IT AI Index

Index › Vendors › Turbopuffer · September 2026 Edition

1 category · Named, not ranked

Turbopuffer

2Judge labels

0First choices

0Negative labels

2 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

2 labels, too few to rank

A product needs 10 labels in a category before a share or quadrant is stated. Turbopuffer was named 2 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 businessnot named

No model named it for this buyer.

Mid-market14 of 24

under the label threshold

Enterprisenot named

No model named it for this buyer.

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 Turbopuffer 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%	14 of 24	0%	2	under 10 labels · led by Qdrant at 37%

Movement

This is the first edition on this tier, so no move can be computed for Turbopuffer 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 Turbopuffer across every prompt where it was named for a mid-market B2B company. Twelve models, six prompts per category.

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

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

Paraphrase0 labelsNone

Comparative1 labelNone

Budget-constrained1 labelNone

Scale-constrained0 labelsNone

Negative0 labelsNone

First choiceAlternativeMentionNegative2 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.

“The Best "Zero-Ops" Serverless Choice: Turbopuffer ... has no minimum monthly platform fees” Gemini 3.5 Flash · Vector databases · budget 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.

No model argued against it.

Named alongside

The products named in the same answers as Turbopuffer, over the 2 answers that named it. Took the first choice instead counts the answers where the other product was the first choice and Turbopuffer was named but was not.

ProductSame answerTook the first choice insteadHead to head

Chroma2 of 20Not in the top three

LanceDB2 of 20Not in the top three

Pinecone2 of 20Not in the top three

Qdrant2 of 20Not in the top three

Weaviate2 of 20Not in the top three

pgvector1 of 21Not in the top three

Elasticsearch / OpenSearch1 of 20Not in the top three

FAISS1 of 20Not in the top three

Milvus1 of 20Not in the top three

Milvus / Zilliz Cloud1 of 20Not 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 Turbopuffer. 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 1 of the 2 answers that named Turbopuffer and are not a share of its labels.

Domains cited

acecloud.ai1

avondale.ai1

dataaihub.co1

dev.to1

developersdigest.tech1

encore.dev1

firecrawl.dev1

greenrobot.org1

ibm.com1

iternal.ai1

Ten of the ten domain citations in answers naming Turbopuffer came from somebody else's page.

Pages cited

acecloud.ai/blog/best-vector-databases-for-multimodal-genai1

avondale.ai/technical/rag/vector-database-comparison.html1

dataaihub.co/learn/vector-databases1

dev.to/michaelaiglobal/beyond-pinecone-a-developers-deep-dive-into-the-top-10-vector-datab1

developersdigest.tech/blog/vector-database-comparison-rag-agents-20261

encore.dev/articles/best-vector-databases1

firecrawl.dev/blog/best-vector-databases1

greenrobot.org/database/top-vector-databases1

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 Turbopuffer'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 Turbopuffer, 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 turbopuffer.com 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.