Updated May 13, 2026 · Independent Analysis
Only in Milvus / Zilliz Cloud
- ✦ Distributed Architecture
- ✦ Billion-Scale Vectors
- ✦ ANNS Algorithms (HNSW, IVF, DiskANN)
✓ Free plan5k+ users · est. 2017

Qdrantvs Milvus / Zilliz Cloud ★ 4.5/5+0.1 vs Milvus / Zilliz Cloud
Only in Qdrant
- ✦ Written in Rust
- ✦ HNSW Index
- ✦ Sparse Vectors (BM25-compatible)
✓ Free plan3k+ users · est. 2021
Pricing Intelligence

Milvus / Zilliz Cloud Plans
Free tier available
Milvus Open Source0
Open Source- • Full features
- • Apache 2.0
- • Community support
Zilliz Cloud Serverless
Custom- • From $0.1/CU-hr
- • Managed Milvus
- • Auto-scaling
- • Dedicated clusters
- • SLA
- • Private VPC
Full Milvus / Zilliz Cloud Pricing Breakdown →
Qdrant Plans
Free tier available
- • Full features
- • Apache 2.0
- • Docker deployment
- • From $0.014/hr
- • Managed clusters
- • Free tier available
- • Private cloud
- • SSO
- • Dedicated support
Full Qdrant Pricing Breakdown →Feature Matrix
1 differences found across 10 standardized features
Feature
Milvus / Zilliz Cloud
Qdrant
Pros & Cons Face-Off
Evaluative strengths and weaknesses — not feature lists
Pros
- +CNCF project — battle-tested for billion-scale workloads
- +GPU acceleration and DiskANN for cost-efficient large-scale search
- +Distributed architecture with independent storage/compute scaling
- +Multi-vector search supports complex AI use cases
Cons
- −Operational complexity — requires Kubernetes expertise for self-hosted
- −Overkill for small-scale RAG applications
Pros
- +Top benchmark performance via Rust and quantization
- +Named vectors enable multimodal and complex search patterns
- +Binary quantization reduces memory 32x
- +Excellent documentation and developer experience
Cons
- −Smaller managed cloud ecosystem than Pinecone
- −Newer company — fewer enterprise customer references
At a Glance
Starting PricePay-per-usevsPay-per-use
Feature Count16 featuresvs16 features
Frequently Asked Questions