Cloud-native, Apache-2.0 vector database for similarity search at scale, powering RAG, semantic and multimodal search, and recommendations. Its distributed architecture separates storage and compute and supports many index types (HNSW, IVF, FLAT, DiskANN, SCANN) with quantization and mmap. Created by Zilliz, which offers the managed Zilliz Cloud.
Vector DB · Zilliz
Milvus
Distributed open-source vector DB built for billion-scale.
Model support
Model-agnostic
Where it runs
- API
Tags
- #vector-db
- #open-source
- #rag
- #ann-search
- #scalable
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Managed vector database. The industry-default serverless option.
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AI insight: Fully managed with no self-host option — the trade-off for the serverless pricing it popularized in the vector-DB space.
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Embedded vector DB. Pip-install, prototype, scale later.
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AI insight: Runs embedded inside your Python process — the lowest-friction way to prototype RAG before you need a server at all.
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Vector similarity search inside Postgres. The pragmatic default.
Postgres extension that adds a vector type plus exact and approximate nearest-neighbour search. Pairs naturally with Supabase, Neon, and any managed Postgres. The lowest-friction RAG backend if you already run Postgres.
AI insight: Keeps embeddings in the same Postgres as your relational data, so you can JOIN against them and back everything up together.
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Open-source, Rust-based vector DB. Fast, predictable, self-hostable.
Vector database written in Rust with a strong focus on filtering, payloads, and predictable latency at scale. Self-host on a single binary or use the managed cloud.
AI insight: Written in Rust and ships as a single self-hostable binary — its payload filtering is why teams pick it for metadata-heavy search.
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AI insight: Stores indexes on object storage instead of RAM, so cost tracks usage not corpus size — Notion runs it in production.
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Open-source vector database with built-in vectorisers.
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