IT AI Index

Index › Vendors › Vespa · September 2026 Edition

1 category · Named, not ranked

Vespa

20Judge labels

0First choices

4Negative labels

9 of 12Models named it

1Category

September 2026 Edition. Every number here is derived from the raw labels under vendor table vv2026-09.2, every buyer segment counted.

Standing

9 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Vespa was named 9 times in Vector databases, where Qdrant led with 37%. The labels and the evidence are below, counted exactly.

By buyer segmentRead the same way at every buyer size.

Small business24 of 25

under the label threshold

Mid-market10 of 24

under the label threshold

Enterprise10 of 43

under the label threshold

In vector databases · each standing computed within its segment · bars are 0 to 100 · the accent bar is the product's own best reading

Standing by category

Every category where a model named Vespa 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%	10 of 24	11%	9	under 10 labels · led by Qdrant at 37%

Movement

This is the first edition on this tier, so no move can be computed for Vespa 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 Vespa 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	0	0	0
GPT-5.4 mini	0	0	0	0
Gemini 3.5 Flash	0	0	1	1
Perplexity Sonar	0	0	0	0
Grok 4.1 Fast	0	2	0	2
Mistral Small	0	2	0	2
DeepSeek V4 Flash	0	1	0	1
Llama 4 Maverick	0	0	0	0
Qwen 3.7 Flash	0	0	0	0
Kimi K2	0	1	0	1
GLM 4.7 FlashX	1	0	0	1
MiniMax M2.5	1	0	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

Direct1 labelNone

Paraphrase0 labelsNone

Comparative10 labelsNone

Budget-constrained1 labelNone

Scale-constrained4 labelsNone

Negative4 labelsNone

First choiceAlternativeMentionNegative20 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.

“Choose Vespa if: You need real‑time updates (no index refresh lag).” GLM 4.7 FlashX · Vector databases · comparative prompt · alternative

“Hybrid search needs: Weaviate or Vespa” MiniMax M2.5 · Vector databases · comparative 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.

“Milvus / Vespa: ... complex to configure, and require dedicated DevOps/infrastructure resources” Gemini 3.5 Flash · Vector databases · direct prompt · hard negative

Named alongside

The products named in the same answers as Vespa, over the 20 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Vespa was named but was not.

ProductSame answerTook the first choice insteadHead to head

Pinecone20 of 207Not in the top three

Qdrant19 of 203Not in the top three

Weaviate19 of 201Not in the top three

pgvector13 of 203Not in the top three

Chroma13 of 202Not in the top three

Milvus / Zilliz Cloud10 of 201Not in the top three

Milvus10 of 200Not in the top three

Redis9 of 200Not in the top three

Elasticsearch8 of 200Not in the top three

LanceDB6 of 200Not 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 Vespa. 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 14 of the 20 answers that named Vespa and are not a share of its labels.

Domains cited

firecrawl.dev13

encore.dev11

dev.to8

iternal.ai8

marktechpost.com7

zenml.io6

atlan.com5

strapi.io5

superlinked.com5

1bench.dev4

Seventy-two of the seventy-two domain citations in answers naming Vespa came from somebody else's page.

Pages cited

firecrawl.dev/blog/best-vector-databases13

encore.dev/articles/best-vector-databases11

iternal.ai/insights/best-vector-databases-20268

dev.to/michaelaiglobal/beyond-pinecone-a-developers-deep-dive-into-the-top-10-vector-datab6

zenml.io/blog/vector-databases-for-rag6

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

strapi.io/blog/best-vector-databases-ai-applications5

1bench.dev/databases/vector4

Pages are listed as the models cited them.

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 Vespa'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 Vespa, 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 vespa.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.