Qdrant
Fewer than 5 people recommend it so far, so there's no written verdict. Here is what commenters said.
4recommend
1warn against
Nov 15, 2025first mention
Aug 26, 2026latest mention
Why people recommend it
“I use QDrant as the main DB, the one every RAG Pipeline requires”
“have been pretty happy with it”
Show 3 more
“in conjunction with a Qdrant vector database”
“Qdrant supports storing embeddings in memory as well as in a local directory (similar to sqlite)”
“At least, with Qdrant, it works like this”
Caveats and dissent
“including milvus, qdrant, and weaviate”
Often compared with
Who recommends it in their own words
From the makers not counted in the ranking
“our Qdrant-based system”
“Qdrant for vector search.”
Threads where it came up
- Ask HN: What Are You Working On? (April 2026) 2 mentions
- Show HN: Phantom – Open-source AI agent on its own VM that rewrites its config 2 mentions
- GERP – A Headless ERP Built with Go, Spanner, Temporal, and GCP Vector 2 mentions
- RAG Is Simpler Than You Think 1 mention
- Qwen3.8 Max now ranked as the best overall model by agentic index 1 mention
- Ask HN: Who wants to be hired? (August 2026) 1 mention
Data: qdrant.json. Quotes are verbatim; the verdict and theme labels are written by a model from these comments, and every clause links to its sources.
All comments 34
“using a Vector Database like Qdrant for persistence”
“in conjunction with a Qdrant vector database”
“rebuilt Qdrant retrieval with clause-aware chunking and cross-encoder reranking”
“I use QDrant as the main DB, the one every RAG Pipeline requires”
“powered by a Qdrant vectordb”
“have been pretty happy with it”
“using qdrant to do embeddings and reranking”
“vector similarity (Qdrant) to classify docs”
“a shared Qdrant vector space”
“It's a tool that use QDrant, a vectorial db”
“persistent vector memory across sessions (Qdrant, local)”
“By utilizing Google's GCP data suite (Vector/ScaNN) and Qdrant”
“By utilizing Google's GCP data suite (Vector/ScaNN) and Qdrant”
“I used QDrant and openAi embedding model.”
“I've got a qdrant based approach”
“Context retrieval (Qdrant + Voyage AI): 50ms”
“in conjunction with a Qdrant vector database”
“Distributed Qdrant writes”
“L1: Qdrant vectors + 2-tier cache”
“Qdrant for vector storage”
“when I have access to SQLite-vec, qdrant and other embedded friends?”
“FalkorDB + Qdrant”
“implemented using Qdrant + Ollama (nomic-embed-text)”
“The shard failure warnings look serious, but the issues look closed”
“Scales via Qdrant.”
“Qdrant supports storing embeddings in memory as well as in a local directory (similar to sqlite)”
“We pay 250€ / mo for all of the qdrant instances across all environments”
“including milvus, qdrant, and weaviate”
“without adding the complexity of a separate service like Qdrant or Pinecone”
“Ollama, Chroma/Qdrant”
“Qdrant running locally”
“bm25 (via qdrant fusion)”
“At least, with Qdrant, it works like this”
“hybrid search on Qdrant (BM25 + dense)”