AI Search Optimization (AEO/GEO) for developer tools

Get your vector database recommended when an AI engineer asks AI

When an AI engineer asks ChatGPT or Perplexity “What's the best vector database for a Series A startup building RAG in 2026?”, does your vector database show up? We measure it across all 5 engines - and run the whole SEO + content + authority stack beneath share-of-model - then get you into the answer.

Get a free teardown for your vector database See the measured vector databases leaderboard

Short answer: when an AI engineer asks AI “What's the best vector database for a Series A startup building RAG in 2026?”, the engines already return a shortlist - and we have measured it. In vector databases they name Weaviate most (19.1% share-of-model, measured 2026-07-01). Below: that reality, the prompts behind it, and how we get you into the answer.

The measured reality for Vector databases

Who AI actually recommends in vector databases - measured, not vibes

Not a screenshot. This is our public AI Visibility Index for vector databases: how often each product is named across all 5 engines, by share-of-model, with 95% confidence intervals. It re-measures every Index cycle, and this page re-stamps with it.

AI Visibility Index - Vector databases · share-of-modelmeasured snapshot · 2026-07-01

Measured AI Visibility Index leaderboard for Vector databases - share-of-model by product.
#	Product	Share	95% CI
1	Weaviate	19.1%	17.6-20.8
2	Qdrant	19%	17.4-20.7
3	Pinecone	18.8%	17.2-20.5
4	Milvus	15.5%	14.1-17.1
5	pgvector	13.8%	12.4-15.3

Engines: 5 of 5
Prompts: 10
Answers: 500
Runs/engine: 10
Interval: Wilson 95% confidence interval

Live proof, not a promise. Weaviate leads Vector databases with a 19.1% share-of-model (95% CI 17.6-20.8), across 500 AI answers - measured 2026-07-01. The top three - Weaviate, Qdrant, Pinecone - cluster at the top; the gap between #1 and #2 is just 0.1 points, close enough that a strong cycle could flip it.

Even the vector databases leader, Weaviate, appears in 92% of Grok answers but only 76% of Claude (measured 2026-07-01, n=100 per engine) - AI visibility is engine-specific. We measure the 5 AI engines — ChatGPT, Perplexity, Claude, Gemini, and Grok — plus Google's AI surfaces (AI Overviews & AI Mode), measured separately. See the full vector databases leaderboard →

The buyer prompts that decide vector databases

When a buyer asks AI one of these, the engines already return a shortlist - right now, whether or not you are on it. These are real questions from our pre-registered Index set for this category.

“What's the best vector database for a Series A startup building RAG in 2026?”
“Best vector database for a small team adding semantic search?”
“What vector database should we use for a Kubernetes microservices stack?”
“Most cost-effective vector database for a high-traffic AI app?”
“Best vector database for a team already on Postgres?”

How we get your vector database into the answer

About 96% of the sources AI cited in vector databases were third-party - comparison pages, community threads (Reddit, Dev.to) and documentation - not vendor domains. That off-site authority work is exactly what we do.

Measure your share of model

How often each of the 5 engines names you vs. the named leaders in vector databases - with confidence intervals, and the exact prompts where you are invisible.

Fix the foundation

Schema, entity and technical work built on a real SEO foundation (technical, SERP, backlinks) because AI citations ride on it - the ground AEO stands on for a vector database.

Produce the content AI cites

Answer-first articles, comparison and benchmark pages, and the community + docs presence that wins citations in this category - done for you, you approve before it publishes.

Earn the off-site authority

The third-party sources the engines pull from in vector databases - review sites, comparison posts, communities - worked on a prioritized, measured roadmap.

FAQ

Why does AI search matter for vector databases?

Buyers increasingly ask ChatGPT, Perplexity, Claude, Gemini, and Grok which vector database to use before they ever visit a vendor site. If your product is not named in those answers, it is invisible at the moment of choice - a competitor gets the shortlist. We measure exactly where you stand and get you into the answer.

Who does AI recommend for vector databases right now?

In our measured AI Visibility Index (2026-07-01), the top three by share-of-model are Weaviate, Qdrant, Pinecone. Weaviate leads Vector databases with a 19.1% share-of-model (95% CI 17.6-20.8), across 500 AI answers - measured 2026-07-01. Point-in-time; engines change continuously.

How do you measure AI visibility for vector databases?

We run the real buyer prompts 10+ times each across all 5 engines, compute your share-of-model with a 95% confidence interval, and map the fastest fixes. The vector databases leaderboard in our Index shows exactly what yours will look like.

Do the AI engines agree on vector databases?

Even the vector databases leader, Weaviate, appears in 92% of Grok answers but only 76% of Claude (measured 2026-07-01, n=100 per engine) - AI visibility is engine-specific. That is why every fix we ship is engine-by-engine, and why a single "we asked ChatGPT" screenshot misleads.

What does it take to get cited in vector databases?

See where AI puts your vector database

Measured across all 5 engines against the vector databases field above - free, no call.

Get a free teardown for your vector database

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