# Qdrant vs Milvus: 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 Milvus. Page: https://it-ai-index.com/it-data/vector-databases/qdrant-vs-milvus/

| | First-choice share | Rank | Negative rate | Labels | Models naming it |
|---|---|---|---|---|---|
| Qdrant | 22% | #3 of 10 | 4% | 70 | 14 of 14 |
| Milvus | 3% | #6 of 10 | 20% | 41 | 14 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)
- Kimi K2: neither first, one named (first choices: pgvector) (alternatives: Pinecone, Qdrant, Weaviate)
- MiniMax M2.5: neither first, one named (first choices: Pinecone) (alternatives: Elasticsearch, Milvus, 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)
- Qwen 3.7 Flash: neither named (first choices: Pinecone) (alternatives: MongoDB Atlas Vector Search, 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 Milvus

- "heavy self-hosted systems like Milvus (requires Kubernetes know-how) ... neither is friendly to a limited budget" (DeepSeek V4 Flash, budget prompt, hard negative)
- "Caution: If you run Milvus self-hosted, you must be on 2.4.24+, 2.5.21+ or 2.6.5+ and strip `sourceID` at the gateway" (Muse Glimmer 30B, negative prompt, soft negative)
- "handles billions of vectors at lower cost; requires engineering resources... more resource-intensive to operate" (Claude Haiku 4.5, comparative prompt, soft negative)
- "likely to be an open-source option such as Milvus or Chroma, as they offer free or low-cost options" (Llama 4 Maverick, budget prompt, first choice)
- "I'd recommend starting with either self-hosted Milvus/Qdrant (if you have DevOps capacity)" (Claude Haiku 4.5, budget prompt, first choice)
- "Massive Scale / Billions of vectors | Milvus" (Qwen 3.7 Flash, comparative 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.