Every category where a model named FAISS for a mid-market B2B company. Share is first choices across the direct, paraphrase, budget and scale prompts; rank is within every product named in that category.

Category	Function	Share	Rank	Negative rate	Labels	Quadrant
Vector databases	Data platform	0%	11 of 24	18%	11	accepted challenger

Movement

This is the first edition on this tier, so no move can be computed for FAISS yet. From the next edition this section shows, per buyer segment, whether its share moved by more than the measured noise floor.

By model

How each model treated FAISS across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

Model	Alternative	Mention	Negative	Labels
Claude Haiku 4.5	0	2	0	2
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	1	1
Perplexity Sonar	0	1	0	1
Grok 4.1 Fast	0	2	1	3
Mistral Small	0	1	0	1
DeepSeek V4 Flash	1	1	0	2
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	0	0	0
GLM 4.7 FlashX	0	0	0	0
MiniMax M2.5	0	1	0	1

By framing

Which of the six questions produced the naming. By model says how often; this says asked what. The first-choice count on the right carries the marks of the models that produced it.

FramingLabels by classFirst choices

Direct0 labelsNone

Paraphrase1 labelNone

Comparative4 labelsNone

Budget-constrained1 labelNone

Scale-constrained1 labelNone

Negative16 labelsNone

First choiceAlternativeMentionNegative23 labels in all, every segment counted; 0 of the 0 first choices count toward share, since the comparative and negative framings do not. The bar is one segment per label class, to scale within the framing.

What the models said for it

Verbatim evidence the judge attached to positive labels.

“Below ~10K vectors, deploying *any* dedicated vector DB is overengineering — use FAISS or even numpy in-memory instead.” DeepSeek V4 Flash · Vector databases · negative prompt · alternative

And against it

Verbatim evidence attached to negative labels. A warning on a product with few labels is a warning; on a product with many, it is one voice among them.

“Be cautious about Chroma (and FAISS) for Large-Scale Production” Gemini 3.5 Flash · Vector databases · negative prompt · soft negative

“Tools like Annoy or Faiss are for offline, not live apps.” Grok 4.1 Fast · Vector databases · paraphrase prompt · soft negative

Named alongside

The products named in the same answers as FAISS, over the 23 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and FAISS was named but was not.

ProductSame answerTook the first choice insteadHead to head

Pinecone21 of 232Not in the top three

Weaviate19 of 230Not in the top three

pgvector18 of 2310Not in the top three

Qdrant18 of 234Not in the top three

Milvus17 of 230Not in the top three

Chroma13 of 232Not in the top three

Milvus / Zilliz Cloud5 of 230Not in the top three

ChromaDB4 of 231Not in the top three

MongoDB Atlas Vector Search4 of 230Not in the top three

Vespa4 of 230Not in the top three

A head-to-head page exists where both products are in a category's top three. The other rows are the same fact without a page behind them, so they link to the product instead.

What carried it into the answer

The sites and pages cited by the answers that named FAISS. A fact about retrieval, not a lever on the model.

Citations exist only for the models that return a source list, four of the twelve in this edition, so these counts come from 21 of the 23 answers that named FAISS and are not a share of its labels.

Domains cited

firecrawl.dev15

actian.com10

medium.com10

dev.to9

encore.dev9

sesamedisk.com9

redis.io8

pecollective.com7

atlan.com6

milvus.io6

Eighty-nine of the eighty-nine domain citations in answers naming FAISS came from somebody else's page.

Pages cited

firecrawl.dev/blog/best-vector-databases15

actian.com/blog/databases/how-to-evaluate-vector-databases-in-202610

encore.dev/articles/best-vector-databases9

sesamedisk.com/when-not-to-use-vector-database-20268

redis.io/blog/common-challenges-working-with-vector-databases6

atlan.com/know/top-vector-databases-enterprise-ai5

towardsdatascience.com/when-not-to-use-vector-db5

aitoolsbusiness.com/do-you-need-a-vector-database-sme-guide4

Pages are listed as the models cited them.

Names read as FAISS

What the judge wrote, as written, with how often. The vendor table decides that these count as FAISS; a claim can dispute any of them.

Faiss 6

Is this your product?

Claim this page

Claiming is free and changes nothing in the data. A claimed page shows a verified contact who is told when each edition publishes and when FAISS's standing changes by more than the noise floor; the right to propose corrections to the vendor table, meaning names the judge wrote that should or should not read as FAISS, applied by version and listed in the change log; and a one-line description supplied by the vendor and marked as such.

It does not get any change to labels, shares or verdicts, any preview, or any say over which quotes appear. A verification link goes to your work email; an address at faiss.ai is approved on the spot, any other address is reviewed by hand.

Your name and company appear on the claimed page, or the company alone if you ask below. A title and a LinkedIn address appear there too if you give them, and are left off if you do not. Your email address is never published.