogle/gemini-2.5-flash
01What are the best managed vector database platforms for a production retrieval-augmented generation application?Recommendation	PineconeWeaviateQdrantZilliz Cloud / Milvus 6 sources`aa989829`	None named 0 sources`9fad189b`	PineconeWeaviateQdrantZilliz Cloud / Milvus 6 sources`7d5f3f4c`
02Recommend an open-source vector database that an engineering team can self-host in its own cloud account.Recommendation	Weaviate, cited 1Qdrant, cited 1Zilliz Cloud / Milvus, cited 1 6 sources`ba4a3aa4`	WeaviateQdrant, cited 1Zilliz Cloud / Milvus, cited 1 6 sources`b99c6762`	Weaviate, cited 1Qdrant, cited 1Zilliz Cloud / Milvus, cited 1 8 sources`50de5eca`
03Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?Recommendation	Zilliz Cloud / Milvus, cited 1 3 sources`87ec9273`	None named 0 sources`ba722281`	None named 8 sources`541d86f2`
04What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking?Recommendation	PineconeWeaviate 5 sources`0c2c9c05`	None named 0 sources`f93a3b8e`	None named 5 sources`780d110e`
05Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.Recommendation	PineconeWeaviateZilliz Cloud / Milvus, cited 1 6 sources`d4786d7b`	None named 0 sources`62fcf9cf`	Weaviate, cited 1 7 sources`3089faad`
06Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?Recommendation	Pinecone, cited 3WeaviateQdrantZilliz Cloud / Milvus 7 sources`f0532c81`	None named 0 sources`127b014a`	Pinecone, cited 1WeaviateQdrant 6 sources`760548bb`
07Recommend a low-latency vector search system for a workload that may grow to billions of embeddings.Recommendation	None named 8 sources`e0a1c5ed`	None named 0 sources`81ceee7c`	None named 9 sources`6cf63b9a`
08What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload?Recommendation	PineconeWeaviateQdrant 6 sources`cbea3f62`	None named 0 sources`ce0741bf`	PineconeQdrantZilliz Cloud / Milvus 7 sources`b9666f1b`
09How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity?Commercial comparison	None named 4 sources`66064c09`	None named 0 sources`a1b600fd`	Pinecone 6 sources`93e40019`
10When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database?Commercial comparison	Pinecone, cited 1 4 sources`b5213ed7`	None named 0 sources`e706fa34`	None named 7 sources`ed59023d`
11Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?Commercial comparison	Zilliz Cloud / Milvus, cited 1 5 sources`d911b3cf`	None named 0 sources`aca9c10c`	Zilliz Cloud / Milvus, cited 1 6 sources`8a897be1`
12How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?Commercial comparison	None named 7 sources`672a4fa7`	None named 0 sources`e1a061c9`	Zilliz Cloud / Milvus, cited 1 3 sources`c8248eb7`

Sites cited in the filtered answers
Site	Answers citing it	Distinct pages	Model APIs	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

What this report publishesThe public buying questions, the brands each answer names, the sources it cites and a fingerprint of each answer. Answer text is not published, and no customer data is in it.vector-database-platforms-2026-07-27 / 4-vector-database-platforms-2026-07-27-models-64232fd32583

Ranks a reviewed Vector Databases brand universe using a fixed, public set of neutral commercial prompts across 3 AI providers.

13

Rank on this board

1 engines

Score

3

Answers behind the score

100% confidence · 1 of 1 components evidenced

What the score is built from

1 of 1 evidenced

Every component evidenced · 1 evidence record each

Components of Cloudflare Vectorize's score
Component	Value	Contribution
Engine breadth	1	1

Moved up since the previous snapshot

+3 places

Gemini brand recommendation presence0 to 12.5
+12.5
Gemini brand mention coverage0 to 8.333
+8.333
Recommendation presence0 to 4.167
+4.167
Mention coverage0 to 2.778
+2.778
Mention share0 to 1.449
+1.449
Engine breadth0 to 1
+1
Anthropic brand citation share0 to 0
0
Anthropic brand mention coverage0 to 0
0
Anthropic brand recommendation presence0 to 0
0
Citation share0 to 0
0
Gemini brand citation share0 to 0
0
OpenAI brand citation share0 to 0
0
OpenAI brand mention coverage0 to 0
0
OpenAI brand recommendation presence0 to 0
0

Where each number comes from

14 public records

Anthropic citation shareAnthropic 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 the source
Anthropic mention coverageAnthropic 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 the source
Anthropic recommendation-prompt presenceAnthropic 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 the source
Citation shareCitation 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 the source
Engine breadthEngine breadth · observed Sep 12, 2026, 12:00 AM UTC1 engines100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Mention coverageMention coverage · observed Sep 12, 2026, 12:00 AM UTC2.778 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Reviewed-brand presence shareMention share · observed Sep 12, 2026, 12:00 AM UTC1.449 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Recommendation-prompt presenceRecommendation presence · observed Sep 12, 2026, 12:00 AM UTC4.167 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source
Gemini citation shareGemini 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 the source
Gemini mention coverageGemini 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 the source
Gemini recommendation-prompt presenceGemini 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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Open the source
OpenAI citation shareOpenAI 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 the source
OpenAI mention coverageOpenAI 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 the source
OpenAI recommendation-prompt presenceOpenAI 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 the source

Cite this data

Free to use with attribution. Copy a citation, or link to this page: it keeps its address when the board updates.

Plain textVector Databases engine breadth. Rank.ai, data as of September 12, 2026. https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-engine-breadth
HTML<a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-engine-breadth">Vector Databases engine breadth</a>, Rank.ai, data as of September 12, 2026
Markdown[Vector Databases engine breadth](https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-engine-breadth), Rank.ai, data as of September 12, 2026
APARank.ai. (2026). Vector Databases engine breadth. Retrieved October 24, 2026, from https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-engine-breadth

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<iframe src="https://www.rank.ai/embed/boards/vector-database-platforms-brand-engine-breadth" title="Vector Databases engine breadth" width="100%" height="420" loading="lazy" style="border:1px solid #111;max-width:720px"></iframe>
<p style="font-size:12px">Source: <a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-engine-breadth">Vector Databases engine breadth</a> by Rank.ai</p>

More in Vector databases

Managed, open source and hyperscaler vector retrieval platforms ranked across a fixed buying corpus, with brand, prompt, engine and citation source views.

3 boards

Vector database platform rank

Reviewed vector retrieval platforms ranked by how often answers to the category's recommendation questions name or cite them, across three model APIs.

1Weaviate145.8%
2Zilliz Cloud / Milvus237.5%
3Qdrant37.5%

Vector database platform presence

Reviewed platforms ranked by the share of all the category's answers that name or cite them.

1Zilliz Cloud / Milvus433.3%
2Weaviate30.6%
3Pinecone227.8%

Vector database citation share

Reviewed platforms ranked by distinct answer-level citations to their canonical domains.

1Zilliz Cloud / Milvus622.9%
2Redis17.1%
3Pinecone14.3%

Vector database platform rank

Reviewed vector retrieval platforms ranked by how often answers to the category's recommendation questions name or cite them, across three model APIs.

Vector database platform presence

Reviewed platforms ranked by the share of all the category's answers that name or cite them.

Vector database citation share

Reviewed platforms ranked by distinct answer-level citations to their canonical domains.

See how often AI names you.

Enter your website. In about two minutes, Rank.ai asks ChatGPT, Claude and Gemini 12 questions your customers ask and grades how often they name you.

Your grade out of 100How often AI names you, cites your site, and how high it ranks you.
Who gets namedEvery competitor in the answers, most named first.
The pages AI readsThe sources behind each answer.
Three fixesWhat to fix first, with a brief for the first article.

Vector Databases engine breadth

All 16 companies by engine breadth

One brand against up to three competitors, answer by answer

Which compared brands each answer names

The sites these answers cite

One brand against up to three competitors, answer by answer

Which compared brands each answer names

The sites these answers cite

Cloudflare Vectorize on Vector Databases Engine Breadth

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Where each number comes from

More in Vector databases

See how often AI names you.

One brand against up to three competitors, answer by answer

Which compared brands each answer names

The sites these answers cite