# pgvector vs Qdrant: which do AI models recommend for vector databases, October 2026

IT AI Recommendation Index, October 2026 Edition, Vector databases. Eight of fourteen models named pgvector first on the direct prompt; four named Qdrant. Page: https://it-ai-index.com/it-data/vector-databases/pgvector-vs-qdrant/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| pgvector | 35% | #1 of 10 | 6% | 66 | 14 of 14 |
| Qdrant | 22% | #3 of 10 | 4% | 70 | 14 of 14 |

## The direct prompt, model by model

- Perplexity Sonar: both first (first choices: Qdrant, pgvector) (alternatives: Pinecone, Weaviate)
- DeepSeek V4 Flash: both first (first choices: Qdrant, pgvector) (alternatives: Pinecone, Weaviate)
- Gemini 3.5 Flash: pgvector first (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- Grok 4.1 Fast: pgvector first (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- Mistral Small: pgvector first (first choices: pgvector) (alternatives: pgvectorscale)
- Llama 4 Maverick: pgvector first (first choices: pgvector)
- Kimi K2: pgvector first (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- Muse Glimmer 30B: pgvector first (first choices: Pinecone, pgvector) (alternatives: Qdrant, Weaviate)
- GLM 4.7 FlashX: qdrant first (first choices: Qdrant, Weaviate) (alternatives: pgvector)
- GPT-6 Luna: qdrant first (first choices: Qdrant) (alternatives: Pinecone, pgvector)
- GPT-5.4 mini: neither first, one named (first choices: Pinecone) (alternatives: Qdrant, Weaviate)
- Claude Haiku 4.5: neither named (first choices: Pinecone, Weaviate)
- Qwen 3.7 Flash: neither named (first choices: Pinecone) (alternatives: MongoDB Atlas Vector Search, Weaviate)
- MiniMax M2.5: neither named (first choices: Pinecone) (alternatives: Elasticsearch, Milvus, Weaviate)

## What the models said about pgvector

- "| Poor filtered search | pgvector, basic setups | ... | Reindexing locks | pgvector | Large HNSW rebuilds block reads/writes" (Grok 4.1 Fast, negative prompt, soft negative)
- "pgvector when you expect very high vector-search throughput or huge scale without testing" (GPT-6 Luna, negative prompt, soft negative)
- "Be cautious at massive scale ... you must be cautious of its ceilings" (Gemini 3.5 Flash, negative prompt, soft negative)
- "Unless you have 2-3 people who can own this infrastructure, lean managed or existing DB extension first.... Already run Postgres heavily, <10M vectors | pgvector" (Kimi K2, scale prompt, first choice)
- "The best vector database for a mid-market B2B company is pgvector, which is a PostgreSQL extension. It is the strongest default for mid-market enterprises." (Llama 4 Maverick, direct prompt, first choice)
- "evaluating pgvector might save you the overhead of learning a new technology, as it handles vector search well within a relational environment" (Qwen 3.7 Flash, scale prompt, first choice)

## What the models said about Qdrant

- "The open source engine is strong technically, but self-hosting requires hardening defaults and active patching." (Muse Glimmer 30B, negative prompt, soft negative)
- "Qdrant \u2013 Known production issues ... Performance issues, GLIBC issues, steep learning curve" (GLM 4.7 FlashX, negative prompt, soft negative)
- "Qdrant had a reported arbitrary-file-write issue involving its logger endpoint." (GPT-6 Luna, negative prompt, soft negative)
- "For most mid-market companies, pgvector or Qdrant represent the best balance of cost, capability, and practical feasibility" (DeepSeek V4 Flash, direct prompt, first choice)
- "I would usually recommend Qdrant if you want a strong balance of production readiness, flexibility, and cost control" (Perplexity Sonar, paraphrase prompt, first choice)
- "I'd recommend Qdrant for most mid-market B2B use cases because it appears most consistently favored for this segment" (Perplexity Sonar, direct prompt, first choice)

Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category. Comparisons are drawn for the top eight products in each category. Published under CC BY 4.0; the output is the models' output, and nothing here is a recommendation by the index.