Qdrant vs Milvus
Qdrant and Milvus are both evaluated in the CIOPages Vector Database & AI Search Buyer's Guide. The guide groups Qdrant under Filtering & Rust and Milvus under Billion-Scale.
| Attribute | Qdrant | Milvus / Zilliz |
|---|---|---|
| Approach | Filtering & Rust | Billion-Scale |
| Best for | Workloads that mix vector similarity with heavy structured filtering — recommendations, personalization, multi-tenant RAG — where filtered-ANN quality is the deciding factor | Large-scale AI applications with hundreds of millions to billions of vectors that need maximum index flexibility and GPU-accelerated search |
From the Vector Database & AI Search Buyer's Guide, updated June 2026. 8 vendors evaluated.
Qdrant
Filtered search is where it separates itself: an Apache-2.0 engine written in Rust with a reputation for memory efficiency, where metadata filtering is integrated into traversal rather than bolted on, so recall holds under selective filters, with recent releases pushing quantization hard through TurboQuant-class compression and low-memory modes, and Qdrant Cloud offering managed deployment on AWS, GCP, and Azure behind fresh growth funding. The enterprise install base and partner ecosystem are smaller than the incumbents’, there are fewer batteries-included extras — no native vectorizer modules, so you bring your own embeddings — and the advanced tuning and quantization knobs reward teams willing to learn them.
Milvus / Zilliz
Built for billion-vector workloads, and the index menu proves it: an open-source, Apache 2.0 database under the LF AI & Data Foundation with Zilliz as lead contributor, offering HNSW, IVF variants, DiskANN, and GPU-accelerated CAGRA on a distributed, cloud-native architecture, with Zilliz Cloud delivering it fully managed for teams that do not want to operate the cluster. Self-hosting the distributed deployment is genuinely complex and component-heavy, GPU indexing means GPU cost and operational know-how to reach peak throughput, and the breadth of options is more than smaller corpora need, with right-sizing the index taking real expertise.
Also evaluated in this guide
Databricks Vector Search, Elastic / Elasticsearch, MongoDB Atlas Vector Search, pgvector (PostgreSQL), Pinecone, Weaviate
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