{
  "schema_version": 1,
  "category": "Vector databases",
  "slug": "vector-databases",
  "niche": "devops",
  "niche_name": "DevOps & developer-infrastructure tools",
  "niche_short": "DevOps & dev tools",
  "description": "Which vector databases do AI engines recommend when developers building RAG and AI features ask what to use to store and search embeddings?",
  "audience": "AI/ML and application engineers, and the founders + DevRel who sell vector search infrastructure",
  "methodology": {
    "metric": "share-of-model",
    "metric_definition": "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?",
    "runs_per_prompt_per_engine": 12,
    "engines": [
      "Perplexity",
      "Google Gemini",
      "ChatGPT (OpenAI)",
      "Claude (Anthropic)",
      "Grok (xAI)"
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    "engine_keys": [
      "perplexity",
      "gemini",
      "openai",
      "anthropic",
      "grok"
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    "n_prompts": 10,
    "total_answers": 360,
    "errors": 240,
    "confidence_interval": "Wilson score interval, 95% (z=1.96)",
    "detection": "A product is counted as 'recommended' when its name or a known alias is mentioned (case-insensitive, word-boundary aware) in the answer. Citations are counted when the product's own domain appears in the answer's source links. Detection is heuristic — a mention is not always a positive endorsement.",
    "mode": "live",
    "is_illustrative": false,
    "engines_measured": [
      "ChatGPT (OpenAI)",
      "Claude (Anthropic)",
      "Grok (xAI)"
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    "engine_keys_measured": [
      "openai",
      "anthropic",
      "grok"
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    "min_n_per_engine": 5,
    "coverage_note": "Measured on 3 of 5 engines this run — Perplexity, Google Gemini were unavailable and are excluded from every figure."
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    "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?",
    "Pinecone vs Weaviate for a production RAG pipeline?",
    "Qdrant vs Milvus for self-hosted vector search?",
    "What's a good Pinecone alternative that is open-source?",
    "Best vector database for hybrid keyword + semantic search?",
    "Which vector database do AI engineering teams recommend for scale?"
  ],
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      "name": "Pinecone",
      "domain": "pinecone.io"
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      "name": "Weaviate",
      "domain": "weaviate.io"
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      "name": "Qdrant",
      "domain": "qdrant.tech"
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    {
      "name": "Chroma",
      "domain": "trychroma.com"
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    {
      "name": "Milvus",
      "domain": "milvus.io"
    },
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      "name": "pgvector",
      "domain": "github.com"
    },
    {
      "name": "Redis",
      "domain": "redis.io"
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      "domain": "vespa.ai"
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      "name": "LanceDB",
      "domain": "lancedb.com"
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    {
      "name": "Turbopuffer",
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  "run_date": "2026-06-23",
  "last_updated": "2026-06-23",
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    "name": "Clear Cited",
    "url": "https://clearcited.com",
    "index_url": "https://clearcited.com/ai-visibility-index/"
  },
  "disclaimer": "Measurements reflect a point in time; AI engines change continuously. API answers approximate, but do not exactly replicate, the consumer apps. Clear Cited does not guarantee any product's ranking. "
}