Qdrant
Qdrant is a vector similarity search engine and vector database that offers a useful API to store, search and manage vectors carrying additional charges. Thanks to extended filtering support, neural network-based pairing is suitable for applications that require semantic search and multiple filtering. Because it is written in Rust, it provides a fast and reliable service even under high load.
Qdrant
100qdrant · Databases & Vector Infrastructure
Common commercial products in this category: Oracle Database, Microsoft SQL Server, Amazon Aurora
- You search for similarities by hiding vectors and additional charges.
- You perform semantic-based matching and searches with extended filtering support.
- You integrate it into your applications using official client libraries such as Python, Go, Rust, and JavaScript.
- • High number of open issues (793)
- ↑ PositiveHacker News
Qdrant, the Vector Search Database, raised $28M in a Series A round
“Depends, short term there is a lot of experimentation and innovation in this space obviously. But long term what matters (for investors) is defensible moats. Most or these products look a bit like one trick ponies to me with some features that are extremely likely to be borrowed…”
- ↑ PositiveHacker News
Qdrant 1.7.0
“As another signal, check out this report by Streamlit, which shows the popularity of different vector databases among Streamlit apps: Faiss and Pinecone are at the top (disclosure: I'm from Pinecone). But Faiss isn't really a full-fledged vector DB. Pinecone is a managed option…”
Verified against README · Oct 26, 2026 · source