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Vector databases

Vector Databases prompt brand leaders

12 prompts ranked by how many of the three model APIs name the same leader.
Recorded Aug 4, 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

100.0%

What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload? ranks first

Leading brand engine consensus

12

Prompts ranked

Aug 4

Snapshot recorded

2026 · Rotating schedule

3

Answers behind each row

From the OpenAI, Anthropic and Google model APIs

Follow changes

All 12 prompts by leading brand engine consensus

What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload? leads at 100.0%; the middle of the board sits at 66.7%.

Ahead of second place
0%
Held by the top three
38%
Middle of the board
66.7%
Below half the leader
5 of 12
Rank, name and leading brand engine consensusMiddle 66.7%
  1. 1What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload?100.0%
  2. 2Recommend an open-source vector database that an engineering team can self-host in its own cloud account.100.0%
  3. 3Recommend a low-latency vector search system for a workload that may grow to billions of embeddings.66.7%
  4. 4Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.66.7%
  5. 5Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?66.7%
  6. 6What are the best managed vector database platforms for a production retrieval-augmented generation application?66.7%
  7. 7When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database?66.7%
  8. 8Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?33.3%
  9. 9How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity?33.3%
  10. 10What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking?33.3%
  11. 11Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?33.3%
  12. 12How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?33.3%

leading brand owned citation gap: 0 on 8 of 12 rows · 3 checks behind each row · 100% confidence across rows.

Vector Databases prompt brand leaders current rankings
RankNameLeading brand engine consensusPresence eventsCitation URLSDistinct reviewed brandsChangeWhy
01What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload?100.0%12118 1This rankRun it
02Recommend an open-source vector database that an engineering team can self-host in its own cloud account.100.0%12125 3This rankRun it
03Recommend a low-latency vector search system for a workload that may grow to billions of embeddings.66.7%9148 7This rankRun it
04Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.66.7%5144 5This rankRun it
05Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?66.7%4133 2This rankRun it
06What are the best managed vector database platforms for a production retrieval-augmented generation application?66.7%12107 2This rankRun it
07When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database?66.7%2111 5This rankRun it
08Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?33.3%5165 2This rankRun it
09How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity?33.3%3103 2This rankRun it
10What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking?33.3%3113 9This rankRun it
11Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?33.3%2142 4ShowingRun it
12How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost?33.3%1121 8This rankRun it

Why this rank

Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations? on Vector Databases Prompt Brand Leaders

Snapshot of Aug 4, 2026, 12:00 AM UTC · Link to this snapshot

Derives prompt-level brand leaders, cross-engine brand-set disagreement, and leading-brand owned-citation opportunity from the complete public Rank.ai benchmark cohort.

11

Rank on this board

33.333 percent

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 Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations?'s score
ComponentValueContribution
Leading brand consensus33.33333333.333

Moved down since the previous snapshot

-4 places
  1. Leading brand consensus66.667 to 33.333
    -33.333
  2. Leading brand owned citation coverage66.667 to 33.333
    -33.333
  3. Engine disagreement83.333 to 66.667
    -16.667
  4. Citation URLS18 to 14
    -4
  5. Presence events3 to 2
    -1
  6. Distinct reviewed brands2 to 2
    0
  7. Leading brand owned citation gap0 to 0
    0

Where each number comes from

7 public records
  • Reviewed-brand presence eventsPresence events · observed Aug 4, 2026, 12:00 AM UTC2 presence_events100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
  • Distinct citation URLsCitation URLS · observed Aug 4, 2026, 12:00 AM UTC14 urls100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
  • Distinct reviewed brands surfacedDistinct reviewed brands · observed Aug 4, 2026, 12:00 AM UTC2 brands100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
  • Cross-engine brand disagreementEngine disagreement · observed Aug 4, 2026, 12:00 AM UTC66.667 percent100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
  • Leading-brand engine consensusLeading brand consensus · observed Aug 4, 2026, 12:00 AM UTC33.333 percent100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
  • Leading-brand owned-citation coverageLeading brand owned citation coverage · observed Aug 4, 2026, 12:00 AM UTC33.333 percent100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
  • Leading-brand owned-citation opportunityLeading brand owned citation gap · observed Aug 4, 2026, 12:00 AM UTC0 percentage_points100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"vector-database-buying-criteria","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["77066b75-85df-4cef-9750-bc1b5f4dc343","28474bc1-0d08-4b4f-a45c-a56619923704","4953d826-fd76-487b-a416-bc3a1c2d1eb5"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open the source
Public Vector Databases prompt leaders, engine disagreement, and citation opportunity, version 2-parent-8f2516a7bd0dea8284 observations · 1 source · quality passed
Snapshot
2865cfef-0f3e-47d9-aa9c-9d0d74c180b1
Data hash
9fad2f17430efb7b6ffe76892b97a47f5b34c4e96919f933bcfdba9138da831f
Method hash
ea7d8f305edb09448d279a6242b5e3693693256a036e6b6cc7560e34ec16da40

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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.