Qdrant vs Milvus
Both are alternatives to Pinecone. Here's how they stack up — verified facts, no spin.
Also searched as Milvus vs Qdrant — same comparison, one verdict.
Qdrant
TOP PICKFast, open-source, one binary — the default self-hosted vector DB.
Qdrant is a high-performance vector database written in Rust and licensed Apache-2.0 — free to self-host with no usage limits or feature gates. It ships as a single self-contained binary with REST and gRPC APIs, runs via Docker or Kubernetes (official Helm chart and Operator), and offers a managed Qdrant Cloud on AWS, GCP, and Azure when you'd rather not operate it. Strong filtering, quantization, and hybrid search make it the most common 'we left Pinecone' landing spot.
Milvus
The heavy-scale distributed choice — billions of vectors, Apache-2.0.
Milvus is a cloud-native, distributed vector database under the LF AI & Data Foundation, licensed Apache-2.0 and developed by Zilliz. It is engineered for the top end of scale — billions of vectors, horizontal scaling, tiered storage — with a managed option (Zilliz Cloud) when you want the same engine without the Kubernetes homework. If your vector workload is genuinely huge, this is the open-source engine built for it.
Side by side
| Qdrant | Milvus | |
|---|---|---|
| Sovereignty Score | 92 | 88 |
| Open source | Yes | Yes |
| Self-hostable | Yes | Yes |
| Local-first | Yes | No |
| License | Apache-2.0 | Apache-2.0 |
| Pricing | Free self-hosted (no limits); Qdrant Cloud managed tiers with a free 1GB cluster | Free self-hosted (Apache-2.0); Zilliz Cloud managed tiers incl. a free tier |
Qdrant is Macrostack's recommended Pinecone alternative, so it's our pick here.
Qdrant
Strengths
- +Apache-2.0, no feature gates — the full engine is open
- +Single Rust binary: laptop to cluster with the same API
- +Excellent metadata filtering, quantization, hybrid search
- +Managed cloud exists when you want zero ops
Trade-offs
- −Self-hosting means you own scaling and backups
- −Smaller managed-service ecosystem than Pinecone's
Milvus
Strengths
- +Proven at billion-vector scale, horizontally scalable
- +LF AI & Data governance — not a single-vendor project
- +Managed escape hatch (Zilliz Cloud) with the same engine
Trade-offs
- −Distributed architecture = real operational complexity self-hosted
- −Overkill for small and mid-size workloads
Related alternative guides
Facts verified 2026-07-15. Licenses and pricing change — spotted something out of date? That's a correction we want.