Vector Databases

4 open source tools compared. Sorted by stars — scroll down for our analysis.

Tool	Stars	Velocity	Language	License	Score
Milvus Cloud-native vector database for scalable ANN search	43.5k	—	Go	Apache License 2.0	79
Qdrant High-performance vector database and search engine	29.8k	+233/wk	Rust	Apache License 2.0	79
Chroma Data infrastructure for AI	26.8k	+130/wk	Rust	Apache License 2.0	79
Weaviate Open-source vector database	15.9k	+57/wk	Go	BSD 3-Clause "New" or "Revised" License	79

Our Analysis

Milvus43.5k★

Milvus is the vector database you reach for when your dataset outgrows everything else. Built for billions of vectors with a distributed architecture that separates storage, compute, and metadata — it scales horizontally where most alternatives hit a wall. Pinecone is the managed alternative — dead simple but locks you into their cloud and pricing. Weaviate blends vector search with structured data nicely. Qdrant is the lean, Rust-based option gaining fast momentum. For most indie projects, pgvector or Chroma will get you started without a new database. Use Milvus if you're building production RAG or search at serious scale and want to self-host. Benchmarks show sub-10ms p50 latency at high throughput. The catch: operational complexity is real. Running a Milvus cluster means managing etcd, MinIO, and Pulsar dependencies. For a solo founder prototyping an AI feature, this is overkill. Start with pgvector, graduate to Milvus when your vector count demands it.

Qdrant29.8k★

Qdrant is what you reach for when your AI app outgrows in-memory vector search. Written in Rust, it's fast, handles filtering alongside similarity search, and scales horizontally without breaking a sweat. The combination of speed and rich filtering makes it the best pure vector database right now. If you're building RAG, semantic search, or recommendation features, Qdrant should be on your shortlist. Pinecone is the managed alternative but locks you in and charges by usage. Milvus has more features but is heavier to operate. Chroma is simpler for prototyping but not built for production scale. Best for developers building AI products who need vector search that actually performs in production. The gRPC API is fast, the REST API is approachable, and the Python/JS SDKs are solid. The catch: self-hosting means you own the ops. The managed Qdrant Cloud exists but isn't cheap at scale. And if your dataset fits in memory, you might not need a dedicated vector DB — pgvector in Postgres could be enough.

Chroma26.8k★

Chroma is the vector database that just wants you to get started. Two lines of code, embeddings stored, similarity search working. No infrastructure, no config files, no PhD required. It's the SQLite of vector databases — embedded by default, scales up when you need it. For prototyping RAG apps, semantic search, or AI features, Chroma gets you from zero to working demo faster than anything else. Qdrant is more performant at scale and has richer filtering. Pinecone is fully managed but proprietary. pgvector keeps everything in Postgres but lacks Chroma's developer experience. Best for indie hackers building AI-powered features who need to move fast. The Python and JS SDKs are excellent. Integrations with LangChain, LlamaIndex, and every AI framework exist. The catch: Chroma's simplicity is also its ceiling. At serious scale (millions of vectors, high QPS), you'll outgrow it and need Qdrant or Milvus. The hosted offering (Chroma Cloud) is still early. And the recent Rust rewrite, while faster, means the internals are still stabilizing.

Weaviate15.9k★

Weaviate is the vector database that also wants to be your search engine. It combines vector similarity search with keyword (BM25) search in a single query — hybrid search that neither pure vector DBs nor traditional search engines do well. Built in Go, production-ready, and backed by a well-funded company. If you're building search that needs to understand meaning AND match keywords, Weaviate's hybrid approach is compelling. Qdrant is faster for pure vector search. Elasticsearch can do both but wasn't designed for vectors. Pinecone is managed but vector-only. Milvus has similar capabilities but is harder to operate. Best for developers building AI-powered search that needs both semantic understanding and traditional keyword matching. The multi-modal support (text, images) is a bonus. The catch: Weaviate is heavier to run than Qdrant or Chroma — it wants real resources. The GraphQL-only API was controversial (REST was added later). The managed cloud pricing gets expensive at scale. And the BSD-3 license is permissive, but some enterprise modules are paid.