head to head · open source
Qdrant vs Weaviate
Qdrant has 35,390 GitHub stars, 2,793 forks, 704 open issues and last shipped yesterday. Weaviate has 16,934 stars, 1,433 forks, 738 open issues and last shipped yesterday. Qdrant leads on adoption by 109% (35,390 vs 16,934 stars). Qdrant is written in Rust under Apache-2.0; Weaviate is written in Go under a custom or non-standard licence. Qdrant has attracted 8% as many forks as stars, Weaviate 8%. Weaviate was the more recently maintained of the two, and both are self-hostable with no licence fee. The two share 6 topic tags (hnsw, hybrid-search, image-search, mlops), so they are genuine substitutes rather than adjacent tools.
Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.
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Side by side
| Qdrant | Weaviate | |
|---|---|---|
| GitHub stars | ★ 35K | ★ 17K |
| License | Apache-2.0 | Custom / other |
| Written in | Rust | Go |
| Last push | 2026-11-02 | 2026-11-02 |
| Forks | ⑂ 2.8K | ⑂ 1.4K |
| Self-hosting | Yes | Yes |
| Data ownership | Your server | Your server |
pick Qdrant if
- You weight community size — 35K stars and counting
- You want the Apache-2.0 license terms
- Your stack matches Rust
- You value the larger contributor base for long-term maintenance
pick Weaviate if
- You want the Weaviate feature set and don't need the biggest community
- You prefer the Custom / other license terms
- Your stack matches Go
- You evaluated both and Weaviate fits your workflow better
About Qdrant
Vector Search Engine for the next generation of AI applications Qdrant (read: quadrant ) is a vector similarity search engine and vector database. It provides a production ready service with a convenient API to store, search, and manage points—vectors with an additional payload. Qdrant is tailored for extended filtering support, making it useful for all sorts of neural network or semantic based matching, faceted search, and other applications. Qdrant is written in Rust 🦀, which makes it fast and reliable even under high load. See benchmarks. With Qdrant, embeddings or neural network encoders can be turned into…
read the full Qdrant overview →
About Weaviate
Weaviate Weaviate is an open source, cloud native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification. Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre computed vector embeddings…
read the full Weaviate overview →
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Frequently asked questions
Is Qdrant or Weaviate more popular?
Qdrant has 35,390 GitHub stars and Weaviate has 16,934. Qdrant has the larger community by that measure.
Are Qdrant and Weaviate free?
Both are open source. Qdrant is licensed under Apache-2.0, and Weaviate has no licence declared in this registry. Both are free to self-host.
What is the difference between Qdrant and Weaviate?
Qdrant is written in Rust and Weaviate in Go. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.
Which should I choose, Qdrant or Weaviate?
Choose Qdrant if you want the larger community (35,390 stars) or its Apache-2.0 licence terms. Choose Weaviate if its feature set, stack or Custom / other licence fits better. Both are self-hostable.