# pgvector vs Weaviate: 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; two named Weaviate. Page: https://it-ai-index.com/it-data/vector-databases/pgvector-vs-weaviate/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| pgvector | 35% | #1 of 10 | 6% | 66 | 14 of 14 |
| Weaviate | 5% | #5 of 10 | 10% | 67 | 14 of 14 |

## The direct prompt, model by model

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

## 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 Weaviate

- "Weaviate's own security checklist says self-managed deployments allow anonymous queries by default unless you configure auth. That's fine for local dev, but risky for production." (GPT-5.4 mini, negative prompt, soft negative)
- "Weaviate Cloud if your costs need to be especially simple and predictable: Pricing can depend on vector dimensions..." (GPT-6 Luna, negative prompt, soft negative)
- "*Proprietary (e.g., Pinecone, Weaviate Cloud):* Easier to start, harder to leave later (vendor lock-in)." (Qwen 3.7 Flash, scale prompt, soft negative)
- "I'd recommend either Pinecone ... or Weaviate (if you want flexibility and cost control)" (Claude Haiku 4.5, direct prompt, first choice)
- "I'd suggest beginning with Weaviate or pgvector if you're already invested in PostgreSQL" (Claude Haiku 4.5, paraphrase prompt, first choice)
- "Weaviate (open‑source + managed cloud, built‑in multimodal & GraphQL APIs)" (GLM 4.7 FlashX, 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.