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Vector databases

Most cited domains in vector Databases answers

79 sites ranked by how often AI answers cite them.
Recorded Sep 12, 2026Rotating scheduleSource: Rank.ai's prompt benchmark: fixed buying questions put to the OpenAI, Anthropic and Google model APIs, with Rank.ai's web searchHow this board is measuredCSV / JSON

36.1%

medium.com ranks first

Cited answer coverage

79

Sites ranked

of 84 tracked; the rest are below the quality bar

Sep 12

Snapshot recorded

2026 · Rotating schedule

36

Answers behind each row

From the OpenAI, Anthropic and Google model APIs

All 79 sites by cited answer coverage

medium.com leads at 36.1%; the middle of the board sits at 2.8%.

Ahead of second place
19.4%
Held by the top three
17%
Middle of the board
2.8%
Below half the leader
78 of 79
Highest 36.1%Middle 2.8%
1. medium.com: 36.1%2. milvus.io: 16.7%3. redis.io: 16.7%4. arxiv.org: 16.7%5. reddit.com: 16.7%6. firecrawl.dev: 11.1%7. zenml.io: 11.1%8. rank.ai: 11.1%9. qdrant.tech: 8.3%10. pinecone.io: 8.3%11. feba-systeme.com: 8.3%12. weaviate.io: 8.3%13. dev.to: 8.3%14. youtube.com: 8.3%15. linkedin.com: 5.6%16. zilliz.com: 5.6%17. scylladb.com: 5.6%18. openmetal.io: 5.6%19. aws.amazon.com: 5.6%20. docs.aws.amazon.com: 5.6%21. docs.databricks.com: 5.6%22. bigdataboutique.com: 5.6%23. actian.com: 5.6%24. facebook.com: 5.6%25. docs.cloud.google.com: 5.6%26. databricks.com: 5.6%27. cognine.com: 2.8%28. couchbase.com: 2.8%29. creditforstartups.com: 2.8%30. gigaspaces.com: 2.8%31. mehmetozkaya.medium.com: 2.8%32. aboutvectordatabase.com: 2.8%33. aperturedata.io: 2.8%34. blog.sugiv.fyi: 2.8%35. ciopages.com: 2.8%36. cohere.com: 2.8%37. petronellatech.com: 2.8%38. strapi.io: 2.8%39. vastdata.com: 2.8%40. webbycrown.com: 2.8%41. xenoss.io: 2.8%42. alphacorp.ai: 2.8%43. benchant.com: 2.8%44. cloud.google.com: 2.8%45. encore.dev: 2.8%46. engineersguide.substack.com: 2.8%47. iternal.ai: 2.8%48. learn.microsoft.com: 2.8%49. mlops.community: 2.8%50. npblue.com: 2.8%51. sec.cloudapps.cisco.com: 2.8%52. community.pinecone.io: 2.8%53. blckalpaca.at: 2.8%54. cloudsecurityalliance.org: 2.8%55. digeniotech.com: 2.8%56. instaclustr.com: 2.8%57. pecollective.com: 2.8%58. pub.towardsai.net: 2.8%59. stackoverflow.blog: 2.8%60. superlinked.com: 2.8%61. velodb.io: 2.8%62. aiamastery.substack.com: 2.8%63. digitalapplied.com: 2.8%64. globenewswire.com: 2.8%65. kalviumlabs.ai: 2.8%66. liveblocks.io: 2.8%67. tigergraph.com: 2.8%68. aipmguru.substack.com: 2.8%69. blog.vectorchord.ai: 2.8%70. elastic.co: 2.8%71. mongodb.com: 2.8%72. pingcap.com: 2.8%73. rconfig.com: 2.8%74. runtime.news: 2.8%75. tigerdata.com: 2.8%76. p99conf.io: 2.8%77. soeasie.com: 2.8%78. dl.acm.org: 2.8%79. ksofttechnologies.com: 2.8%
Rank 1One bar per published rowRank 79

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.

answers citing it: 1 on 53 of 79 rows · engines: 1 on 58 of 79 rows · 36 checks behind each row.

Most cited domains in vector Databases answers current rankings
RankNameCited answer coverageBest positionTypical positionChangeWhy
01medium.com36.1%13 1Showing
02milvus.io16.7%23.5 50This rank
03redis.io16.7%13.5 1This rank
04arxiv.org16.7%15.5 3This rank
05reddit.com16.7%27 4This rank
06firecrawl.dev11.1%12.5 This rank
07zenml.io11.1%23 2This rank
08rank.ai11.1%11—This rank
09qdrant.tech8.3%15 6This rank
10pinecone.io8.3%11 17This rank
11feba-systeme.com8.3%11 31This rank
12weaviate.io8.3%34 2This rank
13dev.to8.3%55 This rank
14youtube.com8.3%46 5This rank
15linkedin.com5.6%12 7This rank
16zilliz.com5.6%12—This rank
17scylladb.com5.6%12.5 11This rank
18openmetal.io5.6%13 6This rank
19aws.amazon.com5.6%33.5 10This rank
20docs.aws.amazon.com5.6%24 15This rank
21docs.databricks.com5.6%34 2This rank
22bigdataboutique.com5.6%45.5—This rank
23actian.com5.6%22.5 7This rank
24facebook.com5.6%23.5—This rank
25docs.cloud.google.com5.6%14 6This rank
26databricks.com5.6%55.5 39This rank
27cognine.com2.8%11—This rank
28couchbase.com2.8%11 12This rank
29creditforstartups.com2.8%11—This rank
30gigaspaces.com2.8%11 6This rank
31mehmetozkaya.medium.com2.8%11 6This rank
32aboutvectordatabase.com2.8%22—This rank
33aperturedata.io2.8%22—This rank
34blog.sugiv.fyi2.8%22—This rank
35ciopages.com2.8%22—This rank
36cohere.com2.8%22 20This rank
37petronellatech.com2.8%22 1This rank
38strapi.io2.8%22—This rank
39vastdata.com2.8%22—This rank
40webbycrown.com2.8%22 This rank
41xenoss.io2.8%22 5This rank
42alphacorp.ai2.8%33—This rank
43benchant.com2.8%33—This rank
44cloud.google.com2.8%33—This rank
45encore.dev2.8%33—This rank
46engineersguide.substack.com2.8%33—This rank
47iternal.ai2.8%33 3This rank
48learn.microsoft.com2.8%33 24This rank
49mlops.community2.8%33—This rank
50npblue.com2.8%33—This rank
51sec.cloudapps.cisco.com2.8%33 29This rank
52community.pinecone.io2.8%23.5 12This rank
53blckalpaca.at2.8%44—This rank
54cloudsecurityalliance.org2.8%44 30This rank
55digeniotech.com2.8%44 3This rank
56instaclustr.com2.8%44 31This rank
57pecollective.com2.8%44 3This rank
58pub.towardsai.net2.8%44—This rank
59stackoverflow.blog2.8%44—This rank
60superlinked.com2.8%44—This rank
61velodb.io2.8%44 22This rank
62aiamastery.substack.com2.8%55—This rank
63digitalapplied.com2.8%55 15This rank
64globenewswire.com2.8%55—This rank
65kalviumlabs.ai2.8%55—This rank
66liveblocks.io2.8%55 46This rank
67tigergraph.com2.8%55 9This rank
68aipmguru.substack.com2.8%66—This rank
69blog.vectorchord.ai2.8%66 14This rank
70elastic.co2.8%66—This rank
71mongodb.com2.8%66—This rank
72pingcap.com2.8%66 62This rank
73rconfig.com2.8%66—This rank
74runtime.news2.8%66—This rank
75tigerdata.com2.8%66 31This rank
76p99conf.io2.8%77—This rank
77soeasie.com2.8%77—This rank
78dl.acm.org2.8%88—This rank
79ksofttechnologies.com2.8%88—This rank

Compare brands

One brand against up to three competitors, answer by answer

12 questions × 3 model APIs · recorded Sep 12, 2026
Run a question live
Compared 4 of 4

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
BrandNamed or citedAgainst primaryShare of comparedMentionsAnswers citing itLinks to its siteModel APIs
Zilliz Cloud / Milvuszilliz.com33.3%12 of 36+5.6 pts28.6%23883 of 3
Weaviateweaviate.io30.6%11 of 36+2.8 pts26.2%25333 of 3
Pineconepinecone.io27.8%10 of 36Primary23.8%23352 of 3
Qdrantqdrant.tech25.0%9 of 36-2.8 pts21.4%22333 of 3

Every answer

Which compared brands each answer names

12 questions · 36 answers · a blank cell names none of the compared brands
Compared brands named in each answer, by question and model API
QuestionOpenAIopenai/gpt-4o-miniAnthropicanthropic/claude-haiku-4-5Geminigoogle/gemini-2.5-flash
01What are the best managed vector database platforms for a production retrieval-augmented generation application?Recommendation
PineconeWeaviateQdrantZilliz Cloud / Milvus
6 sourcesaa989829
None named
0 sources9fad189b
PineconeWeaviateQdrantZilliz Cloud / Milvus
6 sources7d5f3f4c
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 sourcesba4a3aa4
WeaviateQdrant, cited 1Zilliz Cloud / Milvus, cited 1
6 sourcesb99c6762
Weaviate, cited 1Qdrant, cited 1Zilliz Cloud / Milvus, cited 1
8 sources50de5eca
03Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?Recommendation
Zilliz Cloud / Milvus, cited 1
3 sources87ec9273
None named
0 sourcesba722281
None named
8 sources541d86f2
04What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking?Recommendation
PineconeWeaviate
5 sources0c2c9c05
None named
0 sourcesf93a3b8e
None named
5 sources780d110e
05Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.Recommendation
PineconeWeaviateZilliz Cloud / Milvus, cited 1
6 sourcesd4786d7b
None named
0 sources62fcf9cf
Weaviate, cited 1
7 sources3089faad
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 sourcesf0532c81
None named
0 sources127b014a
Pinecone, cited 1WeaviateQdrant
6 sources760548bb
07Recommend a low-latency vector search system for a workload that may grow to billions of embeddings.Recommendation
None named
8 sourcese0a1c5ed
None named
0 sources81ceee7c
None named
9 sources6cf63b9a
08What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload?Recommendation
PineconeWeaviateQdrant
6 sourcescbea3f62
None named
0 sourcesce0741bf
PineconeQdrantZilliz Cloud / Milvus
7 sourcesb9666f1b
09How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity?Commercial comparison
None named
4 sources66064c09
None named
0 sourcesa1b600fd
Pinecone
6 sources93e40019
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 sourcesb5213ed7
None named
0 sourcese706fa34
None named
7 sourcesed59023d
11Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?Commercial comparison
Zilliz Cloud / Milvus, cited 1
5 sourcesd911b3cf
None named
0 sourcesaca9c10c
Zilliz Cloud / Milvus, cited 1
6 sources8a897be1
12How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?Commercial comparison
None named
7 sources672a4fa7
None named
0 sourcese1a061c9
Zilliz Cloud / Milvus, cited 1
3 sourcesc8248eb7

Sources

The sites these answers cite

Each site is read from the cited address itself.
Sites cited in the filtered answers
SiteAnswers citing itDistinct pagesModel APIsBest position
medium.com131021
reddit.com6722
arxiv.org6621
redis.io6431
milvus.io6432
rank.ai4511
firecrawl.dev4131
zenml.io4132
youtube.com3324
dev.to3325
pinecone.io3221
weaviate.io3223
feba-systeme.com3121
qdrant.tech3131
scylladb.com2521
docs.cloud.google.com2211
linkedin.com2221
zilliz.com2221
actian.com2212
facebook.com2212

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

Why this rank

medium.com on Most-cited domains in Vector Databases answers

Snapshot of Sep 12, 2026, 12:00 AM UTC · Link to this snapshot

Some explanation inputs are unavailable. Not published: the evidence frozen with this snapshot.

Ranks URL-derived citation hostnames in the complete public prompt benchmark without additional provider calls.

1

Rank on this board

36.111 percent

Score

36

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 medium.com's score
ComponentValueContribution
Answer coverage36.11111136.111

Moved up since the previous snapshot

+1 places
  1. Answer coverage25 to 36.111
    +11.111
  2. Position denominator9 to 13
    +4
  3. Cited answer9 to 13
    +4
  4. Median position5 to 3
    -2
  5. Mean position4.667 to 3.154
    -1.513
  6. Position numerator42 to 41
    -1
  7. Distinct URL9 to 10
    +1
  8. Answer denominator36 to 36
    0
  9. Engine denominator3 to 3
    0
  10. Best position1 to 1
    0
  11. Engine breadth2 to 2
    0

Where each number comes from

7 public records
  • Cited-answer coverageAnswer coverage · observed Sep 12, 2026, 12:00 AM UTC36.111 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
  • Best citation positionBest position · observed Sep 12, 2026, 12:00 AM UTC1 position100% 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
  • Cited answersCited answer · observed Sep 12, 2026, 12:00 AM UTC13 answers100% 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
  • Distinct citation URLsDistinct URL · observed Sep 12, 2026, 12:00 AM UTC10 URLs100% 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
  • 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
    {"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
  • Mean citation positionMean position · observed Sep 12, 2026, 12:00 AM UTC3.154 position100% 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
  • Median citation positionMedian position · observed Sep 12, 2026, 12:00 AM UTC3 position100% 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
Public Vector Databases answer source visibility, version 2-parent-8f2516a7bd0dea82553 observations · 1 source · quality passed
Snapshot
a924cc92-0f8e-46ac-a128-9aa7958dcaa7
Data hash
624dcaa95d94fb9cc8fca7add74ec68985392dc5477aad00b6ccad714d1e8e8e
Method hash
9c90c58c6875c666d471a2afa32d87db8c2a37b9f21b257c35a6b5f0216e2cab

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

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.