Vector databases in OpenAI model API answers

As of

All 16 companies by OpenAI recommendation prompt presence

Weaviate leads at 75.0%; the middle of the board sits at 12.5%.

Ahead of second place: 12.5%
Held by the top three: 59%
Middle of the board: 12.5%
Below half the leader: 12 of 16

Rank, name and OpenAI recommendation prompt presenceMiddle 12.5%

1Weaviate75.0%
2Zilliz Cloud / Milvus62.5%
3Pinecone62.5%
4Qdrant50.0%
5Redis25.0%
6Amazon OpenSearch Service12.5%
7Chroma12.5%
8Elasticsearch12.5%
9Vespa12.5%
10MongoDB Atlas Vector Search12.5%
11Google Vertex AI Vector Search0.0%
12Azure AI Search0.0%
13Cloudflare Vectorize0.0%
14Turbopuffer0.0%
15LanceDB0.0%
16Supabase Vector0.0%

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.

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

Vector Databases brands in OpenAI model API answers current rankings
Rank	Name	OpenAI recommendation prompt presence	Mention coverage	Share of names	Cited as source	Change	Why
01	Weaviate	75.0%	30.56% · 11 / 36	15.94% · 11 / 69	8.57% · 3 / 35	1	This rank My rank
02	Zilliz Cloud / Milvus	62.5%	33.33% · 12 / 36	17.39% · 12 / 69	22.86% · 8 / 35	2	This rank My rank
03	Pinecone	62.5%	27.78% · 10 / 36	14.49% · 10 / 69	14.29% · 5 / 35	2	This rank My rank
04	Qdrant	50.0%	25% · 9 / 36	13.04% · 9 / 69	8.57% · 3 / 35	1	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	12.5%	13.89% · 5 / 36	7.25% · 5 / 69	11.43% · 4 / 35	1	This rank My rank
07	Chroma	12.5%	8.33% · 3 / 36	4.35% · 3 / 69	0% · 0 / 35	1	Showing My rank
08	Elasticsearch	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35		This rank My rank
09	Vespa	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	0% · 0 / 35	1	This rank My rank
10	MongoDB Atlas Vector Search	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35	1	This rank My rank
11	Google Vertex AI Vector Search	0.0%	8.33% · 3 / 36	4.35% · 3 / 69	8.57% · 3 / 35	1	This rank My rank
12	Azure AI Search	0.0%	2.78% · 1 / 36	1.45% · 1 / 69	2.86% · 1 / 35	1	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	1	This rank My rank
15	LanceDB	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	1	This rank My rank
16	Supabase Vector	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	5	This rank My rank

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

Vector Databases brands in OpenAI model API answers current rankings
Rank	Name	OpenAI recommendation prompt presence	Mention coverage	Share of names	Cited as source	Change	Why
01	Weaviate	75.0%	30.56% · 11 / 36	15.94% · 11 / 69	8.57% · 3 / 35	1	This rank My rank
02	Zilliz Cloud / Milvus	62.5%	33.33% · 12 / 36	17.39% · 12 / 69	22.86% · 8 / 35	2	This rank My rank
03	Pinecone	62.5%	27.78% · 10 / 36	14.49% · 10 / 69	14.29% · 5 / 35	2	This rank My rank
04	Qdrant	50.0%	25% · 9 / 36	13.04% · 9 / 69	8.57% · 3 / 35	1	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	12.5%	13.89% · 5 / 36	7.25% · 5 / 69	11.43% · 4 / 35	1	This rank My rank
07	Chroma	12.5%	8.33% · 3 / 36	4.35% · 3 / 69	0% · 0 / 35	1	Showing My rank
08	Elasticsearch	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35		This rank My rank
09	Vespa	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	0% · 0 / 35	1	This rank My rank
10	MongoDB Atlas Vector Search	12.5%	5.56% · 2 / 36	2.9% · 2 / 69	2.86% · 1 / 35	1	This rank My rank
11	Google Vertex AI Vector Search	0.0%	8.33% · 3 / 36	4.35% · 3 / 69	8.57% · 3 / 35	1	This rank My rank
12	Azure AI Search	0.0%	2.78% · 1 / 36	1.45% · 1 / 69	2.86% · 1 / 35	1	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	1	This rank My rank
15	LanceDB	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	1	This rank My rank
16	Supabase Vector	0.0%	0% · 0 / 36	0% · 0 / 69	0% · 0 / 35	5	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.

7

Rank on this board

12.5 percent

Score

8

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 Chroma's score
Component	Value	Contribution
OpenAI brand recommendation presence	12.5	12.5

Moved down since the previous snapshot

-1 places

OpenAI brand recommendation presence37.5 to 12.5
-25
OpenAI brand mention coverage25 to 8.333
-16.667
Gemini brand recommendation presence12.5 to 25
+12.5
Gemini brand mention coverage8.333 to 16.667
+8.333
Recommendation presence16.667 to 12.5
-4.167
Mention coverage11.111 to 8.333
-2.778
Mention share5.714 to 4.348
-1.366
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
Engine breadth2 to 2
0
Gemini brand citation share0 to 0
0
OpenAI brand citation share0 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 UTC2 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 UTC8.333 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 UTC4.348 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 UTC12.5 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 UTC16.667 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 UTC25 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 UTC8.333 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 UTC12.5 percent100% confidence
Source
Rank.ai Vector Databases Prompt Benchmark
Grade
A
Freshness
fresh
Locator
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Open the source

Cite this data

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Plain textVector Databases brands in OpenAI model API answers. Rank.ai, data as of September 12, 2026. https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-openai-visibility
HTML<a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-openai-visibility">Vector Databases brands in OpenAI model API answers</a>, Rank.ai, data as of September 12, 2026
Markdown[Vector Databases brands in OpenAI model API answers](https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-openai-visibility), Rank.ai, data as of September 12, 2026
APARank.ai. (2026). Vector Databases brands in OpenAI model API answers. Retrieved October 12, 2026, from https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-openai-visibility

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<p style="font-size:12px">Source: <a href="https://www.rank.ai/rankings/vector-databases/vector-database-platforms-brand-openai-visibility">Vector Databases brands in OpenAI model API answers</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 brands in OpenAI model API answers

All 16 companies by OpenAI recommendation prompt presence

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

Chroma on Vector Databases Brands in OpenAI Model API Answers

What the score is built from

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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