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Milvus vs Qdrant

Milvus is built for billion-scale collections with a choice of index types; Qdrant is a leaner Rust engine focused on efficient filtered search.

Side by side

Milvus Qdrant
Vendor LF AI & Data Foundation (originally Zilliz) Qdrant Solutions GmbH
Pricing model Open source + paid options Free tier + paid plans
Free tier Yes Yes
Deployment Self-hosted Cloud, Self-hosted
Open source Yes (Apache-2.0) Yes (Apache-2.0)
Best for Teams needing self-hosted vector search at very large scale with full control over infrastructure. Teams needing efficient, filterable vector search with the option to self-host at no license cost.
Pricing

Free and open source under the Linux Foundation; a fully managed cloud version is sold separately by Zilliz as Zilliz Cloud.

Pricing has not been verified yet — see the vendor's site.

Self-hosted is free; Qdrant Cloud has a free-forever tier, usage-based Standard pricing, and minimum-spend Premium and Hybrid/Private Cloud options.

Free Free forever
Standard Usage-based (rates via calculator)
Premium Minimum spend required (rates via calculator)

Prices read from the vendor's own page on September 21, 2026. Vendors change prices; check the source before you budget.

Features
  • Billion-scale approximate nearest-neighbor search
  • Multiple ANN index types (HNSW, IVF, DiskANN)
  • Hybrid dense + sparse vector search
  • Storage-compute separation in cluster mode
  • Milvus Lite embedded mode for local development
  • Scalar filtering combined with vector search
  • Approximate nearest-neighbor search with payload filtering
  • Rust-based engine for high performance and low memory use
  • Vector quantization for reduced memory footprint
  • gRPC and REST APIs
  • Self-hosted or managed cloud, including hybrid/private cloud
  • Sharding and replication for horizontal scale

Verdict

Both are open-source, self-hostable vector databases with a managed cloud option, but they were built for different points on the scale curve. Milvus separates storage and compute so ingestion, indexing and query nodes scale independently, and it supports multiple ANN index types (HNSW, IVF, DiskANN) chosen per collection — the design point is billion-scale search with room to tune trade-offs between recall, latency and resource use. Qdrant is a Rust-based engine that emphasizes performance per resource and rich payload filtering alongside similarity search, aimed at teams that want efficient filtered search without the operational surface area of a system designed for the largest possible collections.

Milvus's managed cloud, Zilliz Cloud, runs the same engine, which is relevant if you expect to outgrow self-hosting later. Qdrant Cloud offers a genuinely free-forever tier for testing, plus usage-based and minimum-spend plans, including hybrid and private cloud for regulated environments.

Choose Milvus if

  • Your collection is, or will become, billions of vectors, and you want independent scaling of ingestion, indexing and query.
  • You want a choice of ANN index types tuned per collection rather than one default.
  • You are prepared to self-host and operate Kubernetes, or plan to move to Zilliz Cloud on the same engine.

Choose Qdrant if

  • Your workload leans on combining similarity search with exact payload filters — price, category, permissions — in the same query.
  • You want a lean, resource-efficient engine without the operational weight of billion-scale infrastructure.
  • You want a genuinely free managed tier to start on, with the option to self-host with no licence fee.

The honest caveat

Both scale further than most projects will ever need; picking the billion-scale engine for a collection that will hold a few million vectors mainly adds operational complexity, not benefit. Test both against your actual filter selectivity and query pattern rather than a published benchmark — filtered-query performance is where the two diverge most in practice. See vector database and embeddings for background.

Last reviewed September 22, 2026

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