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Buying question

Recommend a vector retrieval platform for searching text, images, audio, and other multimodal embeddings.

Which brands each model API named when asked this question, and which pages it cited. Answer text is not published.
Snapshot Sep 12, 2026. Each category is re-run in rotation, every few weeks.

Recommendation

Kind of question

3

Model APIs

6

Brands named

13

Pages cited

Brands

Brands the answers named

Ordered by how many model APIs named the brand, then by mentions. A mention is not an endorsement or a position.
Rank#BrandModel APIsMentionsOwn-site citationsNamed by
1Weaviateweaviate.io231OpenAI · Google
2Zilliz Cloud / Milvuszilliz.com121OpenAI
3Pineconepinecone.io120OpenAI
4Amazon OpenSearch Serviceaws.amazon.com101Google
5Elasticsearchelastic.co101Google
6Google Vertex AI Vector Searchcloud.google.com101Google

Model APIs

What each model API returned

One row per model API. The hash identifies the exact answer without publishing it.
Model APIModelBrands namedCitationsDomainsAnswer hash
OpenAIopenai/gpt-4o-miniPinecone · Weaviate · Zilliz Cloud / Milvus66d4786d7b…9533c
Anthropicanthropic/claude-haiku-4-5None named0062fcf9cf…ff95c
Googlegoogle/gemini-2.5-flashWeaviate · Elasticsearch · Google Vertex AI Vector Search · Amazon OpenSearch Service773089faad…57e4a

Sources

Pages the answers cited

Hostnames come from the URLs the answers returned. Best position is the earliest place a page was cited in an answer.
DomainCited pageBest positionCited byCitations
arxiv.orghttps://arxiv.org/html/2511.16654v21OpenAI1
docs.cloud.google.comhttps://docs.cloud.google.com/gemini-enterprise-agent-platform/models/embeddings/get-multimodal-embeddings1Google1
aperturedata.iohttps://aperturedata.io/blog/multimodal-data-intro-part12OpenAI1
cohere.comhttps://cohere.com/blog/multimodal-embeddings2Google1
aws.amazon.comhttps://aws.amazon.com/blogs/machine-learning/scale-creative-asset-discovery-with-amazon-nova-multimodal-embeddings-unified-vector-search/3Google1
mlops.communityhttps://mlops.community/blog/how-multimodal-vector-databases-are-transforming-challenges-across-industries3OpenAI1
medium.comhttps://medium.com/@tenyks_blogger/multi-modal-image-search-with-embeddings-vector-dbs-cee61c70a88a4OpenAI1
weaviate.iohttps://weaviate.io/blog/multimodal-guide4Google1
milvus.iohttps://milvus.io/blog/multimodal-semantic-search-with-images-and-text.md5OpenAI1
tigergraph.comhttps://tigergraph.com/glossary/multimodal-embeddings/5Google1
elastic.cohttps://elastic.co/search-labs/blog/multimodal-embeddings-ecommerce-product-search6Google1
youtube.comhttps://youtube.com/watch?v=3WUobZryyok6OpenAI1
soeasie.comhttps://soeasie.com/blog/considerations-for-optimizing-media-retrieval-systems-using-multimodal-embeddings7Google1
Snapshots
794067bc-f4fa-456f-8063-1db7efff0c35
Question set
vector-database-platforms-2026-07-27
Benchmark version
4-vector-database-platforms-2026-07-27-models-64232fd32583
What is kept
Measured in Vector DBs. Full answers are not stored or published, and no customer data is included.

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.