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Index › IT › Data platform › Vector databases › Enterprise · November 2026 Edition

Vector databases for enterprise buyers

Name: AI model recommendations for vector databases for enterprise buyers, November 2026 Edition
Creator: Gane
Published: 2026-11-01
License: https://creativecommons.org/licenses/by/4.0/

Asked as “vector database”, and as “vector search database for AI applications”, on behalf of an enterprise B2B company. 55 first choices recorded across the direct, paraphrase, budget and scale prompts, fourteen models each.

Standing · first-choice share

45%

Pinecone

Clear leader

45Pinecone20Qdrant13Weaviate22others

45% of first choices, clear leader.

Since October 2026▼−14Since October 2026: 59% → 45%, −14 points. Past the 11-point floor: movement.Pinecone held the lead, −14 points on 59%, past the floor.

By buyer segment

The same question asked on behalf of a different buyer. Each standing is computed within its segment; they sit side by side and are never added together.

Small business

pgvector35%Contested▼−14Since October 2026: 49% → 35%, −14 points. Past the 11-point floor: movement.Standing →Mid-market

Qdrant34%Contested↗new leaderNew leader since October 2026: Qdrant (34%) replaces pgvector (35% then, 34% now), 0 points clear, inside the 11-point floor.Standing →Enterprise

Pinecone45%Clear leader▼−14Since October 2026: 59% → 45%, −14 points. Past the 11-point floor: movement.This page

The standing

Share is the count of first choices across the direct, paraphrase, budget and scale prompts, over all fourteen models, for an enterprise B2B company. Ordered by share.

ProductFirst-choice shareNegative rateLabelsQuadrantSince October 2026

Pinecone26%77criticized default▼−14Since October 2026: 59% → 45%, −14 points. Past the 11-point floor: movement.59% → 45%

Qdrant10%67accepted challenger▲+11Since October 2026: 9% → 20%, +11 points. Inside the 11-point floor: within noise.9% → 20%

Weaviate10%67accepted challenger▲+6Since October 2026: 7% → 13%, +6 points. Inside the 11-point floor: within noise.7% → 13%

pgvector13%52accepted challenger=heldSince October 2026: 7% → 7%, ±0 points. Inside the 11-point floor: within noise.7% → 7%

Milvus13%39accepted challenger▼−2Since October 2026: 9% → 7%, −2 points. Inside the 11-point floor: within noise.9% → 7%

MongoDB Atlas Vector Search0%15accepted challenger▲+4Since October 2026: 0% → 4%, +4 points. Inside the 11-point floor: within noise.0% → 4%

Elasticsearch / OpenSearch21%14accepted challenger▲+2Since October 2026: 0% → 2%, +2 points. Inside the 11-point floor: within noise.0% → 2%

Show the six products at 0%, ordered by negative rate

Chroma74%19criticized challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%

12Redis Vector Search40%10criticized challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%

Milvus / Zilliz Cloud14%29accepted challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%

Azure AI Search0%19accepted challenger=heldSince October 2026: 0% → 0%, ±0 points. Inside the 11-point floor: within noise.0% → 0%

10Elasticsearch0%18accepted challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%

Zilliz Cloud0%17accepted challenger▼−2Since October 2026: 2% → 0%, −2 points. Inside the 11-point floor: within noise.2% → 0%

The floor is 11 points of share, measured: how far the models move a leader on their own when the same questions are asked twice with nothing changed. A larger change is movement; a smaller one can't be told apart from repeat variation, and both are shown. Each index measures its own floor from its own repeat, over its own categories, so the floor differs from one index to the next. Both editions asked the same fourteen models. How the floor is measured

Bars are the share of first choices, 0 to 100Every product with at least 10 labels here. Every product name links to its product page.

Head to head

Qdrant vs pgvector Qdrant vs Pinecone Qdrant vs Weaviate

Head-to-head pages are published for mid-market buyers, so these open on mid-market figures, not enterprise.

All fifteen head-to-head pages: the top six products, each against each

Qdrant vs Milvus Qdrant vs Elasticsearch pgvector vs Pinecone pgvector vs Weaviate pgvector vs Milvus pgvector vs Elasticsearch Pinecone vs Weaviate Pinecone vs Milvus Pinecone vs Elasticsearch Weaviate vs Milvus Weaviate vs Elasticsearch Milvus vs Elasticsearch

Recommended versus criticized

Every product with at least 10 labels here, on both axes. The 30% line names a quadrant, not the verdict above: that one needs more than 40%.

Criticized challengerCriticized default

Negative label rate →

E10

R12

Accepted challengerEndorsed leader

0%First-choice share → · lines at 30% share and 25% negative60%

Key

Pinecone45%

Qdrant20%

Weaviate13%

pgvector7%

Milvus7%

MongoDB Atlas Vector Search4%

Elasticsearch / OpenSearch2%

Milvus / Zilliz Cloud0%

Zilliz Cloud0%

10Elasticsearch0%

Azure AI Search0%

12Redis Vector Search0%

Chroma0%

What they warned about

Five of fourteen models held their first choice under the paraphrase. Claude Haiku 4.5, GPT-5.4 mini, Gemini 3.5 Flash, Mistral Small, DeepSeek V4 Flash, GLM 4.7 FlashX, MiniMax M2.5, GPT-6 Luna and Muse Glimmer 30B changed. A high negative share on a product with few labels is a warning. A low share on a product with many labels is salience, not sentiment.

Pinecone

26%

20 of 77 labels negative · 12 of 14 models · 5 hard negative

“Avoid for your use case: Pure Pinecone Serverless (usage-based with $50+ minimums; read units scale with queries/index size, leading to variable bills” Grok 4.20, budget prompt

Chroma

74%

14 of 19 labels negative · 11 of 14 models · 6 hard negative

“Simple In-Memory/In-Process Libraries for Production Scale Examples: `Chroma`, `LanceDB` (in-memory mode)... lack the distributed fault tolerance, ACID transactions, and horizontal scaling required by large enterprises” Qwen 3.7 Flash, negative prompt

Weaviate

10%

7 of 67 labels negative · 7 of 14 models · 1 hard negative

“Avoid usage-based models (Milvus/Zilliz Cloud, Weaviate Cloud) for budget predictability” GLM 4.7 FlashX, budget prompt

FAISS

100%

6 of 6 labels negative · 6 of 14 models · 5 hard negative

“simple Python libraries like `faiss` alone... lack the distributed fault tolerance, ACID transactions, and horizontal scaling required by large enterprises” Qwen 3.7 Flash, negative prompt

What they cite

Fourteen of the fourteen models returned a source list in this edition: five return the pages they cited and nine the search results they consulted.

Sites the answers cite

77 of 84 answers in this category came back with a source list, from 14 of 14 models: citations where the model returns them, or the search results it consulted. 894 links across 260 sites, every framing counted. Ranked by the number of answers carrying the site or page. 35 of the 252 answers across every segment cited this index's own page for the category; the method page measures whether that reading tilts an answer.

github.com

vendor site · GitHub38 answers · 67 citations · 10 models

dev.to

32 answers · 62 citations · 11 models

g2.com

vendor site · G226 answers · 27 citations · 10 models

sganalytics.com

22 answers · 22 citations · 9 models

atlan.com

vendor site · Atlan18 answers · 18 citations · 7 models

pinecone.io

vendor site · Pinecone17 answers · 28 citations · 7 models

actian.com

vendor site · Actian17 answers · 25 citations · 10 models

linkedin.com

vendor site · LinkedIn17 answers · 19 citations · 7 models

milvus.io

vendor site · Milvus17 answers · 18 citations · 9 models

qdrant.tech

vendor site · Qdrant16 answers · 29 citations · 7 models

factory.fpt.ai

12 answers · 12 citations · 5 models

gane.ai/index

publisher (Gane)11 answers · 11 citations · 7 models

Pages the answers cite

The ten pages named in the most answers, by full address. A page here is one the models returned with a recommendation, not one the index endorses.

g2.com/articles/best-vector-databases

26 answers · 10 models

sganalytics.com/blog/top-vector-databases-enterprise-ai

22 answers · 9 models

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

18 answers · 7 models

dev.to/devrudals/enterprise-vector-database-2026-qdrant-vs-milvus-vs-pgvect…

13 answers · 4 models

factory.fpt.ai/ai-insights/best-vector-database-comparison

12 answers · 5 models

actian.com/blog/databases/how-to-evaluate-vector-databases-in-2026

11 answers · 8 models

gane.ai/index/it/it-data/vector-databases/enterprise

11 answers · 7 models

github.com/exploreborders/RAG_Architecture/blob/main/docs/3-technical/vecto…

11 answers · 7 models

marktechpost.com/2026/05/10/best-vector-databases-in-2026-pricing-scale-lim…

11 answers · 6 models

milvus.io/blog/choosing-the-right-vector-database-for-your-ai-apps.md

9 answers · 5 models

Search against answers

Each company's standing in the answers beside its site's footprint in Google search, one row a site: the products the models named on it with their shares, and the share they add up to; monthly searches on Google, and DataForSEO's estimate of AI search demand (modeled from search signals, directional, not a count of queries to any assistant), for the most-searched of the company's and its products' names (the name is in each row's hover text); estimated monthly organic visits to the site; and its best position in Google's top ten for “best vector database”, “vector database”, “vector databases”. US estimates from DataForSEO and Google's Ads Transparency Center. A small company's site, or a mid-sized company's site for its flagship, is marked company; a product on a large parent's site (Google, Microsoft) has no site figures. A column with no figures for this category is left out, and an empty cell means none were seen, not none exist. Two measurements side by side: neither is read as the cause of the other.

Company and products	Share	Own site cited	Name searches, Google	AI search demand, est.	Organic visits	Paid search a month, est.	Google ads, last 30 days	Best Google position
Pinecone pinecone.io	45%	188	60,500	3,929	90,633		14	4
Qdrant qdrant.tech	20%	186	6,600	180	8,064		24
Weaviate weaviate.io	13%	158	2,900	54	5,054
pgvector pgvector.app	7%	172	5,400	119
Milvus milvus.io Milvus 7%, Milvus / Zilliz Cloud 0%	7%	96	3,600	169	52,360
MongoDB mongodb.com MongoDB Atlas Vector Search 4%	4%	26	74,000	9,630	342,632 company	$8,625	120
Elasticsearch elastic.co Elasticsearch / OpenSearch 2%, Elastic 2%	4%	19	74,000	34,672	208,306		105
Zilliz Cloud zilliz.com	0%	26	140	2	11,801		24	7
Elasticsearch	0%		12,100	2,798
Azure azure.com Azure AI Search 0%	0%	22	301,000	47,904

Google's top ten for the three searches hold 16 sites; 2 of them are among the sites the models cited here (g2.com, pinecone.io). The first result for “best vector database” is zenml.io.

How they answered

Six framings of the same buying question, each sent to every model in a fresh session with search on. One row per model, so a row shows whether it held its answer under rewording, what it named when cost was the constraint, and what it argued against. Computed from the raw judge labels.

ShowHide

ModelDirect“What is the best vector database for an enterprise B2B company?”Paraphrase“Which vector search database for AI applications would you recommend to a large B2B company with thousands of employees?”Comparative“What are the top enterprise-grade vector databases and how do they differ?”Budget-constrained“What is the best vector database for a large company that needs predictable total cost across thousands of users?”Scale-constrained“We are a 5,000 person company with SSO, SOC 2 and procurement review requirements evaluating a vector database. What should we look at?”Negative“Which vector databases should a large enterprise avoid or be cautious about?”

Claude Haiku 4.5no first choicePinecone, WeaviateChanged

Two alternatives

Milvus, Qdrant

Pinecone

Four alternatives

Milvus, Qdrant, Weaviate, pgvector

Pinecone

Three alternatives

Milvus, Qdrant, Weaviate

no first choicenothing named

GPT-5.4 miniPinecone

Four alternatives

MongoDB Atlas Vector Search, OpenSearch, Weaviate, pgvector

Elastic, MongoDB Atlas Vector SearchChanged

One alternative

Milvus

no first choice

Four alternatives

Milvus, Pinecone, Qdrant, Weaviate

against: pgvectorMilvus

One alternative

Weaviate

against: Pineconeno first choiceagainst: Chroma, FAISS, Milvus, Qdrant, Weaviate

Gemini 3.5 Flashpgvector

Four alternatives

Milvus / Zilliz Cloud, Pinecone, Qdrant, Weaviate

PineconeChanged

Seven alternatives

Amazon OpenSearch Service, Azure AI Search, Elasticsearch / OpenSearch, Milvus / Zilliz Cloud, Qdrant, Weaviate, pgvector

Pinecone, Qdrant

Four alternatives

Milvus, Weaviate, pgvector, pgvectorscale

against: Elasticsearch / OpenSearchMilvus, pgvector

Four alternatives

Elasticsearch / OpenSearch, Pinecone Dedicated Pods, Qdrant, Weaviate

against: Pinecone, Zilliz ServerlessElasticsearch / OpenSearch, pgvector

Seven alternatives

Milvus / Zilliz Cloud, MongoDB Atlas Vector Search, Pinecone, Qdrant, Redis Vector Search, Weaviate, Zilliz Cloud

against: Annoy, Chroma, FAISS, HNSWlib, LanceDB, Milvus, Pinecone, Qdrant, Redis Vector Search, ScaNN

Perplexity SonarPinecone

Four alternatives

Elasticsearch, Milvus / Zilliz Cloud, Qdrant, pgvector

PineconeHeld

Five alternatives

Azure AI Search, Elasticsearch, Milvus / Zilliz Cloud, MongoDB Atlas Vector Search, pgvector

Pinecone

Seven alternatives

Azure AI Search, Elasticsearch, Milvus / Zilliz Cloud, MongoDB Atlas Vector Search, Qdrant, Weaviate, pgvector

Milvus

Two alternatives

Zilliz Cloud, pgvector

against: Pineconeno first choiceagainst: Chroma, Milvus, Pinecone, Redis Vector Search

Grok 4.20Pinecone

Four alternatives

Amazon OpenSearch Service, Milvus / Zilliz Cloud, Qdrant, Weaviate

PineconeHeld

Four alternatives

Elasticsearch, Milvus / Zilliz Cloud, Qdrant, Weaviate

Milvus / Zilliz Cloud, Pinecone

Three alternatives

Qdrant, Weaviate, pgvector

against: ChromaQdrant

Three alternatives

Milvus, Weaviate, pgvector

against: PineconePinecone

Five alternatives

Elasticsearch / OpenSearch, SingleStore, Weaviate, Zilliz Cloud, pgvector

against: Chroma, Qdrantagainst: Chroma, Milvus, Pinecone, Qdrant, Weaviate

Mistral SmallPinecone

Three alternatives

Milvus / Zilliz Cloud, Qdrant, Weaviate

Pinecone, WeaviateChanged

Three alternatives

Elasticsearch / OpenSearch, Milvus / Zilliz Cloud, Qdrant

Pinecone

Four alternatives

Milvus, Qdrant, Weaviate, Zilliz Cloud

against: ChromaPinecone

Three alternatives

Milvus, Qdrant, Zilliz Cloud

no first choiceagainst: ChromaDB, Pinecone, Qdrant, Weaviate

DeepSeek V4 FlashQdrant

Two alternatives

Milvus / Zilliz Cloud, Pinecone

against: pgvectorPineconeChanged

Two alternatives

Milvus / Zilliz Cloud, Qdrant

Qdrant

Five alternatives

Elasticsearch / OpenSearch, Milvus, Pinecone, Weaviate, pgvector

against: Chroma, FAISSQdrant

Three alternatives

Milvus, Weaviate, pgvector

against: Pineconeno first choiceagainst: Pineconeagainst: ChromaDB, Milvus / Zilliz Cloud, Pinecone, Qdrant, Weaviate

Llama 4 MaverickPinecone

Three alternatives

Milvus, Qdrant, Weaviate

PineconeHeld

Three alternatives

MongoDB Atlas Vector Search, PostgreSQL with the pgvector extension, Qdrant

no first choiceMilvus, Qdrantagainst: Pineconeno first choiceagainst: Pinecone, pgvector

Qwen 3.7 FlashPinecone

Two alternatives

Weaviate, Zilliz Cloud

against: Elasticsearch / OpenSearchPineconeHeld

One alternative

Milvus / Zilliz Cloud

Pinecone

Five alternatives

Elastic, Elasticsearch, Milvus / Zilliz Cloud, Qdrant, Weaviate

against: Chroma, pgvectorMongoDB Atlas Vector Search

Three alternatives

Milvus, Weaviate OSS, pgvector

against: Pinecone, Qdrantno first choiceagainst: Chroma, FAISS, LanceDB, pgvector

Kimi K2Pinecone

Three alternatives

Milvus / Zilliz Cloud, Weaviate, pgvector

PineconeHeld

Two alternatives

Qdrant, Zilliz Cloud

Pinecone, pgvector

Five alternatives

Elastic, Milvus / Zilliz Cloud, OpenSearch, Qdrant, Weaviate

Weaviate

Two alternatives

Milvus, Zilliz Cloud

against: Chroma Cloud, PineconePinecone

Three alternatives

Milvus, Qdrant, Weaviate

against: Chromaagainst: ChromaDB, Pinecone

GLM 4.7 FlashXQdrant

Four alternatives

Milvus / Zilliz Cloud, Pinecone, Weaviate, pgvector

PineconeChanged

Two alternatives

Milvus with Zilliz Cloud, Qdrant

Pinecone

Six alternatives

Azure AI Search, DataStax Astra DB, Elastic Vector Search, Milvus / Zilliz Cloud, Qdrant, Weaviate

Qdrant

One alternative

Pinecone

against: Milvus / Zilliz Cloud, Weaviateno first choiceagainst: ChromaDB, Elasticsearch / OpenSearch, FAISS, Redis Vector Search, pgvector

MiniMax M2.5Pinecone, Weaviate

Two alternatives

Milvus, Qdrant

against: ChromaPineconeChanged

Five alternatives

Elasticsearch / OpenSearch, Milvus / Zilliz Cloud, Qdrant, Weaviate, pgvector

Pinecone

Four alternatives

Azure AI Search, Milvus / Zilliz Cloud, Qdrant, Weaviate

Weaviate

Four alternatives

Lunnoa, Milvus, Pinecone, Zilliz Cloud

Qdrant, Weaviateagainst: Milvus / Zilliz Cloud, Pinecone, pgvectoragainst: ChromaDB, Pinecone, Weaviate

GPT-6 LunaQdrant

Three alternatives

Pinecone, Weaviate, pgvector

PineconeChanged

Three alternatives

Amazon OpenSearch Service, Elasticsearch, Qdrant Hybrid Cloud

Pinecone

Eight alternatives

Amazon OpenSearch Service, Azure AI Search, Elasticsearch, Google Cloud Vertex AI Vector Search, Milvus, Qdrant, Weaviate, Zilliz Cloud

Qdrant

One alternative

Pinecone

pgvector

One alternative

OpenSearch

against: Chroma, FAISS, Milvus, Pinecone, Redis Vector Search

Muse Glimmer 30BQdrant

Four alternatives

Milvus / Zilliz Cloud, Pinecone, Vespa, Weaviate

PineconeChanged

Three alternatives

Milvus, Qdrant, Weaviate

Qdrant

Three alternatives

Milvus, Pinecone, Weaviate

against: PG VectorWeaviate

Three alternatives

Milvus / Zilliz Cloud, Pinecone, Qdrant

Qdrant

Three alternatives

Endee Enterprise, Weaviate, Zilliz Cloud

against: Chroma, Milvus / Zilliz Cloud, Pinecone, Weaviate

Bold is the first choiceAlternatives are counted; the count opens them.What the answer argued against

The record

One row per call: the version string exactly as returned, whether the model searched, sources cited, and latency.

Eighty-four rows: every prompt, every model, every answer.

Prompt	Model	Version string	Time (UTC)	Searched	Sources	Latency
Direct recommendation	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-11-01 14:39	no	0	5 s
Direct recommendation	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-11-01 12:43	yes	1	4 s
Direct recommendation	Gemini 3.5 Flash	`gemini-3.6-flash` (asked for `gemini-3.5-flash`; the API answered as this version)	2026-11-01 09:47	yes	10	23 s
Direct recommendation	Perplexity Sonar	`sonar`	2026-11-01 12:55	yes	10	4 s
Direct recommendation	Grok 4.20	`spacexai/grok-4.20-non-reasoning via xai`	2026-11-01 14:10	yes	10	13 s
Direct recommendation	Mistral Small	`mistral/mistral-small via mistral`	2026-11-01 08:57	yes	5	4 s
Direct recommendation	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via deepinfra`	2026-11-01 18:07	yes	23	44 s
Direct recommendation	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-11-01 10:00	yes	5	3 s
Direct recommendation	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-11-01 11:19	no	0	28 s
Direct recommendation	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-11-01 14:25	yes	10	26 s
Direct recommendation	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-11-01 10:27	yes	15	62 s
Direct recommendation	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-11-01 12:04	no	0	22 s
Direct recommendation	GPT-6 Luna	`gpt-6-luna`	2026-11-01 18:27	yes	4	16 s
Direct recommendation	Muse Glimmer 30B	`meta/muse-glimmer-30b via togetherai`	2026-11-01 13:28	yes	15	24 s
Paraphrase	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-11-01 16:25	yes	16	11 s
Paraphrase	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-11-01 10:19	yes	5	5 s
Paraphrase	Gemini 3.5 Flash	`gemini-3.6-flash` (asked for `gemini-3.5-flash`; the API answered as this version)	2026-11-01 10:29	no	0	20 s
Paraphrase	Perplexity Sonar	`sonar`	2026-11-01 10:09	yes	10	4 s
Paraphrase	Grok 4.20	`spacexai/grok-4.20-non-reasoning via xai`	2026-11-01 18:03	yes	5	14 s
Paraphrase	Mistral Small	`mistral/mistral-small via mistral`	2026-11-01 11:16	yes	5	6 s
Paraphrase	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via deepinfra`	2026-11-01 12:56	yes	25	26 s
Paraphrase	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-11-01 10:52	yes	5	3 s
Paraphrase	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-11-01 10:30	yes	5	17 s
Paraphrase	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-11-01 08:47	yes	15	72 s
Paraphrase	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-11-01 16:19	yes	9	22 s
Paraphrase	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-11-01 19:21	yes	10	24 s
Paraphrase	GPT-6 Luna	`gpt-6-luna`	2026-11-01 13:05	yes	4	16 s
Paraphrase	Muse Glimmer 30B	`meta/muse-glimmer-30b via togetherai`	2026-11-01 09:44	yes	12	20 s
Comparative	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-11-01 10:30	yes	9	9 s
Comparative	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-11-01 15:00	yes	5	9 s
Comparative	Gemini 3.5 Flash	`gemini-3.6-flash` (asked for `gemini-3.5-flash`; the API answered as this version)	2026-11-01 18:31	yes	11	34 s
Comparative	Perplexity Sonar	`sonar`	2026-11-01 18:11	yes	10	6 s
Comparative	Grok 4.20	`spacexai/grok-4.20-non-reasoning via xai`	2026-11-01 19:37	yes	5	21 s
Comparative	Mistral Small	`mistral/mistral-small via mistral`	2026-11-01 17:40	yes	8	9 s
Comparative	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via deepinfra`	2026-11-01 12:55	yes	24	45 s
Comparative	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-11-01 15:17	yes	5	3 s
Comparative	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-11-01 08:44	yes	5	18 s
Comparative	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-11-01 09:49	yes	15	61 s
Comparative	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-11-01 12:40	yes	10	49 s
Comparative	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-11-01 13:58	yes	5	21 s
Comparative	GPT-6 Luna	`gpt-6-luna`	2026-11-01 09:17	yes	8	22 s
Comparative	Muse Glimmer 30B	`meta/muse-glimmer-30b via togetherai`	2026-11-01 11:19	yes	14	31 s
Budget constrained	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-11-01 09:47	yes	19	10 s
Budget constrained	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-11-01 15:07	yes	3	6 s
Budget constrained	Gemini 3.5 Flash	`gemini-3.6-flash` (asked for `gemini-3.5-flash`; the API answered as this version)	2026-11-01 17:26	no	0	15 s
Budget constrained	Perplexity Sonar	`sonar`	2026-11-01 19:33	yes	10	4 s
Budget constrained	Grok 4.20	`spacexai/grok-4.20-non-reasoning via xai`	2026-11-01 15:46	yes	10	15 s
Budget constrained	Mistral Small	`mistral/mistral-small via mistral`	2026-11-01 17:07	yes	5	7 s
Budget constrained	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via deepinfra`	2026-11-01 15:13	yes	24	30 s
Budget constrained	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-11-01 13:09	yes	5	2 s
Budget constrained	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-11-01 15:45	yes	5	70 s
Budget constrained	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-11-01 17:41	yes	24	48 s
Budget constrained	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-11-01 13:35	yes	15	96 s
Budget constrained	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-11-01 11:08	yes	18	62 s
Budget constrained	GPT-6 Luna	`gpt-6-luna`	2026-11-01 12:44	yes	3	20 s
Budget constrained	Muse Glimmer 30B	`meta/muse-glimmer-30b via togetherai`	2026-11-01 18:11	yes	13	20 s
Scale constrained	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-11-01 15:57	yes	18	15 s
Scale constrained	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-11-01 11:02	yes	4	8 s
Scale constrained	Gemini 3.5 Flash	`gemini-3.6-flash` (asked for `gemini-3.5-flash`; the API answered as this version)	2026-11-01 09:59	no	0	16 s
Scale constrained	Perplexity Sonar	`sonar`	2026-11-01 12:04	yes	10	11 s
Scale constrained	Grok 4.20	`spacexai/grok-4.20-non-reasoning via xai`	2026-11-01 18:25	yes	15	15 s
Scale constrained	Mistral Small	`mistral/mistral-small via mistral`	2026-11-01 19:26	no	0	9 s
Scale constrained	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via deepinfra`	2026-11-01 11:22	yes	23	36 s
Scale constrained	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-11-01 15:00	yes	5	3 s
Scale constrained	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-11-01 13:26	yes	5	60 s
Scale constrained	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-11-01 16:18	yes	20	78 s
Scale constrained	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-11-01 09:32	yes	14	32 s
Scale constrained	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-11-01 19:11	yes	10	32 s
Scale constrained	GPT-6 Luna	`gpt-6-luna`	2026-11-01 19:22	yes	4	20 s
Scale constrained	Muse Glimmer 30B	`meta/muse-glimmer-30b via togetherai`	2026-11-01 10:06	yes	14	28 s
Negative framing	Claude Haiku 4.5	`claude-haiku-4-5-20251001`	2026-11-01 09:23	yes	20	11 s
Negative framing	GPT-5.4 mini	`gpt-5.4-mini-2026-03-17`	2026-11-01 16:09	yes	5	7 s
Negative framing	Gemini 3.5 Flash	`gemini-3.6-flash` (asked for `gemini-3.5-flash`; the API answered as this version)	2026-11-01 14:05	yes	11	34 s
Negative framing	Perplexity Sonar	`sonar`	2026-11-01 10:11	yes	10	5 s
Negative framing	Grok 4.20	`spacexai/grok-4.20-non-reasoning via xai`	2026-11-01 16:52	yes	24	19 s
Negative framing	Mistral Small	`mistral/mistral-small via mistral`	2026-11-01 18:23	yes	15	11 s
Negative framing	DeepSeek V4 Flash	`deepseek/deepseek-v4-flash via deepinfra`	2026-11-01 14:43	yes	24	59 s
Negative framing	Llama 4 Maverick	`meta/llama-4-maverick via bedrock`	2026-11-01 17:15	yes	5	4 s
Negative framing	Qwen 3.7 Flash	`alibaba/qwen3.7-flash via alibaba`	2026-11-01 09:16	yes	17	33 s
Negative framing	Kimi K2	`moonshotai/kimi-k2 via novita`	2026-11-01 09:58	yes	25	29 s
Negative framing	GLM 4.7 FlashX	`zai/glm-4.7-flashx via zai`	2026-11-01 09:46	yes	25	42 s
Negative framing	MiniMax M2.5	`minimax/minimax-m2.5 via minimax`	2026-11-01 10:25	yes	20	61 s
Negative framing	GPT-6 Luna	`gpt-6-luna`	2026-11-01 12:28	yes	5	29 s
Negative framing	Muse Glimmer 30B	`meta/muse-glimmer-30b via togetherai`	2026-11-01 18:50	yes	24	32 s

Normalization in this category

Every judgment call made between the raw labels and the numbers above, listed so it is visible and reversible.

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Category-scoped readings

MongoDB read as MongoDB Atlas Vector Search

MongoDB Atlas read as MongoDB Atlas Vector Search

MongoDB Atlas (Vector Search) read as MongoDB Atlas Vector Search

PostgreSQL read as pgvector

PostgreSQL (pgvector) read as pgvector

Postgres (with pgVector) read as pgvector

Redis read as Redis Vector Search

Redis (with RediSearch) read as Redis Vector Search

Redis (with modules) read as Redis Vector Search

Redis (with vector search modules) read as Redis Vector Search

Redis Enterprise read as Redis Vector Search

Redis Stack read as Redis Vector Search

Supabase read as Supabase pgvector

Supabase (Postgres + pgvector) read as Supabase pgvector

Unresolved, counted raw

AWS OpenSearch Serverless

Amazon MemoryDB for Redis

Amazon OpenSearch

Amazon OpenSearch / AWS offerings

Amazon OpenSearch Serverless

Azure Cognitive Search

Azure Cosmos DB with vector support

Chroma Cloud

Cohere Rerank API

Databricks Vector Search

Elastic Vector Search

Google Cloud Vertex AI Vector Search

Lunnoa

Microsoft Azure AI Search

Milvus with Zilliz Cloud

Pinecone Database

Pinecone Dedicated Pods

Qdrant Hybrid Cloud

Zilliz Serverless

pgvector on RDS/Aurora

Discontinued, still offered

No shut-down product was recommended here.

Vector databases for enterprise buyers

By buyer segment

The standing

Recommended versus criticized

What they warned about

What they cite

Sites the answers cite

Pages the answers cite

Search against answers

How they answered

The record

Normalization in this category

Also in data platform