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Which vector databases do AI engines actually recommend?

As of June 20, 2026, Pinecone leads with a 19.7% share-of-model — it appears in 19.7% of all vector databases recommendations across 5 major AI engines.

See your product's share-of-model — free How we measured this
Illustrative methodology preview. This table is generated in mock mode to show the format and method — it is not a live measurement yet. The first public edition will be a real multi-engine run. Get your own measured share-of-model →

The leaderboard — Vector databases by share-of-model

Which vector databases do AI engines recommend when developers building RAG and AI features ask what to use to store and search embeddings? We ran 10 buyer prompts × 10 runs across 5 engines (Perplexity, Google Gemini, ChatGPT (OpenAI), Claude (Anthropic), Grok (xAI)) — 500 total AI answers. Last updated June 20, 2026.

Pinecone leads our AI Visibility Index for Vector databases at 20% share-of-model across 5 AI engines.

10 buyer prompts · 10 runs/engine · June 20, 2026Updated monthly
#ProductShare of modelSoM95% CIPerplexityGoogle GeminiChatGPT (OpenAI)Claude (Anthropic)Grok (xAI)
1Pinecone pinecone.io19.7%18.1%–21.3%98%87%93%96%97%
2Weaviate weaviate.io17.0%15.6%–18.6%86%87%82%73%79%
3Qdrant qdrant.tech13.3%12.0%–14.8%62%65%62%61%69%
4Chroma trychroma.com10.7%9.6%–12.0%56%57%42%57%45%
5Milvus milvus.io10.6%9.4%–11.9%49%42%57%47%59%
6pgvector github.com8.2%7.2%–9.4%41%39%47%34%36%
7LanceDB lancedb.com6.1%5.2%–7.2%34%22%25%34%32%
8Redis redis.io5.1%4.3%–6.1%20%32%24%25%22%
9Turbopuffer turbopuffer.com4.9%4.1%–5.9%30%8%24%32%24%
10Vespa vespa.ai4.2%3.5%–5.1%19%21%20%29%12%

Share of model = % of all recommendations (across every prompt and engine) that named the product. Per-engine columns show how often each engine recommends the product. CI = Wilson 95% confidence interval on share-of-model.

Methodology — reproducible, not vibes

A single AI screenshot is one sample from a distribution. This Index treats AI visibility as the statistical question it is: every buyer prompt is run 10+ times per engine, and we report the median rate with a 95% confidence interval.

Buyer "money prompts"

10 real buyer questions a person would ask an AI when choosing vector databases (e.g. "best vector databases for a Series A startup"). Each prompt is run 10+ times per engine.

5 engines, measured separately

We query Perplexity, Google Gemini, ChatGPT (OpenAI), Claude (Anthropic), Grok (xAI) independently, because the engines disagree — being strong in one says nothing about the others. Per-engine columns expose that spread.

Share-of-model + Wilson CI

For each product we report its share of all recommendations in the field, with a 95% Wilson confidence interval — the honest way to summarize a small, noisy sample.

Heuristic detection, disclosed

A product is "recommended" when its name or a known alias is named in the answer (boundary-aware); a citation is counted when its own domain appears in the answer's sources. A mention is not always a positive endorsement — we say so.

API answers approximate, but do not exactly replicate, the consumer apps (different system prompts, tools, browsing defaults). We treat each edition as a point-in-time measurement and re-run on a cadence.

FAQ

Which vector databases do AI engines recommend most?

This vector databases page is an illustrative methodology preview, not a live measurement. In this mock-data sample Pinecone shows the highest share-of-model (19.7%) to demonstrate the format — the first public edition will be a real multi-engine run. See the table above for the layout.

What is share-of-model?

Share-of-model = the percentage of all product recommendations, across every buyer prompt and engine, that name a given product. It answers: when an AI recommends something in this category, how often is it this product?

How is the AI Visibility Index measured?

Each buyer prompt is run at least 10 times per engine across Perplexity, Google Gemini, ChatGPT (OpenAI), Claude (Anthropic), Grok (xAI); we detect which products each answer recommends, compute share-of-model, and report a Wilson 95% confidence interval. The method is reproducible — the same one Clear Cited uses in its paid audits. Note: this category page is currently an illustrative preview generated in mock mode, so the numbers shown are placeholders that demonstrate the method, not measured results.

Why do different AI engines recommend different products?

AI answer engines draw on different training data, retrieval sources, and ranking — so the 'best tool' a buyer hears depends heavily on which assistant they ask. That is why the Index measures and reports each engine separately.

Is this ranking sponsored or pay-to-play?

No. The AI Visibility Index is free, independent original research. Products are not charged to appear and cannot pay to rank higher. It reflects what AI engines actually say, measured transparently.

Want your product's real share-of-model?

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Free, independent original research. Products cannot pay to appear or to rank higher. Measurements reflect a point in time; AI engines change continuously and are outside our control. Last updated June 20, 2026.