# Qdrant vs MongoDB Atlas Vector Search: which do AI models recommend for vector databases, October 2026

IT AI Recommendation Index, October 2026 Edition, Vector databases. Four of fourteen models named Qdrant first on the direct prompt; zero named MongoDB Atlas Vector Search. Page: https://it-ai-index.com/it-data/vector-databases/qdrant-vs-mongodb-atlas-vector-search/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Qdrant | 22% | #3 of 10 | 4% | 70 | 14 of 14 |
| MongoDB Atlas Vector Search | 2% | #7 of 10 | 8% | 12 | 8 of 14 |

## The direct prompt, model by model

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

## 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)

## 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.