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RANK.AI DATA / LATEST PUBLICATION

Vector Databases engine disagreement by prompt

12 prompts ranked by cross engine brand disagreement.
Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment? is out in front at 100.0%.

12 promptschecked Aug 4, 2026, 12:00 AM UTCdailyhow we measure this →CSV / JSON

Follow changes
In front today

Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment?

100.0%cross engine brand disagreement

Ahead of second place
0%
Held by the top three
32%
Middle of the board
74.5%
Below half the leader
1 of 12
Highest 100.0%Middle 74.5%
1. Which enterprise vector retrieval services offer private networking, access controls, compliance, and regional deployment? — 100.0%2. How should a team compare vector database pricing across storage, ingestion, read throughput, replicas, and idle capacity? — 100.0%3. Recommend a low-latency vector search system for a workload that may grow to billions of embeddings. — 95.8%4. Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings. — 91.7%5. Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval? — 88.9%6. What are the best managed vector database platforms for a production retrieval-augmented generation application? — 76.2%7. What is the best serverless or usage-based vector database for a startup with an unpredictable retrieval workload? — 72.8%8. When should a team buy a dedicated vector database instead of adding embedding search to its existing operational database? — 66.7%9. What is the best production data platform for combining keyword retrieval, semantic retrieval, metadata filters, and reranking? — 66.7%10. Which buying criteria matter most for production vector retrieval, including recall, latency, filtering, durability, and operations? — 66.7%11. How should an engineering team evaluate vector search benchmark claims for recall, tail latency, throughput, scale, and total cost? — 66.7%12. Recommend an open-source vector database that an engineering team can self-host in its own cloud account. — 28.3%
Rank 1one bar per published rowRank 12
WHY THIS RANK / VERIFIED SNAPSHOT

Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?

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

availablePermalink to this snapshot ↗
Published rank
#5
Score
88.889 percent
Sample size
3

100% confidence · 100% component coverage · as of Aug 4, 2026, 12:00 AM UTC

SCORE CONSTRUCTION

Component ledger

7/7 evidenced

All components available · 1 evidence record each

Components contributing to Which vector database is best for a multi-tenant SaaS product that needs namespace isolation and filtered retrieval?'s rank
ComponentValueWeightContribution
public prompt brand presence events40%—
public prompt citation urls130%—
public prompt distinct reviewed brands30%—
public prompt engine disagreement88.888889100%88.889
public prompt leading brand consensus66.6666670%—
public prompt leading brand owned citation coverage33.3333330%—
public prompt leading brand owned citation gap33.3333340%—
WHY IT MOVED

up since prior snapshot

+4 ranks
  1. public prompt engine disagreement44.048 → 88.889
    +44.841
  2. public prompt leading brand owned citation gap66.667 → 33.333
    -33.333
  3. public prompt leading brand consensus100 → 66.667
    -33.333
  4. public prompt brand presence events13 → 4
    -9
  5. public prompt citation urls22 → 13
    -9
  6. public prompt distinct reviewed brands7 → 3
    -4
  7. public prompt leading brand owned citation coverage33.333 → 33.333
    0
PRIMARY EVIDENCE

Source trail

7 public records
  • Reviewed-brand presence eventspublic prompt brand presence events · observed Aug 4, 2026, 12:00 AM UTC4 presence_events100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Distinct citation URLspublic prompt citation urls · observed Aug 4, 2026, 12:00 AM UTC13 urls100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Distinct reviewed brands surfacedpublic prompt distinct reviewed brands · observed Aug 4, 2026, 12:00 AM UTC3 brands100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Cross-engine brand disagreementpublic prompt engine disagreement · observed Aug 4, 2026, 12:00 AM UTC88.889 percent100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Leading-brand engine consensuspublic prompt leading brand consensus · observed Aug 4, 2026, 12:00 AM UTC66.667 percent100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Leading-brand owned-citation coveragepublic prompt leading 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":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
  • Leading-brand owned-citation opportunitypublic prompt leading brand owned citation gap · observed Aug 4, 2026, 12:00 AM UTC33.333 percentage_points100% confidence
    Source
    Rank.ai Vector Databases Prompt Benchmark
    Grade
    A
    Freshness
    fresh
    Locator
    {"promptSlug":"multi-tenant-vector-database-saas","corpusVersion":"vector-database-platforms-2026-07-27","runArtifactIds":["7b32e70b-d69c-445e-a3ca-c50d9c27139e","78fd23e4-aba8-4342-9363-8cca75798517","d7733b38-8b4f-46ba-9eee-44894bad539d"],"parentSnapshotId":"b124696f-f8e4-4b21-8539-c7fd45268eeb","providerMatrixVersion":"64232fd32583"}
    Open primary evidence ↗
REPRODUCIBILITYPublic Vector Databases prompt leaders, engine disagreement, and citation opportunity · v2-parent-8f2516a7bd0dea8284 observations · 1 sources · passed quality
Snapshot
b316994e-84dd-4c49-bf28-543e4f8865e5
Data hash
0d513302a5168b0d5961bba29f52261a15f4e048f424ceda2875a4d90d81fd85
Method hash
ea7d8f305edb09448d279a6242b5e3693693256a036e6b6cc7560e34ec16da40

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