Milvus
Distributed vector DB built for billion-scale workloads. Heavy, capable, China-AI-aligned.
VISIT MILVUSQuick facts
- CategorySelf-hosted
- EngineGo / C++
- PricingFreemium
- LicenseApache-2.0
- Created2019
- GitHub stars32.6k
- Hybrid searchNative
- Edge-readyNo
- Multi-tenantNative
- Max dimensions32,768
What it is
Milvus is a distributed vector database designed for billion-scale workloads. Cluster-mode handles horizontal scale beyond what Weaviate or Qdrant target. Heavier operationally than the alternatives. Backed by Zilliz, deployed at scale across the China AI ecosystem and increasingly in Western enterprises.
Best for
- Genuinely massive vector workloads (1B+ vectors)
- Enterprise deployments with dedicated platform-engineering capacity
- Apps with multimodal data and complex similarity-search requirements
When not to pick it
Skip Milvus for any workload under 100M vectors — the operational complexity is real and Weaviate / Qdrant are simpler at that scale. Skip if your team does not have ops bandwidth for distributed-systems work.
My take
Milvus is the right answer at the very top of the scale curve. For most teams it is overkill; Weaviate or Qdrant is the better default.
Links
Compare Milvus side-by-side
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Read the take →If Milvus is your pick — the next conversation is short
The 30-min call is where your vector-DB choice becomes a real RAG architecture, a chunking + reranking strategy that actually works for your corpus, and a price range you can take to your stakeholders. Describe your data shape, your query patterns, your latency budget. I tell you whether Milvus is genuinely your fit.