Subcategory · AI Citation Index
Vector Databases
Vector databases show a three-way tie at the top of AI attention. Weaviate, Pinecone, and Qdrant each surface in more than 93% of buyer queries about embedding storage and semantic search, visible across all four engines we track. Milvus holds the kingmaker slot — it appears in 92% of discovery prompts and wins more head-to-head comparisons than any other brand (84/100 across 48 matchups), yet trails the consensus trio in raw mention volume. MongoDB is the riser — gaining 13 points month-over-month and scoring 71/100 in head-to-heads despite surfacing in only half of discovery queries. This is a consolidated category with a clear consensus shortlist, but evaluation scores cluster tightly — the gap between the top-rated brand and the median is narrow, signaling that buyers see functional parity across the top five.
157 discovery queries · 466 head-to-heads · refreshed Aug 16, 2026
Discovery stage
The shortlist
Across 157 buyer-style "Vector Databases" queries
Weaviate, Pinecone, and Qdrant form the consensus shortlist — each surfaces in more than 93% of buyer queries about vector databases, visible on ChatGPT, Claude, Gemini, and Perplexity. Milvus trails by two points at 92%, then PostgreSQL (with pgvector) drops to 82%. Zilliz Cloud, the managed Milvus service, surfaces in 80% of queries. Elasticsearch and OpenSearch each land in roughly two-thirds of discovery prompts, while Chroma holds 64%.
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Signal by intent
By topic
Top 5 most-cited brands per intent cluster. Brands with zero citations in a topic are not shown.
Evaluation stage
Head-to-head
How often AI cites each brand across uniform category evaluation prompts · median 35/100
When buyers ask AI to compare vector databases head-to-head, Weaviate, Qdrant, and Milvus tie at 84/100 — winning most matchups they enter. Pinecone follows at 82/100 across 47 comparison queries. OpenSearch scores 81, Elasticsearch 79. MongoDB wins more head-to-heads than it loses (67/100 across 38 comparisons) despite thin discovery share, while Zilliz Cloud scores 75.
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Each brand's score is the share of category evaluation prompts where AI cited them across all four engines — the same prompt pool for every brand. Brands above the median citation rate have stronger presence in evaluation-stage queries.
Brands to know
In this category
Weaviate
Consensus pickOpen-source vector database with GraphQL API
Read brand profile →Pinecone
Consensus pickFully managed vector database as a service
Read brand profile →Qdrant
Consensus pickRust-based open-source vector search engine
Read brand profile →Milvus
KingmakerOpen-source vector database for production-scale embeddings
Read brand profile →Mongodb
RiserDocument database with native vector search
Read brand profile →Citation sources
Where AI pulls citations from
1000 citations captured across Vector Databases prompt runs.
Vendor pages
341Product, help, and marketing pages from tracked vendors
Independent sources
534Reviews, encyclopedias, forums, press — not vendor-owned
Buyer questions
What AI cites for top Vector Databases questions
Buyers ask AI for vector database recommendations by team size and use case — phrasings like 'good vector database options for collaborative research teams', 'top vector database solutions for a one-person startup', 'vector database solutions for local service providers'. A smaller share of queries focuses on evaluation mechanics — how to assess documentation quality, vendor selection checklists, common mistakes when picking a vector database. No pricing or trust-stage prompts appear in the current prompt set; the signal is entirely top-of-funnel exploration and framework-building.
Discovery
Buyers exploring the categoryEvaluation
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