# pgvector vs MongoDB Atlas Vector Search: 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; zero named MongoDB Atlas Vector Search. Page: https://it-ai-index.com/it-data/vector-databases/pgvector-vs-mongodb-atlas-vector-search/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| pgvector | 35% | #1 of 10 | 6% | 66 | 14 of 14 |
| MongoDB Atlas Vector Search | 2% | #7 of 10 | 8% | 12 | 8 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)
- Qwen 3.7 Flash: neither first, one named (first choices: Pinecone) (alternatives: MongoDB Atlas Vector Search, Weaviate)
- GLM 4.7 FlashX: neither first, one named (first choices: Qdrant, Weaviate) (alternatives: pgvector)
- GPT-6 Luna: neither first, one named (first choices: Qdrant) (alternatives: Pinecone, pgvector)
- Claude Haiku 4.5: neither named (first choices: Pinecone, Weaviate)
- GPT-5.4 mini: neither named (first choices: Pinecone) (alternatives: Qdrant, 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 MongoDB Atlas Vector Search

- "MongoDB Atlas gets pricing gripes in broader DB contexts" (Grok 4.1 Fast, negative prompt, soft negative)
- "the sweet spot today is MongoDB Atlas Vector Search (if your data already lives in MongoDB)" (GLM 4.7 FlashX, paraphrase prompt, first choice)
- "Stick with MongoDB Atlas if your existing data pipeline is already built around MongoDB documents." (Qwen 3.7 Flash, direct prompt, alternative)
- "Use pgvector or Atlas Vector Search" (Kimi K2, scale prompt, alternative)

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.